Marketing pages show base prices. Invoices tell a different story, especially on managed platforms where the bill grows through usage-based add-ons rather than the sticker price.

Self-hosting has its own cost story, and it's usually told as "you save money but pay in time." That framing deserves a closer look with actual, published numbers rather than rough estimates.

For a single web app with a database and light traffic, a 2 vCPU / 4 GB RAM instance is the standard starting point. Published pricing from the two most commonly compared VPS providers looks like this as of mid-2026:

Hetzner CX22 (2 vCPU, 4 GB RAM, 40 GB NVMe SSD, 20 TB of included traffic): €7.99 a month, roughly $9.49, following Hetzner's April 2026 price adjustment. The 20 TB traffic allowance is bundled in at no extra charge.

DigitalOcean Basic Droplet (2 vCPU, 4 GB RAM): $24 a month, with a smaller included transfer allowance and overage billed per gigabyte beyond that.

Both figures come from the providers' own published pricing, cross-checked against independent benchmarking comparisons. The gap between them isn't a rounding error. It reflects a genuine difference in positioning: Hetzner competes almost entirely on raw compute-per-dollar, while DigitalOcean's higher price includes a broader ecosystem (managed databases from $15/month, App Platform, integrated CDN, and more extensive documentation for teams that want to self-manage less).

What a dedicated, production-grade instance costs

Once traffic grows enough that shared-CPU noisy-neighbor effects start to matter, teams typically move to dedicated-core instances. DigitalOcean's own pricing page lists CPU-Optimized Droplets starting at $42/month for 4 GB of memory, scaling up to $168/month for an 8 vCPU / 16 GB configuration. These are dedicated-core instances built for sustained CPU workloads rather than the shared-core Basic tier.

Hetzner's dedicated-vCPU line (CCX) targets the same use case at meaningfully lower list prices, though the two providers structure their tiers differently enough that a precise like-for-like comparison at this spec level needs to be checked against current pricing pages rather than quoted as a fixed figure here.

Where managed PaaS costs actually come from

Modern PaaS platforms rarely charge a flat fee for real workloads. The base subscription (commonly around $20/month per seat on platforms like Vercel's Pro tier) covers a baseline allowance. Beyond that, cost accumulates through several separate meters:

Function execution time, billed once you exceed the included compute-hours

Bandwidth, billed per gigabyte beyond an included allowance

Build minutes, billed once CI/CD usage exceeds the free tier

Because each of these is metered independently, and because platforms with multiple services (an API, a background worker, an admin panel) often meter each service separately, the total can grow substantially beyond the advertised base price. The exact dollar impact depends heavily on actual traffic and usage patterns, which is why platform pricing pages show ranges and calculators rather than single numbers, and why it's worth running your own workload through a provider's current calculator before comparing costs.

The usual counterargument is that self-hosting costs engineering hours that managed platforms save. That's true, but it isn't the full comparison. The realistic tradeoff isn't "self-hosting time versus zero time." It's "self-hosting time versus PaaS-debugging time": working around usage-based billing surprises, tuning cold-start latency, hitting platform-specific limitations, or migrating everything when a vendor changes its pricing tiers, as Hetzner itself did in April 2026 with advance notice, and as most providers do periodically without much notice at all.

Setting up a bare VPS still requires configuring a reverse proxy, provisioning SSL, and wiring up container networking by hand, work that a deployment platform like try.direct automates. That's the layer where self-hosting's time cost concentrates, and it's largely a one-time setup cost rather than an ongoing one.

Neither model is free of costs that don't show up on the pricing page:

Per-service metering that doesn't consolidate, so several small services can cost more together than one larger one on the same infrastructure

Vendor-specific limitations (cold start behavior, execution time caps) that still require engineering workarounds despite the platform being "managed"

Migration risk when a vendor changes pricing tiers; moving a production workload afterward takes real effort

Initial setup time for reverse proxy, SSL, and container networking, mostly a one-time cost rather than a recurring one

Backup and monitoring discipline: a self-hosted stack is only as reliable as the backup schedule someone actually maintains

No default uptime SLA on budget VPS providers (Hetzner, for instance, does not publish a formal SLA or credit-backed compensation for cloud downtime), which matters more for workloads where uptime carries real business or compliance weight

Not every workload belongs on infrastructure you manage. A good self-hosting candidate has predictable usage, a clear owner, and a straightforward backup and recovery plan the team actually understands. Compliance-heavy systems, or anything where the operational risk of a mistake is high and the team lacks the experience to catch it early, are usually better left on a managed platform, at least until that experience exists in-house.

The right question isn't "can we self-host this." It's "should we own the operation of this".

Is self-hosting actually cheaper than managed cloud hosting? For a comparable spec, published VPS pricing (Hetzner, DigitalOcean) is consistently lower than managed PaaS once usage-based overages are factored in. The gap narrows for highly variable or spiky traffic, where managed autoscaling and included support can offset some of the difference.

Why is Hetzner so much cheaper than DigitalOcean for the same specs? The two providers are positioned differently. Hetzner competes primarily on raw compute price and bundles a large traffic allowance; DigitalOcean's higher price includes a broader managed-service ecosystem (managed databases, App Platform, CDN) and more extensive support documentation.

What's the biggest hidden cost of managed PaaS platforms? Usage-based billing across separate meters, execution time, bandwidth, build minutes, and per-service charges that aren't obvious from the base subscription price.

What's the biggest hidden cost of self-hosting? Operational setup time for tasks that deployment tooling can automate: reverse proxy configuration, SSL certificate renewal, container networking, and ongoing backup scheduling.

Manage your self-hosted Docker Compose stacks the easy way ➞ deploy Dockge on try.direct

Marketing pages show base prices. Invoices tell a different story, especially on managed platforms where the bill grows through usage-based add-ons rather than the sticker price.

Self-hosting has its own cost story, and it's usually told as "you save money but pay in time." That framing deserves a closer look with actual, published numbers rather than rough estimates.

What a small VPS actually costs today

For a single web app with a database and light traffic, a 2 vCPU / 4 GB RAM instance is the standard starting point. Published pricing from the two most commonly compared VPS providers looks like this as of mid-2026:
Hetzner CX22 (2 vCPU, 4 GB RAM, 40 GB NVMe SSD, 20 TB of included traffic): €7.99 a month, roughly $9.49, following Hetzner's April 2026 price adjustment. The 20 TB traffic allowance is bundled in at no extra charge.
DigitalOcean Basic Droplet (2 vCPU, 4 GB RAM): $24 a month, with a smaller included transfer allowance and overage billed per gigabyte beyond that.

Both figures come from the providers' own published pricing, cross-checked against independent benchmarking comparisons. The gap between them isn't a rounding error. It reflects a genuine difference in positioning: Hetzner competes almost entirely on raw compute-per-dollar, while DigitalOcean's higher price includes a broader ecosystem (managed databases from $15/month, App Platform, integrated CDN, and more extensive documentation for teams that want to self-manage less).

What a dedicated, production-grade instance costs

Once traffic grows enough that shared-CPU noisy-neighbor effects start to matter, teams typically move to dedicated-core instances. DigitalOcean's own pricing page lists CPU-Optimized Droplets starting at $42/month for 4 GB of memory, scaling up to $168/month for an 8 vCPU / 16 GB configuration. These are dedicated-core instances built for sustained CPU workloads rather than the shared-core Basic tier.

Hetzner's dedicated-vCPU line (CCX) targets the same use case at meaningfully lower list prices, though the two providers structure their tiers differently enough that a precise like-for-like comparison at this spec level needs to be checked against current pricing pages rather than quoted as a fixed figure here.

Where managed PaaS costs actually come from

Modern PaaS platforms rarely charge a flat fee for real workloads. The base subscription (commonly around $20/month per seat on platforms like Vercel's Pro tier) covers a baseline allowance. Beyond that, cost accumulates through several separate meters:
Function execution time, billed once you exceed the included compute-hours
Bandwidth, billed per gigabyte beyond an included allowance
Build minutes, billed once CI/CD usage exceeds the free tier

Because each of these is metered independently, and because platforms with multiple services (an API, a background worker, an admin panel) often meter each service separately, the total can grow substantially beyond the advertised base price. The exact dollar impact depends heavily on actual traffic and usage patterns, which is why platform pricing pages show ranges and calculators rather than single numbers, and why it's worth running your own workload through a provider's current calculator before comparing costs.

The part the "time cost" argument misses

The usual counterargument is that self-hosting costs engineering hours that managed platforms save. That's true, but it isn't the full comparison. The realistic tradeoff isn't "self-hosting time versus zero time." It's "self-hosting time versus PaaS-debugging time": working around usage-based billing surprises, tuning cold-start latency, hitting platform-specific limitations, or migrating everything when a vendor changes its pricing tiers, as Hetzner itself did in April 2026 with advance notice, and as most providers do periodically without much notice at all.

Setting up a bare VPS still requires configuring a reverse proxy, provisioning SSL, and wiring up container networking by hand, work that a deployment platform like try.direct automates. That's the layer where self-hosting's time cost concentrates, and it's largely a one-time setup cost rather than an ongoing one.

Hidden costs on both sides

Neither model is free of costs that don't show up on the pricing page:

Hidden costs of managed PaaS:
Per-service metering that doesn't consolidate, so several small services can cost more together than one larger one on the same infrastructure
Vendor-specific limitations (cold start behavior, execution time caps) that still require engineering workarounds despite the platform being "managed"
Migration risk when a vendor changes pricing tiers; moving a production workload afterward takes real effort

Hidden costs of self-hosting:
Initial setup time for reverse proxy, SSL, and container networking, mostly a one-time cost rather than a recurring one
Backup and monitoring discipline: a self-hosted stack is only as reliable as the backup schedule someone actually maintains
No default uptime SLA on budget VPS providers (Hetzner, for instance, does not publish a formal SLA or credit-backed compensation for cloud downtime), which matters more for workloads where uptime carries real business or compliance weight

When self-hosting doesn't make sense

Not every workload belongs on infrastructure you manage. A good self-hosting candidate has predictable usage, a clear owner, and a straightforward backup and recovery plan the team actually understands. Compliance-heavy systems, or anything where the operational risk of a mistake is high and the team lacks the experience to catch it early, are usually better left on a managed platform, at least until that experience exists in-house.

The right question isn't "can we self-host this." It's "should we own the operation of this".

FAQ

Is self-hosting actually cheaper than managed cloud hosting? For a comparable spec, published VPS pricing (Hetzner, DigitalOcean) is consistently lower than managed PaaS once usage-based overages are factored in. The gap narrows for highly variable or spiky traffic, where managed autoscaling and included support can offset some of the difference.

Why is Hetzner so much cheaper than DigitalOcean for the same specs? The two providers are positioned differently. Hetzner competes primarily on raw compute price and bundles a large traffic allowance; DigitalOcean's higher price includes a broader managed-service ecosystem (managed databases, App Platform, CDN) and more extensive support documentation.

What's the biggest hidden cost of managed PaaS platforms? Usage-based billing across separate meters, execution time, bandwidth, build minutes, and per-service charges that aren't obvious from the base subscription price.

What's the biggest hidden cost of self-hosting? Operational setup time for tasks that deployment tooling can automate: reverse proxy configuration, SSL certificate renewal, container networking, and ongoing backup scheduling.

Manage your self-hosted Docker Compose stacks the easy way ➞ deploy Dockge on try.direct

Managed PaaS base prices hide the real bill. A numbers-first look at self-hosting costs versus managed platforms, using published VPS pricing.

The Real Cost of Self-Hosting in 2026

For a decade, the deal was simple: ship every log, metric, and trace to a vendor, pay per gigabyte, and get a dashboard in return. That deal worked when most systems were monoliths producing a predictable, modest stream of data.

It doesn't work anymore, and the reason isn't pricing. It's volume.

The workload changed, the pricing model didn't

Kubernetes clusters, ephemeral pods, serverless functions, and agentic AI workflows generate telemetry at a scale monoliths never approached. Every pod restart is a log entry. Every microservice hop is a trace. Every LLM call adds token counts, latency, and outcome data that can't be sampled down without losing the signal entirely. Once you sample an AI request, you no longer know what the model actually did.

Observability vendors responded the way SaaS vendors usually do: not by rearchitecting the platform, but by adding more dashboards, more SKUs, and higher tiers. The bill grows faster than the insight.

Bring Your Own Cloud (BYOC) describes an architecture where the data plane, meaning the actual compute that stores and processes your logs, metrics, and traces, runs inside your own cloud account or VPC, while the vendor manages the control plane: upgrades, configuration, orchestration. Your data doesn't leave your environment; the vendor's software runs on top of infrastructure you own.

Worth noting: some BYOC vendors argue the term gets applied loosely, to private connectivity options or dedicated instances that still route data through a shared, vendor-owned pipeline behind the scenes. That's a fair distinction to watch for, though it's also a claim made by vendors competing on exactly this point, so it's worth verifying against a specific platform's architecture documentation rather than taking any single vendor's framing at face value.

Zero trust becomes structural, not aspirational. Data never leaves your environment, so a whole category of compliance questions disappears before it's asked.

Costs track actual usage. You pay for the compute and storage you run, not for an ingestion quota set by someone else's pricing page.

You own the relationship. When the platform runs inside your infrastructure, you're not a tenant negotiating for your own data back. You're the owner deciding what stays and what gets deleted.

AI observability lives where the AI does. As inference and agent workloads move into private VPCs for latency and compliance reasons, the monitoring for those workloads needs to move there too.

The same pattern is playing out across adjacent categories: managed databases deployed inside customer VPCs, AI sandboxes that keep code execution inside the customer's own cloud account, platform-as-a-service tools that deploy CI/CD and GPU workloads directly into AWS, GCP, or bare metal instead of a shared vendor cluster. Observability is simply the latest domain to make the same move, for the same reason: as infrastructure gets more distributed, the tools that watch it need to be distributed too.

You don't need an enterprise BYOC contract to get the benefits. A self-hosted observability stack, Prometheus for metrics, Grafana for dashboards, Loki for logs, running as a Docker Compose stack on infrastructure you control, gives you the same core properties: your telemetry never leaves your environment, your cost scales with your actual usage, and you're not waiting on a vendor's roadmap to support a metric you care about.

The tradeoff historically was operational burden. Someone has to write the compose file, wire up the networks and volumes, manage the reverse proxy, keep the images patched. That's exactly the layer try.direct removes.

You don't have to start with the full metrics-and-logs stack, either. If your team is still relying on a SaaS status page or an external uptime checker, a self-hosted uptime monitor like Uptime Kuma is a reasonable first move: it tells you whether your services are actually up, without sending that data to a third party. It's not a replacement for Prometheus or Grafana, and it won't give you metrics or log correlation, but it's a real, working piece of self-hosted observability you can have running in minutes, before deciding how much further to take the migration.

How the pieces map to what you're replacing

It helps to think in terms of what each self-hosted component replaces, rather than as a generic "monitoring stack":

Prometheus replaces the metrics-ingestion layer of tools like Datadog or New Relic. It scrapes and stores time-series data (CPU, memory, request rates, custom application metrics) at whatever retention you configure, not whatever retention your pricing tier allows.

Grafana replaces the dashboard layer. It reads from Prometheus (and from Loki, and from most common databases) and gives you the same kind of visual drill-down, without a per-seat license.

Loki replaces centralized log aggregation. It's built to index metadata rather than full text, which keeps storage costs down even at high log volume, a meaningful difference once Kubernetes or agentic workloads start generating thousands of log lines a minute.

Alertmanager (bundled with Prometheus) replaces the alerting and on-call routing layer, feeding into whatever notification channel your team already uses.

None of this requires Kubernetes. A single Docker Compose file describing these four services, with the right volumes and a shared network, is enough for most small and mid-sized teams.

Moving off a SaaS observability platform doesn't have to happen in one step:

Start with metrics. Point Prometheus at your existing services alongside your current SaaS tool, running both in parallel for two to four weeks. This gives you a baseline without any risk of losing visibility.

Move dashboards next. Rebuild your most-used SaaS dashboards in Grafana once the underlying metrics are flowing. This is usually the fastest part; most teams only actively use a handful of dashboards.

Migrate logs last. Log pipelines tend to have the most downstream dependencies (existing search queries, saved filters, alert rules), so give this stage the most buffer time.

Cut over alerting once confidence is established. Only redirect on-call alerting to the self-hosted stack after it's been running in parallel long enough to trust it under real incident conditions.

The point of running both in parallel isn't caution for its own sake. It's that observability is the one system you don't want to be debugging blind during a migration.

What is BYOC in simple terms? BYOC (Bring Your Own Cloud) means the vendor's software runs inside your own cloud account or VPC instead of theirs. Your data stays in your environment; the vendor manages the software layer on top of it.

Is BYOC the same as self-hosting? Not quite. Self-hosting means you deploy and operate everything yourself. BYOC means a vendor manages the platform, but the compute and data live in your infrastructure. Both keep data under your control; they differ in who carries the operational load.

Why is AI workload observability different from traditional monitoring? AI workloads produce telemetry, like token usage, latency, and output quality, that represents the actual outcome of the system, not a proxy for it. Sampling this data the way you might sample a web server's access logs means losing information you can't recover.

Do I need Kubernetes to benefit from self-hosted observability? No. Docker Compose stacks benefit just as much. The core issue, vendor lock-in on your own telemetry, applies regardless of orchestration layer.

Start with a real first step ➞ deploy Uptime Kuma in one click on try.direct

For a decade, the deal was simple: ship every log, metric, and trace to a vendor, pay per gigabyte, and get a dashboard in return. That deal worked when most systems were monoliths producing a predictable, modest stream of data.

It doesn't work anymore, and the reason isn't pricing. It's volume.

The workload changed, the pricing model didn't

Kubernetes clusters, ephemeral pods, serverless functions, and agentic AI workflows generate telemetry at a scale monoliths never approached. Every pod restart is a log entry. Every microservice hop is a trace. Every LLM call adds token counts, latency, and outcome data that can't be sampled down without losing the signal entirely. Once you sample an AI request, you no longer know what the model actually did.

Observability vendors responded the way SaaS vendors usually do: not by rearchitecting the platform, but by adding more dashboards, more SKUs, and higher tiers. The bill grows faster than the insight.

What BYOC actually means

Bring Your Own Cloud (BYOC) describes an architecture where the data plane, meaning the actual compute that stores and processes your logs, metrics, and traces, runs inside your own cloud account or VPC, while the vendor manages the control plane: upgrades, configuration, orchestration. Your data doesn't leave your environment; the vendor's software runs on top of infrastructure you own.

Worth noting: some BYOC vendors argue the term gets applied loosely, to private connectivity options or dedicated instances that still route data through a shared, vendor-owned pipeline behind the scenes. That's a fair distinction to watch for, though it's also a claim made by vendors competing on exactly this point, so it's worth verifying against a specific platform's architecture documentation rather than taking any single vendor's framing at face value.

Under this model:
Zero trust becomes structural, not aspirational. Data never leaves your environment, so a whole category of compliance questions disappears before it's asked.
Costs track actual usage. You pay for the compute and storage you run, not for an ingestion quota set by someone else's pricing page.
You own the relationship. When the platform runs inside your infrastructure, you're not a tenant negotiating for your own data back. You're the owner deciding what stays and what gets deleted.
AI observability lives where the AI does. As inference and agent workloads move into private VPCs for latency and compliance reasons, the monitoring for those workloads needs to move there too.

This isn't a niche trend anymore

The same pattern is playing out across adjacent categories: managed databases deployed inside customer VPCs, AI sandboxes that keep code execution inside the customer's own cloud account, platform-as-a-service tools that deploy CI/CD and GPU workloads directly into AWS, GCP, or bare metal instead of a shared vendor cluster. Observability is simply the latest domain to make the same move, for the same reason: as infrastructure gets more distributed, the tools that watch it need to be distributed too.

What this looks like in practice

You don't need an enterprise BYOC contract to get the benefits. A self-hosted observability stack, Prometheus for metrics, Grafana for dashboards, Loki for logs, running as a Docker Compose stack on infrastructure you control, gives you the same core properties: your telemetry never leaves your environment, your cost scales with your actual usage, and you're not waiting on a vendor's roadmap to support a metric you care about.

The tradeoff historically was operational burden. Someone has to write the compose file, wire up the networks and volumes, manage the reverse proxy, keep the images patched. That's exactly the layer try.direct removes.

You don't have to start with the full metrics-and-logs stack, either. If your team is still relying on a SaaS status page or an external uptime checker, a self-hosted uptime monitor like Uptime Kuma is a reasonable first move: it tells you whether your services are actually up, without sending that data to a third party. It's not a replacement for Prometheus or Grafana, and it won't give you metrics or log correlation, but it's a real, working piece of self-hosted observability you can have running in minutes, before deciding how much further to take the migration.

How the pieces map to what you're replacing

It helps to think in terms of what each self-hosted component replaces, rather than as a generic "monitoring stack":
Prometheus replaces the metrics-ingestion layer of tools like Datadog or New Relic. It scrapes and stores time-series data (CPU, memory, request rates, custom application metrics) at whatever retention you configure, not whatever retention your pricing tier allows.
Grafana replaces the dashboard layer. It reads from Prometheus (and from Loki, and from most common databases) and gives you the same kind of visual drill-down, without a per-seat license.
Loki replaces centralized log aggregation. It's built to index metadata rather than full text, which keeps storage costs down even at high log volume, a meaningful difference once Kubernetes or agentic workloads start generating thousands of log lines a minute.
Alertmanager (bundled with Prometheus) replaces the alerting and on-call routing layer, feeding into whatever notification channel your team already uses.

None of this requires Kubernetes. A single Docker Compose file describing these four services, with the right volumes and a shared network, is enough for most small and mid-sized teams.

A practical migration path

Moving off a SaaS observability platform doesn't have to happen in one step:
Start with metrics. Point Prometheus at your existing services alongside your current SaaS tool, running both in parallel for two to four weeks. This gives you a baseline without any risk of losing visibility.
Move dashboards next. Rebuild your most-used SaaS dashboards in Grafana once the underlying metrics are flowing. This is usually the fastest part; most teams only actively use a handful of dashboards.
Migrate logs last. Log pipelines tend to have the most downstream dependencies (existing search queries, saved filters, alert rules), so give this stage the most buffer time.
Cut over alerting once confidence is established. Only redirect on-call alerting to the self-hosted stack after it's been running in parallel long enough to trust it under real incident conditions.

The point of running both in parallel isn't caution for its own sake. It's that observability is the one system you don't want to be debugging blind during a migration.

FAQ

What is BYOC in simple terms? BYOC (Bring Your Own Cloud) means the vendor's software runs inside your own cloud account or VPC instead of theirs. Your data stays in your environment; the vendor manages the software layer on top of it.

Is BYOC the same as self-hosting? Not quite. Self-hosting means you deploy and operate everything yourself. BYOC means a vendor manages the platform, but the compute and data live in your infrastructure. Both keep data under your control; they differ in who carries the operational load.

Why is AI workload observability different from traditional monitoring? AI workloads produce telemetry, like token usage, latency, and output quality, that represents the actual outcome of the system, not a proxy for it. Sampling this data the way you might sample a web server's access logs means losing information you can't recover.

Do I need Kubernetes to benefit from self-hosted observability? No. Docker Compose stacks benefit just as much. The core issue, vendor lock-in on your own telemetry, applies regardless of orchestration layer.

Start with a real first step ➞ deploy Uptime Kuma in one click on try.direct

SaaS monitoring pricing was built for monoliths. Kubernetes and AI workloads broke it. Here is why teams are self-hosting observability instead.

Why SaaS Observability Is Hitting a Wall

What You'll Learn A workflow for testing any GitHub project in 2 minutes - no Docker Compose, no manual setup, no debugging. Just clone and run. Deploy any project with 2 commands Automatic project detection and Docker config Healthchecks and .env templates included Works with Python, Node, Go, Rust, PHP projects The Problem You find an interesting GitHub project. You want to test it. Here's what usually happens: Clone the repo Read the README for 20 minutes Realize the Docker setup is incomplete or outdated Write a Dockerfile Write docker-compose.yml Add healthchecks manually Set up .env with the right variables Debug for an hour Total time: 1-3 hours. Often you give up. The Solution stacker init --from-github owner/repo --with-ai --force stacker deploy --target local That's it. Stacker handles everything else. How It Works 1. Stacker clones the repo stacker init --from-github https://github.com/plausible/analytics --force 2. It detects the project type Stacker reads the repo and figures out: Language (Python, Node, Go, Rust, PHP) Framework (Flask, Express, Gin, Actix) Services (PostgreSQL, Redis, Elasticsearch, etc.) Port mappings from docker-compose.yml or source 3. It generates the Docker config # What gets created: stacker.yml # Deployment config .stacker/Dockerfile # Optimized for the project .stacker/docker-compose.yml # Production-ready .env.example # All env vars with placeholders scripts/generate-secrets.sh # Auto-generates passwords 4. You deploy cp .env.example .env ./scripts/generate-secrets.sh stacker deploy --target local Real Examples Test a password manager mkdir ~/vaultwarden && cd ~/vaultwarden stacker init --from-github dani-garcia/vaultwarden --with-ai --force cp .env.example .env && ./scripts/generate-secrets.sh stacker deploy --target local # Running at http://localhost:8080 Test a wiki mkdir ~/wiki && cd ~/wiki stacker init --from-github Requarks/wiki --with-ai --force cp .env.example .env && ./scripts/generate-secrets.sh stacker deploy --target local # Running at http://localhost:3000 Test a code editor mkdir ~/editor && cd ~/editor stacker init --from-github coder/code-server --with-ai --force cp .env.example .env && ./scripts/generate-secrets.sh stacker deploy --target local # Running at http://localhost:8080 Test a file manager mkdir ~/files && cd ~/files stacker init --from-github filebrowser/filebrowser --with-ai --force cp .env.example .env && ./scripts/generate-secrets.sh stacker deploy --target local # Running at http://localhost:8080 What If Detection Fails? If Stacker can't auto-detect the project, it falls back to asking you or using the project's existing Dockerfile. You can also use AI to help: stacker init --from-github owner/repo --with-ai --force # AI reads the README and source code to figure out the stack Customizing After Deployment Edit stacker.yml to change ports, add services, or configure networking: # Change the port app: ports: - "8081:8080" # Add a database services: - name: postgres image: postgres:16-alpine environment: POSTGRES_DB: myapp POSTGRES_PASSWORD: "${DB_PASSWORD}" volumes: - pg_data:/var/lib/postgresql/data # Add a reverse proxy proxy: type: nginx auto_detect: true domains: - domain: myapp.local ssl: off upstream: app:8080 Cleaning Up # Stop everything stacker destroy --target local # Remove all generated files rm -rf .stacker stacker.yml .env .env.example scripts/ The Full Workflow # 1. Find a project # 2. Test it in 2 minutes mkdir ~/project && cd ~/project stacker init --from-github owner/repo --with-ai --force cp .env.example .env && ./scripts/generate-secrets.sh stacker deploy --target local # 3. Like it? Deploy to a server stacker config setup server stacker deploy --target server # 4. Done. Two commands from GitHub to production. Key Takeaways Any GitHub project can be tested in 2 minutes Stacker handles detection, Docker config, healthchecks, and secrets Works with Python, Node, Go, Rust, PHP, and more The same config works on local, server, or cloud Source: github.com/trydirect/stacker

Test any GitHub project in 2 minutes - no Docker Compose, no manual setup. Just clone and run.

The Fastest Way to Test Any GitHub Project Locally

What You'll Build Five real self-hosted apps running on your machine - each deployed with two commands. Total time: under 10 minutes. Plausible - web analytics (Google Analytics alternative) Vaultwarden - password manager (Bitwarden compatible) Outline - team wiki and note-taking Code Server - VS Code in your browser Filebrowser - web-based file manager Quick Answer # For any of these apps: mkdir ~/app-name && cd ~/app-name stacker init --from-github owner/repo --with-ai --force cp .env.example .env && ./scripts/generate-secrets.sh stacker deploy --target local Same pattern. Different repo. 2 minutes each. Prerequisites Docker ( docker --version ) Stacker ( stacker --version ) curl -fsSL https://raw.githubusercontent.com/trydirect/stacker/main/install.sh | bash 1. Plausible Analytics (2 min) Privacy-first web analytics. No cookies, no personal data collection. mkdir ~/plausible && cd ~/plausible stacker init --from-github plausible/analytics --with-ai --force cp .env.example .env ./scripts/generate-secrets.sh # Edit .env - set your domain: # PLAUSIBLE_BASE_URL=http://localhost:8000 stacker deploy --target local # Dashboard: http://localhost:8000 Stacker handles: ClickHouse database, PostgreSQL, the Plausible API, and the tracker script endpoint. 2. Vaultwarden (2 min) Lightweight Bitwarden-compatible password manager. Self-host, sync across devices. mkdir ~/vaultwarden && cd ~/vaultwarden stacker init --from-github dani-garcia/vaultwarden --with-ai --force cp .env.example .env ./scripts/generate-secrets.sh stacker deploy --target local # Web UI: http://localhost:8080 Create an admin account, then use the Bitwarden mobile app with your self-hosted URL. 3. Outline Wiki (3 min) Fast, collaborative team wiki. Notion alternative, open source. mkdir ~/outline && cd ~/outline stacker init --from-github outline/outline --with-ai --force cp .env.example .env ./scripts/generate-secrets.sh # Outline needs an OIDC provider - edit .env: # OIDC_CLIENT_ID=your-id # OIDC_CLIENT_SECRET=your-secret # OIDC_AUTH_URI=http://localhost:3000 stacker deploy --target local # Wiki: http://localhost:3000 Outline requires an authentication provider (Google, Slack, or OIDC). The .env.example lists all required variables. 4. Code Server (1 min) VS Code running in your browser. Access your dev environment from anywhere. mkdir ~/code-server && cd ~/code-server stacker init --from-github coder/code-server --with-ai --force cp .env.example .env ./scripts/generate-secrets.sh # Set your password in .env: # PASSWORD=your-secure-password stacker deploy --target local # IDE: http://localhost:8080 5. Filebrowser (1 min) Web-based file manager. Browse, edit, and share files from your browser. mkdir ~/filebrowser && cd ~/filebrowser stacker init --from-github filebrowser/filebrowser --with-ai --force cp .env.example .env ./scripts/generate-secrets.sh stacker deploy --target local # File manager: http://localhost:8080 Running Everything Together All five apps can run simultaneously on different ports: stacker status # plausible running :8000 # vaultwarden running :8080 # outline running :3000 # code-server running :8443 # filebrowser running :8500 Customizing Ports If a port conflicts, edit stacker.yml : # Example: move Vaultwarden to port 8081 app: ports: - "8081:8080" Deploying to a Server Once everything works locally, push to a server: stacker config setup server stacker deploy --target server Or deploy to cloud: stacker config setup cloud stacker deploy --target cloud What Each App Needs App Services Port Setup Notes Plausible ClickHouse, PostgreSQL 8000 Set PLAUSIBLE_BASE_URL in .env Vaultwarden None (single container) 8080 Just set PASSWORD Outline PostgreSQL, Redis, S3 3000 Needs OIDC provider Code Server None (single container) 8080 Set PASSWORD Filebrowser None (single container) 8080 Zero config Key Takeaways Same pattern for every app: init, configure, deploy Stacker handles Docker complexity automatically Healthchecks, port isolation, and secret generation are built in Works identically on local, server, or cloud Source: github.com/trydirect/stacker

Deploy 5 useful self-hosted apps in 10 minutes with Stacker - analytics, password manager, note-taking, code editor, and file sharing.

Deploy 5 Useful Self-Hosted Apps in 10 Minutes

What You'll Build A self-hosted Umami analytics dashboard with PostgreSQL - running on your machine in under 5 minutes. No Google Analytics, no data leaves your server. Umami dashboard (Node.js) PostgreSQL database with healthchecks Persistent volumes for data .env template with auto-generated secrets Quick Answer mkdir ~/umami && cd ~/umami stacker init --from-github umami-software/umami --with-ai --force cp .env.example .env && ./scripts/generate-secrets.sh stacker deploy --target local Dashboard at http://localhost:3000 . Default login: admin / umami. Prerequisites Docker ( docker --version ) Git ( git --version ) Stacker ( stacker --version ) Step 1: Install Stacker curl -fsSL https://raw.githubusercontent.com/trydirect/stacker/main/install.sh | bash stacker --version # stacker 0.3.0 Step 2: Generate Configuration mkdir ~/umami && cd ~/umami stacker init --from-github umami-software/umami --with-ai --force Stacker clones Umami, reads the docker-compose.yml, and generates: stacker.yml # Deployment config .stacker/Dockerfile # Optimized Node.js build .stacker/docker-compose.yml # Production compose file .env.example # All env vars with placeholders scripts/generate-secrets.sh # Fills in passwords Step 3: Configure Secrets cp .env.example .env ./scripts/generate-secrets.sh # Generates: DB_PASSWORD, DATABASE_URL, HASH_SALT Optional - edit .env to set your timezone or tracking domain: TZ=UTC TRACKER_SCRIPT_NAME=tracker.js Step 4: Deploy stacker config validate # ✓ Configuration is valid stacker deploy --target local # Building app... # Creating postgres... # ✓ Stack deployed successfully Step 5: Verify and Use stacker status # umami running # postgres running open http://localhost:3000 Login with admin / umami . Change the password immediately. Understanding the Generated Config The stacker.yml that Stacker generated: name: umami app: type: node ports: - "3000:3000" environment: DATABASE_URL: "postgresql://umami:${DB_PASSWORD}@postgres:5432/umami" NEXT_AUTH_URL: "http://localhost:3000" NEXT_AUTH_SECRET: "${HASH_SALT}" services: - name: postgres image: postgres:16-alpine ports: - "127.0.0.1:5432:5432" environment: POSTGRES_DB: umami POSTGRES_USER: umami POSTGRES_PASSWORD: "${DB_PASSWORD}" volumes: - postgres_data:/var/lib/postgresql/data healthcheck: test: "CMD pg_isready -U umami" interval: 5s timeout: 2s retries: 10 volumes: postgres_data: {} What Stacker got right automatically: Node.js detected from package.json PostgreSQL service extracted from compose file Healthcheck - pg_isready added for database Port isolation - PostgreSQL on 127.0.0.1 only Named volume - data persists across restarts Secrets - no hardcoded passwords Customizing Add a reverse proxy with SSL # Add to stacker.yml: proxy: type: nginx auto_detect: true domains: - domain: analytics.yourdomain.com ssl: auto upstream: app:3000 stacker deploy --target local Deploy to a remote server stacker config setup server # Enter: host, user, SSH key stacker deploy --target server Deploy to cloud stacker config setup cloud # Choose: hetzner, digitalocean, aws, linode, vultr stacker deploy --target cloud Adding Your Website to Umami Once Umami is running: Open http://localhost:3000 Login → Settings → Websites → Add Website Enter your domain and name Copy the tracking script into your HTML: Troubleshooting Port 3000 already in use # Edit stacker.yml: app: ports: - "3001:3000" stacker deploy --target local Database connection refused # Check PostgreSQL is healthy: stacker status # Check logs: stacker logs postgres Want to reset everything stacker destroy --target local rm -rf .stacker stacker.yml .env .env.example Key Takeaways Umami deploys in 5 minutes with 4 commands Stacker handles the entire Docker stack automatically Healthchecks, port isolation, and secret generation are built in The same config works on local, server, or cloud Source: github.com/trydirect/stacker

Deploy Umami analytics in 5 minutes with Stacker - self-hosted, privacy-first alternative to Google Analytics.

Deploy Self-Hosted Analytics in 5 Minutes - Umami + Stacker

What You'll Build In this tutorial, you'll deploy three real open-source projects using Stacker - no Docker expertise needed. By the end, you'll have: A self-hosted analytics dashboard (Umami) A privacy-focused web archiver (ArchiveBox) An AWS-compatible local cloud emulator (Floci) Each deployment takes two commands. Total time: under 5 minutes. Quick Answer The Stacker deployment pattern for any GitHub repo: stacker init --from-github owner/repo --with-ai --force stacker deploy --target local That's it. Stacker handles project detection, Docker configuration, service setup, healthchecks, env vars, and secrets generation. Prerequisites Docker installed ( docker --version ) Git installed ( git --version ) Optional: Ollama running locally for AI-assisted config ( ollama serve ) Step 1: Install Stacker curl -fsSL https://raw.githubusercontent.com/trydirect/stacker/main/install.sh | bash stacker --version # Should print: stacker 0.3.0 Step 2: Deploy Umami Analytics Umami is a privacy-first, open-source alternative to Google Analytics. It uses PostgreSQL for data storage and runs on Node.js. 2.1 Generate the configuration mkdir ~/umami && cd ~/umami stacker init --from-github umami-software/umami --with-ai --force Stacker clones the repo, reads the docker-compose.yml, detects a Node.js app with a PostgreSQL dependency, and generates: # What you'll see in your directory: stacker.yml # The deployment config .stacker/Dockerfile # Optimized Node.js Dockerfile .stacker/docker-compose.yml # Production compose file .env.example # Template with detected env vars scripts/generate-secrets.sh # Auto-generate DB passwords 2.2 Generate secrets and deploy cp .env.example .env ./scripts/generate-secrets.sh # Fills in DB_PASSWORD, etc. stacker config validate # ✓ Configuration is valid stacker deploy --target local # Builds and starts containers 2.3 Verify stacker status # Should show: umami (running), postgres (running) # Open in browser open http://localhost:3000 Step 3: Deploy ArchiveBox ArchiveBox is a self-hosted web archiving solution. Python-based, with a more complex setup involving multiple volumes and environment variables. mkdir ~/archivebox && cd ~/archivebox stacker init --from-github ArchiveBox/ArchiveBox --with-ai --force # Review the generated stacker.yml cat stacker.yml # Generate secrets and deploy cp .env.example .env ./scripts/generate-secrets.sh stacker deploy --target local Notice how Stacker handled ArchiveBox's complexity: multiple volumes for data persistence, the correct entrypoint command, and all the environment variables pulled from the project's documentation. Step 4: Deploy Floci (AWS Local Emulator) Floci emulates AWS services locally. It's a complex Go project with Docker-in-Docker, multiple port ranges, and volume mounts for the Docker socket. mkdir ~/floci && cd ~/floci stacker init --from-github floci-io/floci --with-ai --force Stacker handles the complexity automatically: Detects the Dockerfile at the project root Reads the docker-compose.yml to find port mappings (4566, plus the port ranges) Extracts all 7 FLOCI_* environment variables from the compose file Identifies the Docker socket volume mount ( /var/run/docker.sock ) Generates a clean stacker.yml with the app as app.image: floci/floci:latest Step 5: Understanding the Generated Config Let's look at what Stacker produced for Umami. The stacker.yml will look something like: name: umami deploy: target: local app: type: node path: . ports: - "3000:3000" environment: DATABASE_URL: "postgresql://umami:${DB_PASSWORD}@postgres:5432/umami" HASH_SALT: "${HASH_SALT}" services: - name: postgres image: postgres:16-alpine ports: - "127.0.0.1:5432:5432" environment: POSTGRES_DB: umami POSTGRES_USER: umami POSTGRES_PASSWORD: "${DB_PASSWORD}" volumes: - postgres_data:/var/lib/postgresql/data healthcheck: test: "CMD-SHELL pg_isready -U umami -d umami" interval: 5s timeout: 2s retries: 10 volumes: postgres_data: {} proxy: type: none auto_detect: false Key things Stacker got right without any manual input: Correct app type: Node.js detected from package.json Database service: PostgreSQL extracted from docker-compose.yml Healthcheck: pg_isready automatically added Port isolation: PostgreSQL bound to 127.0.0.1 - not exposed externally Volume persistence: Named volume for database data Secret handling: All passwords use ${VAR_NAME} syntax, never hardcoded Step 6: Customizing the Configuration Edit stacker.yml to customize your deployment: # Add a reverse proxy with SSL proxy: type: nginx auto_detect: true domains: - domain: umami.yourdomain.com ssl: auto upstream: app:3000 # Enable monitoring monitoring: status_panel: true healthcheck: endpoint: /api/health interval: 30s # Add AI troubleshooting for failed deployments ai: enabled: true provider: ollama After editing, redeploy: stacker config validate stacker deploy --target local Step 7: Deploying to Production Once your stack works locally, deploy it to your own server: # Option A: Your own server stacker config setup server # Enter: host IP, SSH user, SSH key path stacker deploy --target server # Option B: Cloud provider (Hetzner, DO, AWS, etc.) stacker config setup cloud # Interactive wizard: choose provider, region, instance size stacker deploy --target cloud Stacker provisions the server, installs Docker, deploys your stack, configures the reverse proxy with SSL, and installs the Status Panel agent for ongoing monitoring - all from the CLI. What to Do When Things Go Wrong AI generation fails # Stacker falls back to template-based detection automatically. # Or try a different AI provider: stacker init --from-github owner/repo --with-ai \ --ai-provider openai --ai-api-key sk-... --force Deployment fails # Check logs stacker logs # With AI troubleshooting enabled in stacker.yml: stacker ai ask "why is my postgres container crashing?" --context ./stacker.yml Port conflict # Edit stacker.yml to change the host port: app: ports: - "3001:3000" # Changed host port from 3000 to 3001 # Redeploy stacker deploy --target local Need to regenerate # Use --force to overwrite existing files stacker init --from-github owner/repo --with-ai --force Beyond the Tutorial Stacker does much more than deploy from GitHub: Visual Stack Builder: Drag-and-drop interface for composing multi-service stacks at try.direct 56+ pre-built stacks: One-click deploy for n8n, Supabase, Ghost, Grafana, Open WebUI, and more Pipes: Container-to-container data flows (like self-hosted Zapier) CI/CD integration: stacker ci export --platform github generates GitHub Actions workflows Vault secrets: Remote secret storage and injection for production deployments Key Takeaways Any GitHub repo with a docker-compose.yml can be deployed in two commands --with-ai dramatically improves config quality by reading README, compose, and source Healthchecks, port isolation, and secret handling are automatic The same stacker.yml works identically on local, server, and cloud targets You own the infrastructure - Stacker just manages the deployment Everything is open source (MIT) on GitHub

Step-by-step tutorial: deploy any GitHub project with Stacker in 5 minutes. From discovery to production - no Docker expertise required.

From GitHub Repo to Production - A 5-Minute Stacker Tutorial

The Self-Hosting Landscape in 2026 Self-hosting has never been more accessible. Between Docker, Compose, and a growing ecosystem of deployment tools, you no longer need a full-time DevOps engineer to run production workloads on your own servers. But the landscape is crowded, and picking the right tool depends on what you actually need: simplicity, control, cost predictability, or all three. Quick Answer Use Stacker if you want to deploy any GitHub repo in one command and own your infrastructure. It auto-detects project types, generates Docker configurations, provisions cloud servers, and deploys - all from a CLI. Unlike Vercel or Railway, you own the servers. Unlike Coolify or CapRover, you don't need to set them up first. The Competitors at a Glance Tool Model Best For Biggest Limitation Stacker Open-source CLI + hosted platform Deploying OSS projects, multi-service stacks Newer ecosystem, smaller community Vercel / Netlify PaaS, usage-based pricing Frontend apps, serverless functions Not for databases or stateful workloads Railway / Render PaaS, per-service pricing Full-stack apps with managed DBs Unpredictable bills, platform lock-in Coolify Self-hosted OSS, free Heroku-like PaaS on your own server Must provision and maintain the server yourself CapRover Self-hosted OSS, free Simple app hosting with a web UI Single-server only, limited multi-stack support Portainer Self-hosted, free/enterprise Managing existing Docker containers Manages containers you already have - doesn't create them Stacker vs Vercel/Railway: Own Your Infrastructure Vercel and Railway are excellent for getting started fast - push to Git, get a URL. But the tradeoffs compound over time: Pricing scales with usage, not value. A moderately busy app can cost $50-200/mo before you know it. You don't own the infrastructure. Migrating away means re-architecting your deployment. Limited to their supported stack. Want to run a Rust binary, an n8n workflow, or a custom Docker image? You're often out of luck or paying a premium. With Stacker, you deploy to your own Hetzner, DigitalOcean, AWS, or on-premise server. You pay the cloud provider directly - Stacker manages the deployment, not the bill. A $5/mo Hetzner VPS running Stacker can replace $200/mo of PaaS services. Stacker vs Coolify: Provisioning Included Coolify is the most popular self-hosted PaaS. It gives you a Heroku-like dashboard on your own server. But there's a catch: Coolify requires you to already have a server running. You need to provision a VPS, install Docker, install Coolify, configure networking, and then start deploying apps. That's the part Coolify doesn't help with. Stacker operates one layer below and above. It provisions the cloud server for you (from 8+ providers including Hetzner, DigitalOcean, AWS, Linode, Vultr, Contabo), deploys your stack, configures the reverse proxy and SSL, sets up monitoring, and installs a Status Panel agent for ongoing management. You can use Coolify alongside Stacker - Stacker provisions the server, then Coolify manages day-2 operations. They complement each other. Stacker vs CapRover: Multi-Service, Multi-Cloud CapRover is a simple, single-server PaaS. Install it on one machine, deploy apps through a web UI. It works well for simple setups but hits walls when you need: Multi-server deployments (API on one machine, database on another) Complex multi-container stacks with service dependencies Cloud-provider portability (move from Hetzner to AWS without changing your deployment config) Pre-built templates for common stacks (n8n + Qdrant + Ollama, for example) Stacker handles all of these with a single stacker.yml file. You define your stack once, and it deploys identically to any provider. Where Stacker Is Unique Six capabilities that no single competitor combines: Full lifecycle: Provision server → generate Dockerfiles → deploy → configure proxy/SSL → monitor → update. One CLI, end to end. Multi-cloud: 8+ providers (Hetzner, DO, Linode, Vultr, Contabo, AWS, OCI, OVH) plus your own server. Switch providers by changing one line. AI-powered config generation: Point at a GitHub repo, Stacker reads the README, compose file, and source to infer the full deployment config. No lock-in: You own the servers, SSH keys, and data. Stacker generates standard Docker Compose files you can use without the CLI. Status Panel agent: A lightweight agent runs on your server for health monitoring, log streaming, container restarts, and configuration updates - without SSH. Marketplace: 56+ pre-built stacks (n8n, Qdrant, Ollama, Supabase, Ghost, Umami, Plausible, etc.) deployable in one click. Creators earn 75% revenue share. When to Use What Your Need Best Tool Why Deploy a Next.js landing page Vercel Free tier, instant deploys, built-in analytics Deploy a full-stack app with DB Railway or Stacker Railway for speed, Stacker for cost control Self-host 10+ OSS tools on one server Coolify Web UI for managing many apps on a single machine Deploy a complex multi-service stack (API + DB + cache + queue + AI) Stacker Auto-detects everything, provisions the server, one command Manage existing Docker containers Portainer Purpose-built for container management, not deployment Deploy any GitHub repo without reading the docs Stacker stacker init --from-github is unmatched The Bottom Line Stacker isn't trying to be the best at everything. It's the best at one specific workflow: I found a project on GitHub, I want it running on my server, and I don't want to spend an hour configuring YAML. For everything else, use the right tool - but for that workflow, nothing else comes close. Getting Started # Install curl -fsSL https://raw.githubusercontent.com/trydirect/stacker/main/install.sh | bash # Deploy something stacker init --from-github plausible/analytics --with-ai stacker deploy --target local Stacker CLI is MIT-licensed on GitHub . The managed platform is at try.direct .

Compare Stacker, Coolify, CapRover, Vercel, and Railway for self-hosting. Which tool gives you the most control with the least ops overhead?

Self-Host Without the Ops Overhead - Stacker vs Coolify, Vercel, and CapRover

What Is Stacker? Stacker is an open-source CLI tool that turns any project into a deployable Docker stack. Add a stacker.yml to your repo, and Stacker generates Dockerfiles, docker-compose definitions, reverse-proxy configs, and deploys locally or to cloud providers - optionally with AI assistance. It's like having a DevOps engineer in your terminal. Quick Answer Deploy any GitHub repo with one command: stacker init --from-github owner/repo --with-ai stacker deploy --target local Stacker shallow-clones the repo, auto-detects the project type and services, generates a ready-to-use stacker.yml , and deploys with a single follow-up command. No YAML, no Docker expertise needed. The Problem: "I Found a Cool Project, Now What?" You find an interesting project on GitHub - a self-hosted analytics dashboard, an AI agent framework, an internal helpdesk. The README says "docker compose up" but then you discover: The compose file expects 14 environment variables you need to configure Three of those are secrets you're supposed to generate yourself The database needs manual initialization before the app starts Port mapping is wrong for your machine SSL needs configuring separately What should take 30 seconds becomes a 30-minute debugging session. Stacker fixes this. Installing Stacker curl -fsSL https://raw.githubusercontent.com/trydirect/stacker/main/install.sh | bash stacker --version Stacker is a single static binary for macOS and Linux, x86_64 and ARM64. No runtime dependencies beyond Docker and git. The Magic Command: stacker init --from-github This is the feature that makes Stacker fundamentally different from every other deployment tool. You point it at any GitHub repo, and it does the rest: # Deploy ArchiveBox (self-hosted web archiving) stacker init --from-github ArchiveBox/ArchiveBox --with-ai stacker deploy --target local # Deploy Plausible Analytics (privacy-first analytics) stacker init --from-github plausible/analytics --with-ai stacker deploy --target local # Deploy anything with a docker-compose.yml stacker init --from-github owner/repo --with-ai What happens under the hood: Shallow-clone the repo into a temp directory Auto-detect the project type (Node, Python, Rust, Go, PHP, static, or custom) Parse docker-compose.yml to identify services - the main app, databases, caches, queues Classify infrastructure (postgres, redis, mysql, mongo, rabbitmq, elasticsearch) and infer appropriate healthchecks Detect environment variables and generate .env.example with secrets marked for generation Generate a complete stacker.yml with the correct app type, image or Dockerfile reference, port mappings, service dependencies, and monitoring config Create .stacker/Dockerfile and .stacker/docker-compose.yml for immediate deployment Generate scripts/generate-secrets.sh that uses openssl rand -hex to fill in secret values AI-Powered Config Generation With --with-ai , Stacker scans the repo's README, docker-compose.yml, Dockerfile, package.json, and source files using an LLM to infer context that simple file detection can't discover - port mappings buried in documentation, environment variables documented in READMEs, service relationships, and deployment prerequisites. Supports Ollama (local, free, private by default), OpenAI, and Anthropic: # Local AI with Ollama (default, free) stacker init --from-github owner/repo --with-ai # OpenAI stacker init --from-github owner/repo --with-ai \ --ai-provider openai --ai-api-key sk-... # Anthropic stacker init --from-github owner/repo --with-ai \ --ai-provider anthropic --ai-api-key $ANTHROPIC_API_KEY # Remote Ollama server stacker init --from-github owner/repo --with-ai \ --ai-endpoint http://192.168.1.100:11434 --ai-model qwen2.5-coder If the AI provider is unreachable, Stacker falls back to template-based detection automatically. And if the model outputs docker-compose format instead of stacker format (common with smaller models), Stacker automatically converts it. From Zero to Deployed in Two Commands Here's the complete workflow for deploying any project you find on GitHub: # 1. Generate configuration from the repo stacker init --from-github floci-io/floci --with-ai --force # 2. Review what was generated stacker config validate cat stacker.yml # 3. Deploy locally stacker deploy --target local # 4. Check status stacker status # 5. View logs stacker logs What Gets Generated After running stacker init --from-github , your directory contains: stacker.yml - The single deployment configuration file .stacker/Dockerfile - Optimized for the detected project type .stacker/docker-compose.yml - Ready-to-use compose definition .env.example - Template with detected environment variables scripts/generate-secrets.sh - Auto-generates secure random values for secrets Healthchecks That Work Out of the Box Stacker automatically infers appropriate healthchecks for infrastructure services: PostgreSQL: pg_isready -U postgres (interval 5s, timeout 2s) Redis: redis-cli ping (interval 5s, timeout 2s) MySQL/MariaDB: mysqladmin ping -h localhost (interval 5s, timeout 2s) MongoDB: mongosh --eval 'db.adminCommand("ping")' RabbitMQ: rabbitmq-diagnostics -q ping Elasticsearch: curl -fsSL http://localhost:9200/_cluster/health Security by Default Stacker enforces several security best practices automatically: Secrets use ${VAR_NAME} syntax - never hardcoded passwords Infrastructure ports bind to 127.0.0.1 - databases aren't exposed to the network Secrets generation is idempotent - generate-secrets.sh only fills missing values, never overwrites existing ones .env is gitignored - secrets never accidentally committed Beyond Local: Deploy to Any Cloud Once your stack works locally, deploy it to production on your own infrastructure: # Deploy to your own server stacker config setup server stacker deploy --target server # Deploy to Hetzner, DigitalOcean, AWS, Linode, or Vultr stacker config setup cloud stacker deploy --target cloud You own the infrastructure. Stacker manages the deployment. No vendor lock-in, no platform markups on your cloud bill. Open Source The Stacker CLI is MIT-licensed and available on GitHub at github.com/trydirect/stacker . The deployment engine, visual Stack Builder, and marketplace are hosted services available at try.direct . Getting Started Install: curl -fsSL https://raw.githubusercontent.com/trydirect/stacker/main/install.sh | bash Find a project: Browse GitHub for anything with a docker-compose.yml Deploy: stacker init --from-github owner/repo --with-ai && stacker deploy --target local Iterate: Edit stacker.yml , re-run stacker deploy

Deploy any GitHub repository with a single command using Stacker. No Docker expertise needed - auto-detects project type, services, ports, and env vars.

Deploy Any GitHub Repo in One Command with Stacker

OpenClaw went from a side project to hundreds of thousands of GitHub stars in months. It also went from zero to hundreds of malicious skills on its own marketplace in weeks. Both facts describe the same tool, and if you are planning to run it, you need to understand why.

OpenClaw connects to messaging apps like WhatsApp, Telegram, Discord, and Slack, reads local files, executes shell commands, and acts on your behalf across whatever services you connect it to.

OpenClaw is not unsafe by design, but its permission model makes a compromised instance far more damaging than typical software. The risks are well documented at this point: a supply-chain campaign called ClawHavoc placed 341 malicious skills on ClawHub, a critical remote-code-execution flaw (CVE-2026-25253) let attackers hijack instances through a single malicious link, and Cisco found that 26% of 31,000 scanned agent skills contained at least one exploitable vulnerability. None of that means the tool is unusable. It means safe deployment comes down to isolation, a locked-down firewall, skill vetting, and logging, not avoidance.

This guide covers what independent security researchers actually documented, why the risk is structural rather than a handful of bad skills, and a practical checklist for running OpenClaw without becoming the next case study.

Why OpenClaw Is a Different Kind of Security Risk

Most software vulnerabilities give an attacker a foothold. A compromised OpenClaw instance often gives an attacker the same permissions the agent already has, which for most users includes:

Read access to local files, and often to connected cloud storage

Live API keys and OAuth tokens for every connected service, from email and calendars to crypto wallets in some setups

The ability to install new skills on its own, if configured for autonomous operation

Skills, OpenClaw's extension mechanism, run with the same permissions as the agent itself. There is no sandbox boundary between "this skill checks the weather" and "this skill reads your browser's saved passwords." Security researchers have repeatedly shown that the two can look identical from the outside, and by design, ClawHub requires nothing more than a week-old GitHub account to publish a new one.

What the Security Research Actually Found

These incidents are documented by named security vendors and researchers, not rumor. Where sources disagreed on details, we've gone with the figures corroborated by multiple independent outlets.

The ClawHavoc supply chain attack. In February 2026, Koi Security audited all 2,857 skills then live on ClawHub and identified 341 malicious entries, roughly 12% of the registry. Of those, 335 were traced to a single coordinated campaign, now tracked as ClawHavoc, that used fake "prerequisite" install steps to deliver Atomic Stealer (AMOS) on macOS, a tool built to harvest browser passwords, crypto wallet keys, and session cookies. By later scans the count of flagged malicious skills had grown well past 800 as the registry itself expanded.

A critical remote code execution flaw. Tracked as CVE-2026-25253 and rated CVSS 8.8, this vulnerability let an attacker hijack a running OpenClaw instance and steal its authentication token simply by getting the victim to click a crafted link or visit a malicious page. It was patched within days of disclosure, but only for users who updated.

Widespread vulnerability rates across the wider skill ecosystem. Cisco's AI Defense team scanned roughly 31,000 agent skills across multiple frameworks, OpenClaw included, and found that about 26% contained at least one exploitable vulnerability: command injection, data exfiltration, or prompt injection.

Credential exposure at scale. Snyk's separate ToxicSkills audit scanned close to 4,000 ClawHub skills and found that 7.1% (283 skills) exposed sensitive credentials in plaintext, with 13.4% classified as containing critical-severity issues.

Scanners can be bypassed. Trail of Bits demonstrated that ClawHub's own malware-scanning pipeline could be defeated by padding a malicious skill file with roughly 100,000 blank lines, pushing the harmful code outside the window the scanner actually inspects. Comparable gaps were found in third-party scanning tools built specifically to catch this kind of threat.

Exposed instances at scale. Cisco has reported more than 135,000 OpenClaw instances reachable from the public internet, many running outdated, vulnerable versions.

The institutional response has been telling. China's government restricted state agencies, state-owned enterprises, and banks from running OpenClaw, citing risks around unauthorized data deletion, data leaks, and excessive resource usage. Multiple security vendors, including Cisco, report seeing similar internal restrictions inside enterprises as adoption grows.

The checklist below is what deploying it "deliberately, isolated, and monitored" actually looks like in practice.

How to Deploy OpenClaw Safely: A 7-Step Checklist

1. Never run it on a machine with unrelated sensitive access

Do not install OpenClaw on your daily laptop, especially one logged into personal email, banking, or production infrastructure. A dedicated VM, cloud instance, or container gives you a hard boundary between "if this agent is compromised" and "everything I own".

2. Default to deny on outbound network access

An agent that can only reach the specific APIs it needs is far less useful to an attacker than one with open egress to the entire internet. Configure your firewall for allowlisted outbound connections rather than blocklisting known-bad destinations after the fact.

3. Treat every third-party skill as untrusted code

Skills execute with full agent permissions the moment they are installed. Before adding anything from ClawHub:

Scan it with a dedicated tool built for this, such as Cisco's open-source skill-scanner, rather than relying on the marketplace's built-in check alone

Read the SKILL.md file and any bundled scripts yourself if the skill touches credentials, files, or network calls

Keep the total number of installed skills small; every skill is additional attack surface

Avoid one-click installs from public marketplaces in any environment connected to sensitive accounts

4. Keep credentials out of the agent's filesystem

Use a secrets manager or vault rather than letting OpenClaw store API keys and tokens in plaintext config files. Rotate tokens regularly, and scope each connected service to the minimum permissions the agent actually needs. If a skill only needs to read a calendar, it should not also hold write access to email.

5. Patch on a schedule, not when you remember

CVE-2026-25253 and similar flaws were fixed quickly once disclosed. The instances that got compromised were, overwhelmingly, ones running outdated versions. Update OpenClaw and its dependencies on a defined cadence, and subscribe to the project's security advisories.

If OpenClaw can act autonomously, you need an audit trail of what it acted on: which skills ran, which tool calls fired, which external destinations received data. Runtime monitoring is what turns "we think something went wrong three weeks ago" into "we caught this in under a minute".

7. Separate the agent from production infrastructure

If OpenClaw is managing DevOps tasks, deploying code, or touching infrastructure, give it a scoped service account rather than admin credentials, and route its actions through the same review gates you would require of a junior engineer.

Once the isolation and monitoring model above is in place, the remaining decision is infrastructure: a self-managed server, a cloud VM you harden by hand, or a preconfigured deployment.

TryDirect's marketplace includes an OpenClaw preconfigured stack, built by the StackDog Team, that deploys to Amazon EC2, DigitalOcean, or Hetzner Cloud with a firewall already configured. It is worth being direct about what that stack currently is and is not: the listing is explicitly labeled an experimental setup, and at the time of writing it has passed 3 of the marketplace's 8 verification checks. That is not unusual for a preconfigured stack built around a tool this new, but it does mean the firewall and isolation it ships with are a starting point, not a substitute for the credential hygiene, skill vetting, and monitoring practices above.

The advantage of deploying through a marketplace stack rather than a manual install is mainly speed and a consistent baseline: a fresh, isolated instance with networking pre-configured, rather than OpenClaw sharing a machine with other workloads by default. Whichever route you choose, self-hosted VM, containerized deployment, or a marketplace image, the checklist does not change. Isolation, a locked-down firewall, vetted skills, and real logging are what determine whether an OpenClaw deployment is a productivity tool or a liability.

Is OpenClaw safe to use? OpenClaw is not inherently unsafe, but its permission model, an agent with shell access, file access, and live credentials, means the consequences of a compromise are more severe than with typical software. Safety depends heavily on how it is deployed and which skills are installed, not just which version you run.

What is ClawHub and why does it matter for security? ClawHub is OpenClaw's community marketplace for skills, the packages that extend what the agent can do. Because anyone with a week-old GitHub account can publish and vetting has historically been limited, ClawHub was the primary vector for the ClawHavoc campaign and other malicious-skill incidents documented by Koi Security, Snyk, and Cisco.

Should I run OpenClaw on my personal computer? It is not recommended if that machine also holds sensitive personal accounts, saved passwords, or production credentials. A dedicated, isolated environment, whether a cloud VM, container, or preconfigured stack, limits the blast radius if something goes wrong.

Do skill scanners fully solve the malicious skill problem? No. Trail of Bits demonstrated bypasses against ClawHub's own scanning pipeline and several third-party scanners. Scanning is a useful first filter, not a guarantee, which is why limiting the number of installed skills and reviewing anything with credential or network access still matters.

Deploy OpenClaw on an isolated, firewall-configured instance in minutes with the OpenClaw preconfigured stack on TryDirect.

OpenClaw went from a side project to hundreds of thousands of GitHub stars in months. It also went from zero to hundreds of malicious skills on its own marketplace in weeks. Both facts describe the same tool, and if you are planning to run it, you need to understand why.

OpenClaw connects to messaging apps like WhatsApp, Telegram, Discord, and Slack, reads local files, executes shell commands, and acts on your behalf across whatever services you connect it to.

OpenClaw is not unsafe by design, but its permission model makes a compromised instance far more damaging than typical software. The risks are well documented at this point: a supply-chain campaign called ClawHavoc placed 341 malicious skills on ClawHub, a critical remote-code-execution flaw (CVE-2026-25253) let attackers hijack instances through a single malicious link, and Cisco found that 26% of 31,000 scanned agent skills contained at least one exploitable vulnerability. None of that means the tool is unusable. It means safe deployment comes down to isolation, a locked-down firewall, skill vetting, and logging, not avoidance.

This guide covers what independent security researchers actually documented, why the risk is structural rather than a handful of bad skills, and a practical checklist for running OpenClaw without becoming the next case study.

Why OpenClaw Is a Different Kind of Security Risk

Most software vulnerabilities give an attacker a foothold. A compromised OpenClaw instance often gives an attacker the same permissions the agent already has, which for most users includes:
Full shell access on the host machine
Read access to local files, and often to connected cloud storage
Live API keys and OAuth tokens for every connected service, from email and calendars to crypto wallets in some setups
The ability to install new skills on its own, if configured for autonomous operation

Skills, OpenClaw's extension mechanism, run with the same permissions as the agent itself. There is no sandbox boundary between "this skill checks the weather" and "this skill reads your browser's saved passwords." Security researchers have repeatedly shown that the two can look identical from the outside, and by design, ClawHub requires nothing more than a week-old GitHub account to publish a new one.

What the Security Research Actually Found

These incidents are documented by named security vendors and researchers, not rumor. Where sources disagreed on details, we've gone with the figures corroborated by multiple independent outlets.

The ClawHavoc supply chain attack. In February 2026, Koi Security audited all 2,857 skills then live on ClawHub and identified 341 malicious entries, roughly 12% of the registry. Of those, 335 were traced to a single coordinated campaign, now tracked as ClawHavoc, that used fake "prerequisite" install steps to deliver Atomic Stealer (AMOS) on macOS, a tool built to harvest browser passwords, crypto wallet keys, and session cookies. By later scans the count of flagged malicious skills had grown well past 800 as the registry itself expanded.

A critical remote code execution flaw. Tracked as CVE-2026-25253 and rated CVSS 8.8, this vulnerability let an attacker hijack a running OpenClaw instance and steal its authentication token simply by getting the victim to click a crafted link or visit a malicious page. It was patched within days of disclosure, but only for users who updated.

Widespread vulnerability rates across the wider skill ecosystem. Cisco's AI Defense team scanned roughly 31,000 agent skills across multiple frameworks, OpenClaw included, and found that about 26% contained at least one exploitable vulnerability: command injection, data exfiltration, or prompt injection.

Credential exposure at scale. Snyk's separate ToxicSkills audit scanned close to 4,000 ClawHub skills and found that 7.1% (283 skills) exposed sensitive credentials in plaintext, with 13.4% classified as containing critical-severity issues.

Scanners can be bypassed. Trail of Bits demonstrated that ClawHub's own malware-scanning pipeline could be defeated by padding a malicious skill file with roughly 100,000 blank lines, pushing the harmful code outside the window the scanner actually inspects. Comparable gaps were found in third-party scanning tools built specifically to catch this kind of threat.

Exposed instances at scale. Cisco has reported more than 135,000 OpenClaw instances reachable from the public internet, many running outdated, vulnerable versions.

The institutional response has been telling. China's government restricted state agencies, state-owned enterprises, and banks from running OpenClaw, citing risks around unauthorized data deletion, data leaks, and excessive resource usage. Multiple security vendors, including Cisco, report seeing similar internal restrictions inside enterprises as adoption grows.

The checklist below is what deploying it "deliberately, isolated, and monitored" actually looks like in practice.

How to Deploy OpenClaw Safely: A 7-Step Checklist

1. Never run it on a machine with unrelated sensitive access
Do not install OpenClaw on your daily laptop, especially one logged into personal email, banking, or production infrastructure. A dedicated VM, cloud instance, or container gives you a hard boundary between "if this agent is compromised" and "everything I own".

2. Default to deny on outbound network access
An agent that can only reach the specific APIs it needs is far less useful to an attacker than one with open egress to the entire internet. Configure your firewall for allowlisted outbound connections rather than blocklisting known-bad destinations after the fact.

3. Treat every third-party skill as untrusted code
Skills execute with full agent permissions the moment they are installed. Before adding anything from ClawHub:
Scan it with a dedicated tool built for this, such as Cisco's open-source skill-scanner, rather than relying on the marketplace's built-in check alone
Read the SKILL.md file and any bundled scripts yourself if the skill touches credentials, files, or network calls
Keep the total number of installed skills small; every skill is additional attack surface
Avoid one-click installs from public marketplaces in any environment connected to sensitive accounts

4. Keep credentials out of the agent's filesystem
Use a secrets manager or vault rather than letting OpenClaw store API keys and tokens in plaintext config files. Rotate tokens regularly, and scope each connected service to the minimum permissions the agent actually needs. If a skill only needs to read a calendar, it should not also hold write access to email.

5. Patch on a schedule, not when you remember
CVE-2026-25253 and similar flaws were fixed quickly once disclosed. The instances that got compromised were, overwhelmingly, ones running outdated versions. Update OpenClaw and its dependencies on a defined cadence, and subscribe to the project's security advisories.

6. Log everything the agent does
If OpenClaw can act autonomously, you need an audit trail of what it acted on: which skills ran, which tool calls fired, which external destinations received data. Runtime monitoring is what turns "we think something went wrong three weeks ago" into "we caught this in under a minute".

7. Separate the agent from production infrastructure
If OpenClaw is managing DevOps tasks, deploying code, or touching infrastructure, give it a scoped service account rather than admin credentials, and route its actions through the same review gates you would require of a junior engineer.

Where to Actually Run It

Once the isolation and monitoring model above is in place, the remaining decision is infrastructure: a self-managed server, a cloud VM you harden by hand, or a preconfigured deployment.

TryDirect's marketplace includes an OpenClaw preconfigured stack, built by the StackDog Team, that deploys to Amazon EC2, DigitalOcean, or Hetzner Cloud with a firewall already configured. It is worth being direct about what that stack currently is and is not: the listing is explicitly labeled an experimental setup, and at the time of writing it has passed 3 of the marketplace's 8 verification checks. That is not unusual for a preconfigured stack built around a tool this new, but it does mean the firewall and isolation it ships with are a starting point, not a substitute for the credential hygiene, skill vetting, and monitoring practices above.

The advantage of deploying through a marketplace stack rather than a manual install is mainly speed and a consistent baseline: a fresh, isolated instance with networking pre-configured, rather than OpenClaw sharing a machine with other workloads by default. Whichever route you choose, self-hosted VM, containerized deployment, or a marketplace image, the checklist does not change. Isolation, a locked-down firewall, vetted skills, and real logging are what determine whether an OpenClaw deployment is a productivity tool or a liability.

FAQ

Is OpenClaw safe to use? OpenClaw is not inherently unsafe, but its permission model, an agent with shell access, file access, and live credentials, means the consequences of a compromise are more severe than with typical software. Safety depends heavily on how it is deployed and which skills are installed, not just which version you run.

What is ClawHub and why does it matter for security? ClawHub is OpenClaw's community marketplace for skills, the packages that extend what the agent can do. Because anyone with a week-old GitHub account can publish and vetting has historically been limited, ClawHub was the primary vector for the ClawHavoc campaign and other malicious-skill incidents documented by Koi Security, Snyk, and Cisco.

Should I run OpenClaw on my personal computer? It is not recommended if that machine also holds sensitive personal accounts, saved passwords, or production credentials. A dedicated, isolated environment, whether a cloud VM, container, or preconfigured stack, limits the blast radius if something goes wrong.

Do skill scanners fully solve the malicious skill problem? No. Trail of Bits demonstrated bypasses against ClawHub's own scanning pipeline and several third-party scanners. Scanning is a useful first filter, not a guarantee, which is why limiting the number of installed skills and reviewing anything with credential or network access still matters.

Deploy OpenClaw on an isolated, firewall-configured instance in minutes with the OpenClaw preconfigured stack on TryDirect.

ClawHavoc, CVE-2026-25253, and 341 malicious skills: what the OpenClaw security research found, and a checklist to deploy it safely in 2026.

OpenClaw Security: Safe Deployment Guide

What Happens When a Service Is Missing? You've created a project on the Stacker dashboard, deployed it to a cloud server, and everything looks good - except the main application container isn't there. Only the database is running. This usually happens when the project was set up without a proper application template, leaving you with a half-empty stack. In this guide, we'll walk through a real scenario: an Umami analytics project that was deployed with only PostgreSQL and no Umami container. You'll learn how to inspect the deployment, add the missing app via the Stacker API, deploy it, and verify everything works - all from the command line. Quick Answer Use stacker agent status to inspect your deployment, then register the missing app via the Stacker API ( POST /project/{id}/apps ) and deploy it with stacker agent deploy-app . Step 1: Inspect the Current Deployment Start by checking what's actually running on your deployment agent. Use the stacker agent status command with your deployment hash: stacker agent status --deployment deployment_c837325f-34b3-4d1f-b4a0-e55a49cf8926 This shows you the running containers, their images, and their states: ──────────────────────────────────────────────────────────────────────────────────────────── Agent: ● online Heartbeat: 2026-07-01T18:31:56.563318Z ──────────────────────────────────────────────────────────────────────────────────────────── APP NAME IMAGE ENABLED postgres postgres postgres:latest yes ──────────────────────────────────────────────────────────────────────────────────────────── CONTAINER STATE PORTS IMAGE project-postgres-1 ⟳ restarting 5432 postgres:latest statuspanel_agent ▶ running - registry-intl.eu-central-1.… statuspanel ▶ running - registry-intl.eu-central-1.… ──────────────────────────────────────────────────────────────────────────────────────────── Notice only postgres appears in the APP list. The Umami analytics container is completely absent. Step 2: Get Detailed App Configuration For more detail, use the --json flag. This reveals the exact environment variables, port mappings, volumes, and network configuration of the running apps: stacker agent status --deployment deployment_c837325f-34b3-4d1f-b4a0-e55a49cf8926 --json Look for the apps array in the JSON output. For our postgres app: { "apps": [ { "code": "postgres", "image": "postgres:latest", "environment": { "POSTGRES_DB": "umami", "POSTGRES_USER": "umami", "POSTGRES_PASSWORD": "umami" }, "ports": [{"host_port": 5432, "container_port": 5432}], "networks": ["default_network"], ... } ] } This tells us the database credentials ( umami for user, password, and database name) - exactly what we need to configure the Umami container's DATABASE_URL . Step 3: Check If a Template Exists See what templates are available in the marketplace: stacker templates | grep -i umami stacker catalog | grep -i umami If nothing shows up, there's no Umami template you can simply install. You'll need to register the app manually. Step 4: Create the App via the Stacker API Apps are managed through the Stacker REST API. The endpoint is a POST to /project/{project_id}/apps - and it's an upsert, so you can safely run it multiple times. First, find your project ID and get your auth token from ~/.config/stacker/credentials.json : # List projects to find your project ID stacker projects | grep umami # Output: 243 umami 2026-06-30T08:49:12... # Your access token is stored here cat ~/.config/stacker/credentials.json Now create the Umami app: TOKEN="*******token*******" curl -s -X POST "https://try.direct/stacker/project/243/apps" \ -H "Authorization: Bearer $TOKEN" \ -H "Content-Type: application/json" \ -d '{ "code": "umami", "name": "Umami Analytics", "image": "ghcr.io/umami-software/umami:postgresql-latest", "environment": { "DATABASE_URL": "postgresql://umami:umami@postgres:5432/umami", "HASH_SALT": "3b8e9f1a7c2d5e4f6a0b9c8d7e6f5a4b3c2d1e0f" }, "ports": [ {"host_port": 3000, "container_port": 3000, "protocol": "tcp"} ], "depends_on": ["postgres"], "restart_policy": "always", "enabled": true, "deploy_order": 2 }' Successful response: { "message": "OK", "item": { "id": 28, "project_id": 243, "code": "umami", "name": "Umami Analytics", "image": "ghcr.io/umami-software/umami:postgresql-latest", "environment": { "DATABASE_URL": "postgresql://umami:umami@postgres:5432/umami", "HASH_SALT": "3b8e9f1a7c2d5e4f6a0b9c8d7e6f5a4b3c2d1e0f" }, "ports": [{"container_port": 3000, "host_port": 3000, "protocol": "tcp"}], "restart_policy": "always", "enabled": true, "deploy_order": 2, "created_at": "2026-07-01T18:39:11.469030Z", "updated_at": "2026-07-01T18:39:11.469030Z", "config_version": 1 } } Step 5: Plan and Deploy the App First, preview what deploy-app will do: stacker agent deploy-app umami \ --deployment deployment_c837325f-34b3-4d1f-b4a0-e55a49cf8926 \ --plan Expected plan output: { "hasChanges": true, "actions": [ { "kind": "redeploy_app", "target": "app", "appCode": "umami", "reason": "explicit deploy-app plan targets a single app" }, { "kind": "sync_app_config", "target": "app", "appCode": "umami", "reason": "app config version is ahead of the synced Vault/runtime version" } ] } Apply the plan: stacker agent deploy-app umami \ --deployment deployment_c837325f-34b3-4d1f-b4a0-e55a49cf8926 Successful deployment output: Command: cmd_513774de-e91e-4357-b886-8565f3ce3ef2 Type: deploy_app Status: ✓ completed { "app_code": "umami", "container_state": "running", "deployed_at": "2026-07-01T18:40:36Z", "status": "deployed" } Step 6: Verify Everything Is Running Check the status again: stacker agent status --deployment deployment_c837325f-34b3-4d1f-b4a0-e55a49cf8926 ──────────────────────────────────────────────────────────────────────────────────────────── Agent: ● online Heartbeat: 2026-07-01T18:40:36.661528Z ──────────────────────────────────────────────────────────────────────────────────────────── APP NAME IMAGE ENABLED postgres postgres postgres:latest yes umami Umami Analytics ghcr.io/umami-software/umam… yes ──────────────────────────────────────────────────────────────────────────────────────────── CONTAINER STATE PORTS IMAGE umami ▶ running - ghcr.io/umami-software/umam… postgres ▶ running - postgres:latest statuspanel_agent ▶ running - registry-intl.eu-central-1.… statuspanel ▶ running - registry-intl.eu-central-1.… ──────────────────────────────────────────────────────────────────────────────────────────── Both postgres and umami are now listed as apps and running. You can inspect the logs to confirm the application started correctly: stacker agent logs umami \ --deployment deployment_c837325f-34b3-4d1f-b4a0-e55a49cf8926 Look for key log lines: ✓ DATABASE_URL is defined. ✓ Database connection successful. ✓ Database is up to date. > umami@3.2.0 start-server > node server.js ▲ Next.js 16.2.6 - Local: http://localhost:3000 - Network: http://0.0.0.0:3000 ✓ Ready in 0ms Step 7: Configure a Domain Set a public domain for the new app via the API: curl -s -X PUT "https://try.direct/stacker/project/243/apps/umami/domain" \ -H "Authorization: Bearer $TOKEN" \ -H "Content-Type: application/json" \ -d '{"domain": "umami.try.direct", "ssl_enabled": true}' Then configure the reverse proxy on the agent: stacker agent configure-proxy umami \ --domain umami.try.direct \ --port 3000 \ --ssl \ --deployment deployment_c837325f-34b3-4d1f-b4a0-e55a49cf8926 \ --force Troubleshooting Common Issues Issue 1: "service refers to undefined network" If the existing app has networks configured but the Docker Compose file lacks the network definition, update the existing app to remove the network reference: curl -s -X POST "https://try.direct/stacker/project/243/apps" \ -H "Authorization: Bearer $TOKEN" \ -H "Content-Type: application/json" \ -d '{ "code": "postgres", "image": "postgres:latest", ... }' Omitting the networks field lets Docker Compose use its default network, which all services can share. Issue 2: "service refers to undefined volume" Named volumes must be defined at the top level of the Compose file. If the deploy-app command complains about undefined volumes, remove the volumes configuration from the app definition (the data persists on disk via the existing container). Issue 3: Vault is unavailable Some operations (like configure-proxy ) require HashiCorp Vault for credential resolution. If Vault is unavailable, the proxy configuration step will fail. In this case, the app will still run and be accessible on its internal port - you can configure DNS and SSL manually on the server. Key Commands Reference Command Purpose stacker agent status --deployment Inspect running containers and apps stacker agent status --deployment --json Detailed JSON view with config stacker projects | grep Find your project ID POST /project/{id}/apps Register a new app (via API) stacker agent deploy-app --deployment Deploy the registered app to the agent stacker agent logs --deployment View container logs stacker agent configure-proxy --domain ... --port ... Set up reverse proxy and SSL How It All Fits Together The Stacker platform separates app registration from app deployment (done through the CLI, which sends commands to the remote agent). This two-step process gives you full control: API - Define the Docker image, environment variables, port mappings, volumes, restart policy, and deployment order CLI - Push the configuration to the remote server, pull the image, create the container, and wire it into the existing Docker Compose project This approach works for any containerized application, not just Umami. Whether you're adding Redis, a custom API, or a background worker, the workflow is identical. The next time you find a project deployed without its main application container, you now have the exact steps to fix it - inspect, register, deploy, verify.

Learn how to add a missing container service to an existing Stacker deployment using the Stacker CLI and API. Step-by-step walkthrough with real commands and output.

How to Add a Missing Service to a Stacker Deployment

Matomo is an open-source, privacy-first web analytics platform you can self-host. Compared to Google Analytics 4, it gives you 100% data ownership, no data sampling, unlimited retention, and a path to cookieless tracking that the French data protection authority has accepted as consent-exempt. You can deploy a ready-made Matomo stack to your own cloud from try.direct in minutes.

Google Analytics is free, and that is exactly the problem. You are not the customer, your visitors' data is the product. In return for a powerful tool at no cash cost, your numbers get sampled, your data lives on Google's servers, and your visitors get one more cookie banner to dismiss. For a lot of teams in 2026 that trade no longer makes sense.

Matomo is the most established alternative, and the comparison is more even than Google would like. Let us be fair to both, then show you the part most articles skip: how to actually run it.

The first is ownership. GA4 processes and stores your analytics data on Google's infrastructure, largely on US servers. You can read it through dashboards, but ultimate control sits with Google. For EU businesses and regulated industries, that raises real data-residency questions that no setting inside GA4 fully answers.

The second is accuracy. On the free tier, GA4 applies sampling to exploration reports once queries exceed roughly 10 million events. Above that threshold you are looking at estimates, not exact counts. Matomo processes 100% of your data at every traffic level, with no sampling threshold, so the number you see is the number that happened.

The third is retention. GA4 caps event-data retention at 14 months on standard properties. Self-hosted Matomo keeps your data for as long as you want, on your own disk, which matters the moment you need a true year-over-year comparison.

The fourth is consent fatigue. GA4's tracking model generally requires consent in the EU, which means a banner on every visit and lost data every time a visitor clicks reject.

Put plainly, the two tools optimize for different things.

Matomo, self-hosted, gives you complete data ownership on your own server, no sampling at any traffic level, retention for as long as you choose, and a cookieless mode that France's CNIL has accepted as exempt from consent. The software core is free and open source, and it can even import your historical Universal Analytics data so you do not start from zero.

Google Analytics 4 stores your data on Google's servers, samples large exploration reports on the free tier, caps standard retention at 14 months, and generally requires consent in the EU. It does not import Universal Analytics history.

Two things deserve a fair word for Google, though. GA4 is genuinely strong if you live in the Google Ads ecosystem, because it feeds conversion data straight into automated bidding and remarketing, and Matomo does not replicate that natively. Its AI-assisted insights also lower the barrier for teams without an analyst. If paid search is your main channel, that integration alone may keep you on GA4. This is a real tradeoff, not a one-sided pitch.

The data ownership point that actually matters

When your analytics run on someone else's cloud, you have outsourced a piece of your own understanding of your business. Where your traffic comes from, which pages convert, how visitors behave: that is competitive insight, and with GA4 it sits in a third party's system under their terms.

Self-hosted Matomo keeps all of it on infrastructure you control, in the jurisdiction you choose. You decide who can access it, how long to keep it, and when to delete it. That is a posture you can defend to a regulator, a client, or your own security team.

Cookieless tracking and the consent banner

This is the feature that drives most self-hosted adoption. Matomo has a privacy mode that anonymizes IP addresses at ingestion and operates without cookies. EU data protection authorities, including France's CNIL, have explicitly named Matomo's cookieless configuration as one that can run without a consent banner, on the basis that it does not constitute personal data processing.

No banner means no rejected visitors, which means more complete data and a cleaner experience. One honest caveat: the moment you enable behavioral features like session recording, you can re-enter consent territory, because replay can capture personal data on screen. Privacy mode is a deliberate configuration, not an automatic guarantee, so confirm your specific setup against your obligations.

Switching analytics usually means starting from zero. Matomo is an exception, because it can import historical Universal Analytics data, so the years of context you built up do not vanish the way they do when moving cold into GA4. For anyone who still has that old data sitting around, this turns a painful migration into a clean continuation.

Matomo is not magic. The core self-hosted platform is free and complete for standard reporting, but some advanced features such as funnels, certain behavioral tools, and a few premium plugins are paid add-ons. And like any self-hosted app, it needs a server, a database, and basic upkeep. That operational step is the reason many teams stall before they start.

The try.direct Matomo stack ships the platform with everything a real deployment needs, then installs it on your own cloud account. You get Matomo itself, a MySQL database with phpMyAdmin for management, Redis for performance, MyDumper for automated database backups, Fail2ban for protection, and a status panel to keep an eye on the containers. No manual database setup, no PHP tuning, no guesswork.

Run it on Hetzner, DigitalOcean, AWS, Vultr, UpCloud, or any VPS with Docker, and your visitor data never leaves infrastructure you control.

Ready to own your analytics? Deploy the free Matomo stack to your own cloud here: 

Is Matomo a real alternative to Google Analytics 4?

Yes. Matomo has been an established analytics platform for nearly two decades and covers the core analytics needs of most sites: traffic sources, page views, events, goals, and e-commerce, with full data ownership on top.

No. Matomo processes 100% of your data at all traffic levels. GA4 applies sampling to large exploration reports on the free tier.

Can I run Matomo without a cookie consent banner?

In its cookieless privacy mode, yes, in many EU scenarios. France's CNIL has named Matomo's cookieless configuration as exempt from consent. Always confirm your specific configuration against your own legal requirements, especially if you enable behavioral features.

Do I keep my old Universal Analytics data?

Matomo can import historical Universal Analytics data, so you do not have to start from scratch the way you do moving into GA4.

The self-hosted core is free and open source. Some advanced and behavioral features are paid plugins. Your other cost is the server you run it on.

Use the try.direct Matomo stack. It deploys Matomo with its database, caching, and automated backups to your own cloud in minutes, so you skip the manual setup entirely.

Matomo is an open-source, privacy-first web analytics platform you can self-host. Compared to Google Analytics 4, it gives you 100% data ownership, no data sampling, unlimited retention, and a path to cookieless tracking that the French data protection authority has accepted as consent-exempt. You can deploy a ready-made Matomo stack to your own cloud from try.direct in minutes.
Google Analytics is free, and that is exactly the problem. You are not the customer, your visitors' data is the product. In return for a powerful tool at no cash cost, your numbers get sampled, your data lives on Google's servers, and your visitors get one more cookie banner to dismiss. For a lot of teams in 2026 that trade no longer makes sense.
Matomo is the most established alternative, and the comparison is more even than Google would like. Let us be fair to both, then show you the part most articles skip: how to actually run it.

Why teams are leaving GA4
Four reasons come up again and again.
The first is ownership. GA4 processes and stores your analytics data on Google's infrastructure, largely on US servers. You can read it through dashboards, but ultimate control sits with Google. For EU businesses and regulated industries, that raises real data-residency questions that no setting inside GA4 fully answers.
The second is accuracy. On the free tier, GA4 applies sampling to exploration reports once queries exceed roughly 10 million events. Above that threshold you are looking at estimates, not exact counts. Matomo processes 100% of your data at every traffic level, with no sampling threshold, so the number you see is the number that happened.
The third is retention. GA4 caps event-data retention at 14 months on standard properties. Self-hosted Matomo keeps your data for as long as you want, on your own disk, which matters the moment you need a true year-over-year comparison.
The fourth is consent fatigue. GA4's tracking model generally requires consent in the EU, which means a banner on every visit and lost data every time a visitor clicks reject.

The honest comparison
Put plainly, the two tools optimize for different things.
Matomo, self-hosted, gives you complete data ownership on your own server, no sampling at any traffic level, retention for as long as you choose, and a cookieless mode that France's CNIL has accepted as exempt from consent. The software core is free and open source, and it can even import your historical Universal Analytics data so you do not start from zero.
Google Analytics 4 stores your data on Google's servers, samples large exploration reports on the free tier, caps standard retention at 14 months, and generally requires consent in the EU. It does not import Universal Analytics history.
Two things deserve a fair word for Google, though. GA4 is genuinely strong if you live in the Google Ads ecosystem, because it feeds conversion data straight into automated bidding and remarketing, and Matomo does not replicate that natively. Its AI-assisted insights also lower the barrier for teams without an analyst. If paid search is your main channel, that integration alone may keep you on GA4. This is a real tradeoff, not a one-sided pitch.

The data ownership point that actually matters
When your analytics run on someone else's cloud, you have outsourced a piece of your own understanding of your business. Where your traffic comes from, which pages convert, how visitors behave: that is competitive insight, and with GA4 it sits in a third party's system under their terms.
Self-hosted Matomo keeps all of it on infrastructure you control, in the jurisdiction you choose. You decide who can access it, how long to keep it, and when to delete it. That is a posture you can defend to a regulator, a client, or your own security team.

Cookieless tracking and the consent banner
This is the feature that drives most self-hosted adoption. Matomo has a privacy mode that anonymizes IP addresses at ingestion and operates without cookies. EU data protection authorities, including France's CNIL, have explicitly named Matomo's cookieless configuration as one that can run without a consent banner, on the basis that it does not constitute personal data processing.
No banner means no rejected visitors, which means more complete data and a cleaner experience. One honest caveat: the moment you enable behavioral features like session recording, you can re-enter consent territory, because replay can capture personal data on screen. Privacy mode is a deliberate configuration, not an automatic guarantee, so confirm your specific setup against your obligations.

Migrating without losing history
Switching analytics usually means starting from zero. Matomo is an exception, because it can import historical Universal Analytics data, so the years of context you built up do not vanish the way they do when moving cold into GA4. For anyone who still has that old data sitting around, this turns a painful migration into a clean continuation.

The honest cons
Matomo is not magic. The core self-hosted platform is free and complete for standard reporting, but some advanced features such as funnels, certain behavioral tools, and a few premium plugins are paid add-ons. And like any self-hosted app, it needs a server, a database, and basic upkeep. That operational step is the reason many teams stall before they start.
It is also the step try.direct removes.

Deploy Matomo the easy way
The try.direct Matomo stack ships the platform with everything a real deployment needs, then installs it on your own cloud account. You get Matomo itself, a MySQL database with phpMyAdmin for management, Redis for performance, MyDumper for automated database backups, Fail2ban for protection, and a status panel to keep an eye on the containers. No manual database setup, no PHP tuning, no guesswork.
Run it on Hetzner, DigitalOcean, AWS, Vultr, UpCloud, or any VPS with Docker, and your visitor data never leaves infrastructure you control.
Ready to own your analytics? Deploy the free Matomo stack to your own cloud here: 
➞ Matomo

Frequently asked questions
Is Matomo a real alternative to Google Analytics 4?
Yes. Matomo has been an established analytics platform for nearly two decades and covers the core analytics needs of most sites: traffic sources, page views, events, goals, and e-commerce, with full data ownership on top.

Does Matomo sample data like GA4?
No. Matomo processes 100% of your data at all traffic levels. GA4 applies sampling to large exploration reports on the free tier.

Can I run Matomo without a cookie consent banner?
In its cookieless privacy mode, yes, in many EU scenarios. France's CNIL has named Matomo's cookieless configuration as exempt from consent. Always confirm your specific configuration against your own legal requirements, especially if you enable behavioral features.

Do I keep my old Universal Analytics data?
Matomo can import historical Universal Analytics data, so you do not have to start from scratch the way you do moving into GA4.

Is Matomo free?
The self-hosted core is free and open source. Some advanced and behavioral features are paid plugins. Your other cost is the server you run it on.

How do I deploy it quickly?
Use the try.direct Matomo stack. It deploys Matomo with its database, caching, and automated backups to your own cloud in minutes, so you skip the manual setup entirely.

Matomo vs GA4: the open-source, self-hosted analytics with 100% data ownership, zero sampling, and cookieless tracking. See the comparison and deploy it.

Matomo vs GA4: Own Your Analytics Data

Wazuh is a free, open-source platform that unifies SIEM and XDR in one place. It gives you threat detection, file integrity monitoring, vulnerability scanning, and compliance reporting across your servers and cloud workloads, with no license fees and no per-gigabyte pricing. You can deploy a production-ready Wazuh stack to your own cloud from try.direct in minutes.

Here is the uncomfortable truth about most commercial security monitoring: your bill scales with the volume of logs you ingest, not with the number of threats you actually catch. The more visibility you try to buy, the more you pay to store data you may never query. Teams end up filtering out logs to control cost, which quietly shrinks the exact coverage a SIEM is supposed to provide. You are paying more to see less.

Wazuh flips that model. The software is free, the architecture is yours, and the only thing you pay for is the infrastructure you already control.

Wazuh is an open-source security platform that combines two things teams usually buy separately.

The SIEM side collects, normalizes, and analyzes logs from your endpoints, servers, network devices, and cloud workloads. It correlates events in real time and raises alerts when something looks like an attempted or successful intrusion, a misconfiguration, or a policy violation.

The XDR side runs a lightweight agent on each monitored system. Instead of only reading logs after the fact, it watches what is happening on the host itself: file changes, suspicious processes, malware and rootkit indicators, and signs of compromise. When an alert fires, you do not just see a strange login, you see what happened on that machine before and after it.

Putting both in one platform is the point. A log line on its own is noise. A log line plus host context is an investigation you can actually close.

A default Wazuh deployment already covers most of what a small security team needs, and it covers it without a single license key.

On the detection side, Wazuh handles log collection and rule-based correlation across all your systems, flags attempted and successful intrusions, and watches for malware, rootkits, hidden files, and cloaked processes directly on each host. File integrity monitoring tracks changes to the files, permissions, and ownership you care about, and records which user or process made each change, which is exactly the kind of evidence you want during an incident.

On the prevention side, it continuously compares your installed software against updated CVE databases so you find vulnerable packages before an attacker does, and it runs configuration checks against hardening guides to catch risky settings early. When something does go wrong, active response can run automated countermeasures, such as blocking a source address when your criteria are met, and every detected event is mapped to the relevant MITRE ATT&CK tactics so your analysts spend less time guessing and more time responding.

Wazuh reports more than 15 million downloads per year and runs one of the largest open-source security communities in the world. It was also named a 2026 Cybersecurity Stars Awards winner for Best SIEM Platform and Best Cloud Security Platform. This is not a science project, it is a platform large organizations run in production.

If you have ever sat through a PCI DSS or ISO audit, you know that "we have logs somewhere" is not an acceptable answer. Auditors want centralized log collection, file integrity monitoring, and evidence that you review security events.

Wazuh ships with dashboards and reporting aligned to common frameworks including PCI DSS, GDPR, HIPAA, and NIST. File integrity monitoring alone satisfies a specific PCI DSS requirement that many teams scramble to meet at audit time. Because you self-host, the data lives on infrastructure you control, in a jurisdiction you choose, which is exactly the posture regulated buyers increasingly need.

Self-hosted vs managed: the honest tradeoff

Wazuh is free, but it is not effortless. The platform has real depth, and getting the indexer, server, dashboard, and agents wired together correctly takes time and Linux comfort. A managed cloud SIEM hands you a login and a bill. Self-hosted Wazuh hands you full control and a setup task.

That setup task is the friction that stops most teams from switching, even when the cost savings are obvious. It is also exactly the part try.direct removes.

The try.direct Wazuh stack packages the platform with the pieces a real deployment needs, then deploys the whole thing to your own cloud account. You get Wazuh for detection and monitoring, Fail2ban for an extra layer of brute-force protection, Netdata for host-level performance visibility, Portainer for container management, and baseline optimization applied during setup. No manual indexer tuning, no YAML marathon, no weekend lost to install docs.

You run it on Hetzner, DigitalOcean, AWS, Vultr, UpCloud, or any VPS with Docker. The data stays yours.

Ready to start? Deploy the free Wazuh stack to your own cloud here:

Yes. Wazuh is open source and free to download and self-host, even for large organizations. Your costs are the servers you run it on and any optional paid support, not the software itself.

For core SIEM work like log collection, correlation, detection, and dashboards, Wazuh is a credible replacement, and it avoids the per-gigabyte licensing that makes commercial tools expensive at scale. Very large enterprises with heavy advanced-analytics needs should evaluate against their specific requirements.

Endpoints, servers, virtual machines, containers, and cloud workloads. It handles log analysis, intrusion detection, file integrity monitoring, vulnerability detection, configuration assessment, and automated response.

Yes. It includes reporting and controls mapped to standards such as PCI DSS, GDPR, HIPAA, and NIST. Self-hosting also keeps your security data in a jurisdiction you control.

With the try.direct Wazuh stack, a working deployment to your own cloud takes minutes rather than the hours a manual install can require.

A VPS with Docker on a provider like Hetzner, DigitalOcean, AWS, Vultr, or UpCloud. The stack handles the rest.

Wazuh is a free, open-source platform that unifies SIEM and XDR in one place. It gives you threat detection, file integrity monitoring, vulnerability scanning, and compliance reporting across your servers and cloud workloads, with no license fees and no per-gigabyte pricing. You can deploy a production-ready Wazuh stack to your own cloud from try.direct in minutes.
Here is the uncomfortable truth about most commercial security monitoring: your bill scales with the volume of logs you ingest, not with the number of threats you actually catch. The more visibility you try to buy, the more you pay to store data you may never query. Teams end up filtering out logs to control cost, which quietly shrinks the exact coverage a SIEM is supposed to provide. You are paying more to see less.
Wazuh flips that model. The software is free, the architecture is yours, and the only thing you pay for is the infrastructure you already control.

What Wazuh actually is
Wazuh is an open-source security platform that combines two things teams usually buy separately.
The SIEM side collects, normalizes, and analyzes logs from your endpoints, servers, network devices, and cloud workloads. It correlates events in real time and raises alerts when something looks like an attempted or successful intrusion, a misconfiguration, or a policy violation.
The XDR side runs a lightweight agent on each monitored system. Instead of only reading logs after the fact, it watches what is happening on the host itself: file changes, suspicious processes, malware and rootkit indicators, and signs of compromise. When an alert fires, you do not just see a strange login, you see what happened on that machine before and after it.
Putting both in one platform is the point. A log line on its own is noise. A log line plus host context is an investigation you can actually close.

What you get out of the box
A default Wazuh deployment already covers most of what a small security team needs, and it covers it without a single license key.
On the detection side, Wazuh handles log collection and rule-based correlation across all your systems, flags attempted and successful intrusions, and watches for malware, rootkits, hidden files, and cloaked processes directly on each host. File integrity monitoring tracks changes to the files, permissions, and ownership you care about, and records which user or process made each change, which is exactly the kind of evidence you want during an incident.
On the prevention side, it continuously compares your installed software against updated CVE databases so you find vulnerable packages before an attacker does, and it runs configuration checks against hardening guides to catch risky settings early. When something does go wrong, active response can run automated countermeasures, such as blocking a source address when your criteria are met, and every detected event is mapped to the relevant MITRE ATT&CK tactics so your analysts spend less time guessing and more time responding.
Wazuh reports more than 15 million downloads per year and runs one of the largest open-source security communities in the world. It was also named a 2026 Cybersecurity Stars Awards winner for Best SIEM Platform and Best Cloud Security Platform. This is not a science project, it is a platform large organizations run in production.

The compliance angle
If you have ever sat through a PCI DSS or ISO audit, you know that "we have logs somewhere" is not an acceptable answer. Auditors want centralized log collection, file integrity monitoring, and evidence that you review security events.
Wazuh ships with dashboards and reporting aligned to common frameworks including PCI DSS, GDPR, HIPAA, and NIST. File integrity monitoring alone satisfies a specific PCI DSS requirement that many teams scramble to meet at audit time. Because you self-host, the data lives on infrastructure you control, in a jurisdiction you choose, which is exactly the posture regulated buyers increasingly need.

Self-hosted vs managed: the honest tradeoff
Wazuh is free, but it is not effortless. The platform has real depth, and getting the indexer, server, dashboard, and agents wired together correctly takes time and Linux comfort. A managed cloud SIEM hands you a login and a bill. Self-hosted Wazuh hands you full control and a setup task.
That setup task is the friction that stops most teams from switching, even when the cost savings are obvious. It is also exactly the part try.direct removes.

Deploy Wazuh the easy way
The try.direct Wazuh stack packages the platform with the pieces a real deployment needs, then deploys the whole thing to your own cloud account. You get Wazuh for detection and monitoring, Fail2ban for an extra layer of brute-force protection, Netdata for host-level performance visibility, Portainer for container management, and baseline optimization applied during setup. No manual indexer tuning, no YAML marathon, no weekend lost to install docs.
You run it on Hetzner, DigitalOcean, AWS, Vultr, UpCloud, or any VPS with Docker. The data stays yours.
Ready to start? Deploy the free Wazuh stack to your own cloud here:

➞ Wazuh

Frequently asked questions
Is Wazuh really free?
Yes. Wazuh is open source and free to download and self-host, even for large organizations. Your costs are the servers you run it on and any optional paid support, not the software itself.

Can Wazuh replace Splunk or Datadog?
For core SIEM work like log collection, correlation, detection, and dashboards, Wazuh is a credible replacement, and it avoids the per-gigabyte licensing that makes commercial tools expensive at scale. Very large enterprises with heavy advanced-analytics needs should evaluate against their specific requirements.

What does Wazuh monitor?
Endpoints, servers, virtual machines, containers, and cloud workloads. It handles log analysis, intrusion detection, file integrity monitoring, vulnerability detection, configuration assessment, and automated response.

Does Wazuh help with compliance?
Yes. It includes reporting and controls mapped to standards such as PCI DSS, GDPR, HIPAA, and NIST. Self-hosting also keeps your security data in a jurisdiction you control.

How long does it take to deploy?
With the try.direct Wazuh stack, a working deployment to your own cloud takes minutes rather than the hours a manual install can require.

What infrastructure do I need?
A VPS with Docker on a provider like Hetzner, DigitalOcean, AWS, Vultr, or UpCloud. The stack handles the rest.

Wazuh is a free, open-source SIEM and XDR you can self-host. Get threat detection, file integrity monitoring, and compliance, with no per-gigabyte fees.

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