# Beamhall vs Dokploy vs Coolify: when the person shipping the app is an AI agent

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> Author: Beamhall (https://beamhall.com) · Published: 2026-08-26 · Type: comparison
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Dokploy and Coolify make self-hosting easier. Beamhall solves a different
problem: letting AI coding agents ship internal apps without giving them
infrastructure credentials or control.

## TL;DR

- Coolify and Dokploy are self-hosted PaaS products: a developer or operator remains responsible for the infrastructure decisions. Beamhall is an application backplane whose intended operator is the AI coding agent.
- Both Dokploy and Coolify now ship MCP servers that let an agent drive the platform. Beamhall inverts the contract: the agent requests application capabilities and never becomes an infrastructure administrator.
- Beamhall treats the agent and the code it writes as untrusted. Database connection strings and secrets are injected into the running app, never returned to the agent, so prompt injection has nothing to steal.
- Beamhall's defaults are structural, not advisory: least privilege, read-only root filesystem, default-deny egress, IT-set resource ceilings and runtime tier, per-workspace isolation — with no agent tool to widen any of them.
- Preview and live are separate lifecycle states; promotion can be reserved for IT and optionally require four-eyes approval.
- Keep Dokploy or Coolify if your users are developers who want full control over images, Compose files and build strategies. Choose Beamhall if non-developers will build internal apps with AI and you need the constraints to be the product.
- Maturity trade-off: Coolify and Dokploy are established projects; Beamhall is pre-1.0 and entering design-partner validation.

## The premise

Dokploy and Coolify solved an important problem: getting an application onto
your own servers without turning every deployment into a small Kubernetes
consulting engagement.

AI coding agents create a different problem.

The person asking for the application may not know what a container is. They may
not know what Git is. They may be in Finance, Operations, HR, Support, or Sales.
They have an idea, Claude Code or another coding agent can build it, and at some
point they type something close to:

> Publish this so the team can use it.

That sentence changes the threat model.

A conventional self-hosted PaaS assumes that a developer or operator is
ultimately responsible for choosing the source, configuring the runtime, wiring
a database, handling secrets, deciding what should be reachable, and granting
the automation enough access to make those things happen.

Beamhall starts from a different assumption: **both the AI agent and the code it
produces are untrusted**.

That is the useful way to compare Beamhall, Dokploy, and Coolify. Not by
counting deployment buttons, but by asking who is expected to make
infrastructure decisions, what the AI agent is allowed to touch, and what
happens when the builder is not a developer.

## Dokploy and Coolify are PaaS products. Beamhall is an application backplane for agents.

Coolify describes itself as an open-source, self-hostable PaaS alternative to
Vercel, Heroku, and Netlify. It connects to servers, runs applications as Docker
resources, handles routing and TLS, and supports Git-based deployment,
databases, services, previews, API automation, and an increasingly capable MCP
interface.

Dokploy occupies similar territory. It supports applications from Git, Docker
images, and Docker Compose, manages databases and domains, and now has an
official MCP server that exposes the Dokploy API to AI clients.

Those are useful products. If your starting point is:

> I have an application. Help me deploy and operate it.

a self-hosted PaaS is a sensible answer.

Beamhall starts one step earlier:

> I have an employee with an idea and an AI coding agent. Let them create an
> internal application without turning either of them into an infrastructure
> administrator.

That difference sounds subtle until you follow an app all the way from prompt to
production.

## The real difference appears after "publish this"

Imagine a People Operations employee asks Claude Code to build a simple RSVP
application with a database and employee sign-in.

With a conventional PaaS, somebody or something still has to translate the
application into infrastructure decisions:

1. Where does the source live?
2. How is it built?
3. Which image or build method should be used?
4. Which database should be created?
5. Where does the connection string go?
6. Which environment variables are secrets?
7. Which domain should be exposed?
8. Can the application reach the public internet?
9. Who can access the application?
10. Is the agent allowed to push this to production?

An experienced developer may barely notice those decisions. A vibe coder
probably does not know they exist.

An AI agent does know they exist, which creates a new temptation: give the agent
enough tooling and credentials to solve the entire problem itself.

That is the point where "AI-assisted deployment" and "governed AI application
platform" stop being the same thing.

## Dokploy gives agents a powerful deployment API

Dokploy has made a serious investment in agent integration. Its official MCP
server currently exposes hundreds of tools across projects, applications,
deployments, domains, databases, Git providers, Docker, backups, enterprise
features, and other platform functions.

That is useful if you want Claude Code, Cursor, or another MCP client to operate
Dokploy directly. Tool presets and category filtering can reduce the surface
exposed to a client, and Dokploy also supports redacting secret-bearing fields
from MCP responses.

The model is still recognizable, though:

    AI agent --[Dokploy API token]--> Dokploy MCP --[create / configure / deploy]--> PaaS resources

The agent is an automated PaaS operator.

That is a perfectly reasonable model for a developer agent. It is less
comfortable when the user behind the agent is someone who cannot review the
infrastructure decisions the agent is making.

Dokploy itself is moving toward this use case and now explicitly markets a
governed environment for employees deploying AI-built applications. That makes
it one of the more interesting PaaS options for organizations experimenting with
Claude Code and similar tools.

Beamhall takes the idea further by refusing to make the agent an infrastructure
operator in the first place.

## Coolify is adding MCP, but its basic contract remains developer-oriented

Coolify has also added a built-in MCP server. Its current documentation
describes team-scoped API tokens, infrastructure inspection, deployment and
lifecycle operations, resource discovery, logs, and related tools.

Again, this is useful. An AI assistant can understand what is deployed,
troubleshoot failures, and perform operations that its token permits.

But Coolify is explicit about the security boundary of the product. Its security
documentation says the operator remains responsible for server security, Docker
and OS updates, firewall and network controls, application code, container
images, runtime permissions, public endpoints, backups, monitoring, and incident
response.

That contract makes sense for a PaaS.

It also explains why "give Claude access to Coolify" is not the same
architecture as "let a non-developer safely create an internal app with Claude."

Coolify helps you operate infrastructure you control. Beamhall tries to make
most infrastructure choices unavailable to the agent altogether.

## Beamhall gives the agent capabilities, not infrastructure

In Beamhall, the agent does not create a PostgreSQL service and then receive its
password. It asks for a database capability.

The platform creates the database and injects the connection information into
the running application. The connection string is not returned to the agent.

The same pattern applies to secrets. The agent can set a secret, but there is no
corresponding tool to read it back.

For company sign-in, the agent asks for authentication. Beamhall provisions the
application identity under policy.

For object storage or outbound email, the app receives access to a brokered
capability. The upstream provider credential stays outside both the app and the
agent.

The flow looks more like this:

    Employee --["this app needs a database"]--> AI coding agent
             --[request capability]--> Beamhall (policy check · provision · inject at runtime)
             --[connection injected, never returned]--> Application

The agent gets enough information to use the capability, not enough information
to reconfigure the platform behind it.

That distinction matters under prompt injection, agent mistakes,
generated-code vulnerabilities, and plain old bad judgment.

## Git still exists. The vibe coder does not need to care.

A common objection to self-hosted developer platforms is that the workflow
eventually reaches a sentence like "now create a Git repository," which is
approximately where half the intended audience quietly returns to a spreadsheet.

Beamhall has integrated Git, but Git is plumbing.

Each app gets its own private repository. When an agent deploys, Beamhall
provides a one-time push destination, builds the source with a controlled Cloud
Native Buildpacks pipeline, publishes a pinned image to the internal registry,
and runs it.

The human can remain blissfully unaware that a commit, remote, registry, or
build pipeline exists.

This is an important distinction from hiding Git behind a better UI. The
intended operator of the workflow is the agent. The employee describes the
application and evaluates the result.

A developer can still inspect the machinery when necessary. It simply is not a
prerequisite for making a small internal tool.

## The comparison that matters for AI-built internal apps

| Question | Coolify | Dokploy | Beamhall |
| --- | --- | --- | --- |
| Primary design center | Self-hosted PaaS for developers and operators | Self-hosted PaaS with strong AI deployment integration | Self-hosted backplane for AI-agent-built internal apps |
| AI integration | Built-in MCP with team-scoped API authentication and supported operations | Official MCP exposing broad Dokploy API coverage | MCP is the primary agent-facing control surface |
| What the agent becomes | An automation client for the PaaS | A highly capable PaaS operator | A requester of policy-governed application capabilities |
| Source workflow | Git, images, services, other deployment paths | Git, Docker image, Docker Compose | Integrated private Git per app; agent handles the push |
| Build control | Flexible Docker/source deployment model | Flexible build, Docker and Compose model | Controlled Buildpacks pipeline; agent cannot supply its own Dockerfile |
| Database credentials | Managed as platform/application configuration | Internal credentials and environment variables; external vaults supported | Minted and injected by Beamhall; raw connection string is not returned to the agent |
| Secret handling | Encrypted platform values and scoped API behavior | Environment variables plus external secret providers; MCP can redact sensitive fields | Write-only agent interface; secrets injected into the workload |
| Egress posture | Operator-managed as part of server/workload security | Isolation features available; host/network security remains an operator concern | Default-deny per workspace; agent has no tool to widen it |
| Runtime hardening | Operator/application responsibility | Docker/Swarm deployment controls and isolation options | IT-set immutable baseline; runc or gVisor tier |
| Production governance | Teams, roles and token permissions | Enterprise SSO, RBAC and audit features available | Preview and live are separate lifecycle states; promotion can be IT-only with optional four-eyes approval |
| Application SSO | Platform SSO options are focused on Coolify access | Enterprise Application Authentication can protect deployed apps | Company sign-in is a provisioned application capability |
| Open-source model | Apache 2.0 | Core Apache 2.0; proprietary enterprise directory for paid functionality | Apache 2.0 |
| Current maturity | Established project and ecosystem | Established project with active AI/PaaS development | Pre-1.0; currently moving into design-partner validation |

This is not a scorecard where every row should turn green for Beamhall.

If you need an extremely flexible self-hosted PaaS and your users are
developers, flexibility is a feature. Dokploy and Coolify let experienced
operators make choices that Beamhall deliberately removes from an AI agent.

The question is whether you want those choices available in your
employee-facing vibe-coding workflow.

## Security defaults matter more when nobody is reviewing the deployment

Traditional developer platforms can offer secure deployments. The catch is that
somebody needs to configure them securely.

AI-generated internal apps produce a different scale problem. If ten developers
ship ten services, review is plausible. If hundreds of employees discover that
Claude can turn a sentence into a working internal tool, security cannot depend
on each employee understanding container networking.

Beamhall therefore makes several decisions structural rather than advisory:

An application starts least-privileged. Its root filesystem is read-only except
where explicitly writable. Resource ceilings are set by IT. Workspaces are
isolated. Outbound traffic is denied by default. The agent cannot open its own
network path, raise its quota, read secrets, change its runtime security
context, or reach another workspace because **those operations do not exist in
the agent tool surface**.

For stronger workload isolation, a workspace can use gVisor rather than the
default hardened runc runtime.

This does not make AI-generated code magically safe. Nothing does. Beamhall's
own [threat model](https://github.com/Beamhall/beamhall/blob/main/docs/threat-model.md)
explicitly retains the shared-kernel risk of runc and documents the trade-offs
of gVisor.

What it does is reduce the number of security decisions delegated to the thing
most likely to improvise them: the coding agent.

## The database password question is more important than it looks

Consider one small architectural detail.

A coding agent creates a database.

In a typical automation model, the platform returns credentials or makes them
available as configuration. You can mask them in a UI, use an external vault,
narrow token permissions, and redact API responses. All of those are worthwhile
controls.

Beamhall changes the contract.

The agent asks for the database. Beamhall provisions it. The application
receives the connection data at runtime. The agent receives a reference telling
it where the application will find that data.

There is no useful database password sitting in the model context.

Why care? Because an AI coding agent processes untrusted material constantly:
source files, package documentation, logs, copied snippets, user input, issue
descriptions, web content, and tool responses. Prompt injection turns every
unnecessary credential in that context into an avoidable liability.

**The rule: the safest secret for an agent is the one it never had.**

## Production should not be one more tool call

Vibe coding makes preview environments cheap. That is good.

It should not make production approval meaningless.

Beamhall separates the builder's ability to create and deploy a preview from the
ability to promote that application to live use. IT can reserve promotion for a
different role and optionally require a second administrator to approve it.

That creates a useful employee workflow:

    "I need an equipment checkout app." -> Claude -> build + preview
      -> employee tests it -> IT approval -> live

The employee still gets the satisfying part of vibe coding: an idea can become
working software quickly.

IT keeps the part that should never have been crowdsourced: deciding what is
allowed to become a production application inside the company.

Dokploy can provide enterprise RBAC, SSO, audit logs, and application
authentication. Coolify provides teams, roles, scoped API tokens, SSO
integrations, and security controls around its management plane. Organizations
can build governed workflows on top of both.

Beamhall simply makes that workflow the product rather than a platform design
exercise.

## When should you keep Dokploy or Coolify?

If your primary users are developers, there is a strong case for doing exactly
that.

Choose a general-purpose PaaS when your team wants direct control over Docker
images, Compose definitions, build strategies, environment configuration, public
applications, arbitrary services, and the rest of a broad deployment surface.

Coolify is especially attractive if you want a mature, fully open-source
self-hosted PaaS with a large ecosystem and no feature-lock distinction between
its hosted and self-hosted product.

Dokploy is particularly interesting if you want a modern PaaS that is
aggressively integrating with AI coding tools. Its official MCP coverage is
broad, its enterprise governance features are relevant to larger organizations,
and its product direction clearly acknowledges AI-built applications.

Beamhall is the more opinionated choice.

It is for the organization asking a different question:

> How do we let people who are not developers build useful internal software
> with AI, without giving those people or their agents the keys to our
> infrastructure?

If that is the question, the constraints are the feature.

## Is Beamhall a Dokploy alternative?

Sometimes.

If you are searching for a **Dokploy alternative** because you want another
general-purpose self-hosted PaaS, Beamhall is probably not what you want. It
intentionally provides less infrastructure freedom to the agent.

If you are evaluating Dokploy because of its Claude Code and MCP story, and your
actual goal is employee-created internal applications, Beamhall is worth
comparing. Dokploy exposes a powerful deployment platform to the agent. Beamhall
exposes a narrower application capability model and keeps the infrastructure
behind it.

That is not a UI difference. It is a different trust model.

## Is Beamhall a Coolify alternative?

Again, it depends on the job.

As a **Coolify alternative** for hosting arbitrary developer-managed workloads,
Beamhall is intentionally narrower.

As a self-hosted environment for secure vibe coding and AI-generated internal
apps, it addresses several concerns that Coolify leaves to the operator: runtime
posture, outbound-network defaults, capability provisioning, agent credential
exposure, and the separation between building a preview and promoting an app to
production.

Coolify is a platform your developers operate.

Beamhall is intended to be the platform your AI agent operates **without
becoming your infrastructure administrator**.

## What about Claude Code?

Claude Code is the first agent Beamhall is targeting, but Beamhall's agent
interface is based on MCP rather than a private Claude-only deployment protocol.

The important part is not the logo on the coding agent. It is the contract on
the other side of the MCP connection.

A broad infrastructure API asks the model to make infrastructure decisions.

Beamhall's tool surface asks it to state what the application needs.

That is the abstraction we think internal AI development needs.

## A note on maturity

There is an obvious trade-off here and it is better stated plainly.

Coolify and Dokploy are established projects with larger user bases and broader
deployment capabilities. Beamhall is pre-1.0. The core security surface and
end-to-end workflow are built and validated, and the project is entering
design-partner validation.

If your requirement today is "replace Heroku on our own servers," use the mature
PaaS that fits your environment.

If your requirement is "we expect AI coding to spread beyond developers, and we
need an architecture for that before employees create a shadow stack of Vercel,
Neon, Supabase, Fly.io, random API keys, and public URLs," Beamhall is being
built for exactly that problem.

The interesting part of enterprise vibe coding is no longer whether AI can write
the application.

It can.

The hard part is deciding what happens when someone types **publish**.

## Sources and product documentation

The comparison above reflects publicly available product documentation checked
on 26 August 2026.

- Beamhall overview, architecture, security model, features, and roadmap — https://beamhall.com/
- Beamhall source repository and project status — https://github.com/Beamhall/beamhall
- Beamhall security and threat model — https://github.com/Beamhall/beamhall/blob/main/docs/threat-model.md
- Dokploy: Deploy AI Apps Securely — https://dokploy.com/deploy-ai
- Dokploy official MCP server — https://github.com/Dokploy/mcp
- Dokploy application deployment model — https://docs.dokploy.com/docs/core/applications
- Dokploy secrets providers — https://docs.dokploy.com/docs/core/secrets-providers
- Dokploy Enterprise features — https://docs.dokploy.com/docs/core/enterprise
- Coolify overview — https://coolify.io/docs/get-started/introduction
- Coolify MCP documentation — https://coolify.io/docs/integrations/mcp
- Coolify security model — https://next.coolify.io/docs/core/security-model
- Coolify deployment model — https://next.coolify.io/docs/applications/deployments/overview