You let Claude or ChatGPT deploy your app by connecting the assistant to a deployment platform through the Model Context Protocol (MCP), with sign-in and per-org permissions, so it can build, deploy, and check your app with tools instead of pasted commands. Control Plane is a listed connector in Claude and a listed plugin in ChatGPT, so connecting takes a few clicks.
TL;DR
An AI assistant on its own writes code and suggests commands. An assistant connected to a platform over MCP can also build an image, deploy it, read the logs, and roll back a bad release. The connection is what turns “here is a Dockerfile” into “your app is live at this URL.”
The 4-step version:
- Connect. Add Control Plane from Claude’s connector directory or install it from the ChatGPT Plugin directory.
- Authorize. Sign in to Control Plane and pick the orgs the assistant may access.
- Describe the app. Tell the assistant what to build or deploy, and where.
- Review the result. Check the URL, the workload status, and the audit trail. Approve anything destructive yourself.
How do AI assistants deploy apps?
MCP is an open-source standard for connecting AI applications such as Claude and ChatGPT to external tools and data. A platform publishes an MCP server, the assistant connects to it, and the assistant can then run the actions the server exposes, such as creating a workload or reading logs. The specification is public at modelcontextprotocol.io.
| Assistant that only writes code | Assistant connected to a platform |
|---|---|
| Output | Source files and commands |
| Who runs the commands | You, by pasting them |
| Build | You run the build |
| Verification | You check the result |
| Failure handling | You read logs and report back |
| Access control | Your terminal session |
Pasted commands also run with whatever credentials your shell holds. A connected assistant uses the access you granted on the platform, and nothing more.
How do I let Claude or ChatGPT deploy my application?
Connect the assistant to a platform that exposes deployment tools over MCP, then ask it to deploy. With Control Plane, the Claude and ChatGPT paths are directory listings, so there is no JSON to edit and no API key to paste. Coding agents and other MCP clients connect with a plugin or a URL. Each path is below.
How do I let Claude deploy my application?
Control Plane is a listed connector in Claude’s directory. These steps work in claude.ai and in the Claude desktop app. Connectors sync across both.
- Open Claude’s connector directory.
- Find Control Plane.
- Click Connect to Claude.
- Sign in to Control Plane with Google, GitHub, Microsoft, or SAML.
- Pick the orgs Claude may access.
Directory listings connect on the core toolset profile. Full setup details are in the Claude setup guide.
Can Claude deploy my app to production?
Yes, once it is connected to a platform that deploys to production. Claude itself writes the code and calls tools. The platform does the build and the deploy, inside the orgs you selected during sign-in.
Three practices keep that safe:
- Grant only the orgs Claude needs. An org you did not select is out of reach.
- Start on the readonly toolset profile, added as a custom connector at
https://mcp.cpln.io/mcp?toolsets=readonly, if you want Claude to inspect before it changes anything. - Review the result and the audit trail after every change.
Can ChatGPT deploy my app?
Yes. Control Plane is a listed plugin in the ChatGPT Plugin directory.
- Open the Control Plane listing in the ChatGPT Plugin directory.
- Click Install plugin.
- Authorize the orgs ChatGPT may access.
The same listing reaches ChatGPT on the web, desktop, and mobile, and Codex. As with Claude, directory listings connect on the core toolset profile. See the ChatGPT setup guide.
How do coding agents and other MCP clients connect?
Claude Code, Codex, and Antigravity CLI can use the Control Plane AI plugin. It bundles:
- The MCP server.
- Control Plane domain skills.
- Two guided agents: workload troubleshooting, and migration from Kubernetes, Docker Compose, or Helm.
- Slash commands.
- Guardrails.
The plugin is also listed in Anthropic’s community plugin marketplace, where every listing has passed Anthropic’s review. See the AI plugin guide.
Other MCP clients, including Cursor, VS Code, Amp, and OpenCode, connect to https://mcp.cpln.io/mcp. The AI overview lists every supported client.
The MCP server has over 100 tools in four toolset profiles: readonly, core, mk8s, and full. Directory listings use core. To use readonly, mk8s, or full, add the MCP URL by hand.
The MCP server cannot read your local filesystem. A folder on your machine builds with the CLI (cpln image build --remote) from an agent that has a terminal. A repository, an existing image, or code the assistant writes in the chat does not need the CLI.
The CLI, console, Terraform and Pulumi providers, and the MCP server all work through the same Control Plane API.
What should I ask the assistant to do?
Job tools do a whole task in one call. Examples are deploying an app, adding a database, creating or rotating a secret, and diagnosing a workload. The prompts below are quoted from Control Plane’s examples page.
Create an app from scratch
Create me a small Flask app that shows a random quote on each refresh and deploy it to org “my-org”. Give me the URL and the source code when it is up.
The assistant writes the code, builds it into an image in your org’s private registry, and deploys it as a public workload. It reports the URL and a link to download the source.
Deploy an existing image
Deploy nginx:latest as a public app called “web” on port 80 in GVC “production” and give me its URL.
A GVC is Control Plane’s Global Virtual Cloud, the set of locations a workload runs in. One call deploys the image with production defaults: health checks and two replicas, so a deploy does not take it offline.
Build from a repository
Build https://github.com/my-org/my-app on the main branch and deploy it as a public app called “my-app” in GVC “production”.
The repository is built on Control Plane. No Docker daemon or CLI is involved.
Recover from a bad deploy
Workload “api-server” in GVC “production” started failing after the last deploy. Diagnose it, and if the new image is the cause, roll it back and confirm it is healthy again.
Databases, caches, and secrets
Databases and caches come from the Template Catalog. Secrets can be created or rotated without the value passing through the chat, so the credential never lands in the conversation history.
Check workload health (illustration)
This prompt is an illustration, not quoted from Control Plane’s documentation.
Show me the status of every workload in GVC “production” and list any that are unhealthy.
Pair a prompt like this with the readonly profile when you want the assistant to look and not touch.
How do I give an AI agent safe access to my cloud infrastructure?
Start with principles that apply on any platform, then check how your platform implements them.
Vendor-neutral principles
- Least privilege. Give the agent only the permissions the task needs.
- Scoped tokens. Limit credentials by account, project, or org, and prefer short-lived ones.
- Read-only first. Let the agent observe before it can change anything.
- Audit trail. Record every change the agent makes, with the identity that made it.
- Human review for destructive actions. Deletes, scale-downs, and secret changes need a person to approve.
- Keep secrets out of the chat. A value pasted into a conversation can be stored, logged, or repeated.
How Control Plane applies them
| Principle | Control Plane behavior |
|---|---|
| Scoped access | OAuth sign-in with per-org consent on every path. You scope each AI tool to the orgs it needs. |
| Read-only first | The readonly toolset profile advertises no tool that creates, updates, deletes, or executes anything. An assistant on it cannot change a live resource. |
| No privilege creep | The profile is fixed for the life of a connection. An agent cannot expand it mid-session. |
| Human review | Before a deletion, the assistant states what it removes and what breaks, then waits for your explicit yes. |
| Audit trail | Every change is recorded in the audit trail. |
| Secrets | Job tools create or rotate secrets without the value passing through the chat. |
A practical rollout:
- Connect the assistant to a test org first.
- Begin on the readonly profile and ask it to describe what is running.
- Reconnect on core, the profile the directory listings use, when you want it to deploy.
- Keep production in a separate org, and grant it only when needed.
- Review the audit trail after the first few sessions.
For a deeper look at risk and controls, see how to run AI agents on production infrastructure safely.
Where can I find a cloud friendly deployment platform for AI models?
The flow in this guide also deploys inference services. A model server is a workload like any other. Workloads can attach NVIDIA GPUs, and the pricing page lists models from T4 to B200. You describe it to the assistant, the platform builds or pulls the image, and the assistant deploys it and checks its status.
On Control Plane, a workload deployed to a Control Plane Global Virtual Cloud (GVC) runs active-active in every location of that GVC. A global endpoint routes each request to the nearest healthy location. That gives an inference service failover without paying for idle standby capacity.
For compute options and what to look for in infrastructure that serves models, read what AI-ready infrastructure is.
Which cloud platforms can AI assistants deploy to?
Several platforms and clouds publish MCP servers or AI integrations. The table summarizes what each vendor’s own documentation says. Capabilities change, so check each page before you decide.
| Platform | What its docs describe | Source |
|---|---|---|
| Heroku | A remote MCP server at https://mcp.heroku.com/mcp that uses OAuth 2.0. A separate local STDIO server runs through the Heroku CLI. |
Heroku Dev Center |
| Render | A hosted MCP server at https://mcp.render.com/mcp. It can create web services, static sites, cron jobs, Render Postgres, and Render Key Value. Render states other service types are not yet supported and that it does not support modifying scaling settings. |
Render docs |
| Railway | An MCP server at mcp.railway.com that lets assistants create projects, deploy templates, manage environments, pull variables, and redeploy services. Connects through the Railway CLI or OAuth. |
Railway docs |
| Fly.io | A flyctl MCP server, labeled experimental in its CLI reference, that supports most flyctl commands for apps, certificates, logs, machines, orgs, secrets, status, and volumes. |
Fly.io docs |
| Vercel | A remote MCP server at https://mcp.vercel.com with OAuth, for managing projects and deployments and analyzing logs. Vercel notes that connecting grants the AI system the same access as your Vercel user account. |
Vercel docs |
| AWS | A managed AWS MCP Server with OAuth or SigV4 sign-in, IAM based access controls, and CloudTrail logging of API calls. | AWS docs |
Control Plane’s strengths for this workflow:
- It is listed in both Claude’s connector directory and ChatGPT’s plugin directory.
- Its job tools do a whole task in one call.
- Per-org OAuth consent, a readonly toolset profile, and a profile an agent cannot expand mid-session keep the assistant inside the access you grant.
- Deployed apps run active-active across every location in their GVC, which can span regions and clouds.
On that last point, Control Plane runs serverless, standard, stateful, cron, and VM workloads on AWS, GCP, and Azure, on clusters you already run through Control Plane’s Bring Your Own Kubernetes (BYOK), on your own machines (bare metal or VMs) turned into managed locations with Control Plane’s Managed Kubernetes (MK8s), and on premises. On Control Plane-provided compute, there is no Kubernetes cluster you have to operate. Many teams run it in a single cloud for its day-two operations, including autoscaling, built-in observability, security, and cost control.
The 99.999% availability SLA applies to workloads running at least two replicas in at least two locations. Those locations can be two regions of one cloud or locations on different providers.
For a wider comparison of tools in this space, see the best MCP servers for DevOps.
What happens to credentials and cloud access after the app is live?
An app deployed by an assistant still needs to call cloud services. Control Plane’s Universal Cloud Identity gives workloads short-lived, least-privilege access to 600+ AWS, GCP, and Azure services, so the code an assistant writes needs no long-lived cloud keys.
Control Plane is PCI DSS Level 1, SOC 2 Type II, HIPAA, and GDPR compliant. Control Plane’s Capacity AI right-sizes CPU and memory between a minimum and maximum you set, based on historical usage. On Control Plane-provided compute, you pay for reserved CPU and memory by the millicore and the megabyte, with no contracts or minimums.
FAQ
How do I let Claude or ChatGPT deploy my application?
Connect the assistant to a deployment platform through MCP, authorize the orgs it may use, and ask it to deploy. For Control Plane, add the connector from Claude’s directory or install the plugin from the ChatGPT Plugin directory.
Can ChatGPT deploy my app?
Yes, when ChatGPT is connected to a platform that deploys apps. Control Plane is a listed plugin in the ChatGPT Plugin directory. The same listing reaches ChatGPT on the web, desktop, and mobile, and Codex.
Can Claude deploy my app to production?
Yes, through a connected platform. Claude writes code and calls the platform’s tools. Limit the orgs you authorize, start on a read-only profile if you want, and review the audit trail after each change.
Which cloud platforms can AI assistants deploy to?
Several. Heroku, Render, Railway, Fly.io, Vercel, AWS, and Control Plane each publish an MCP server or AI integration. Their scopes differ, so read each vendor’s documentation for what the assistant can create and change.
How do I give an AI agent safe access to my cloud infrastructure?
Apply least privilege, scoped tokens, read-only access first, an audit trail, and human review for destructive actions. On Control Plane, that means per-org OAuth consent, a readonly toolset profile, a profile that stays fixed for the life of a connection, and an audit trail of every change.
Where can I find a cloud friendly deployment platform for AI models?
Look for a platform that deploys inference services as workloads, exposes tools to your assistant, and runs across locations for failover. Control Plane deploys inference services as workloads through the same MCP flow. See the guide on AI-ready infrastructure.
What platform as a service solutions are available for building and deploying new applications?
Options include Control Plane, Heroku, Render, Railway, Fly.io, and Vercel. They differ in supported runtimes, regions, pricing, and how much they expose to AI assistants. Control Plane runs serverless, standard, stateful, cron, and VM workloads active-active across regions and clouds, and its MCP server is listed in both Claude and ChatGPT.
Can an AI assistant change my infrastructure without asking me?
Only within the access you grant. On Control Plane’s readonly profile, the assistant sees no tool that creates, updates, deletes, or executes anything. On other profiles, the assistant asks for your explicit yes before a deletion, and every change is recorded in the audit trail.
Does the Control Plane MCP server read files on my computer?
No. The MCP server cannot read your local filesystem. To build a local folder, use the CLI (cpln image build --remote) from an agent that has a terminal, or deploy from a repository or an existing image.
Can the assistant handle secrets and databases?
Yes. Databases and caches come from the Template Catalog. Secrets can be created or rotated without the value passing through the chat.
Get started
Connect Control Plane to Claude or ChatGPT, authorize a test org, and ask for a small app. Then grant a production org when you trust the setup.
Originally published on the Control Plane blog.
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