# Deployment.io

> Deployment.io turns an outcome you describe into shipped software — its AI agents write the code, test it, and deploy it to your own cloud.

Deployment.io turns an outcome into a deployment. You describe what you want shipped; our AI agents plan the work, write the code across your repos, test every change before you review it, and ship it to your cloud. You approve what reaches production. Prefer to drive it yourself? Point your own coding agent at Deployment.io over MCP. Either way, everything runs in your own cloud and nothing reaches production without your sign-off.

## Two Ways to Use Deployment.io

- **Self-serve**: Describe an outcome and our agents ship it, or connect your own coding agent (Claude Code, Cursor, Windsurf, Copilot, OpenAI Codex, Gemini CLI, and other SKILL.md-compliant agents) over MCP. You operate the platform. Metered by jobs.
- **Managed**: Our team operates Deployment.io for you — done-for-you engineering that turns a backlog of outcomes into deployments on your cloud. Custom annual retainer.

## What Makes Deployment.io Different

- **More than code generation**: Not just code generation. Our planning agent reads your repos and architecture, writes a spec, and decomposes an outcome into ordered work across the right repos. Agents then build, verify, and ship.
- **Verified before it ships**: The agent runs your build and tests on every change and only marks a Task done when they pass — nothing reaches production unvalidated.
- **Your cloud, your data**: Code, secrets, build artifacts, and runtime traffic never leave your own cloud account. We connect via a scoped role you control — revoke it and we lose access instantly.
- **Open-source runner**: The runner that touches your infrastructure is open source. Audit it, fork it, run your own build.
- **Open-source skill**: A SKILL.md-compliant skill (MIT, github.com/deployment-io/skills) teaches your agent the deploy playbook. Pairs with the MCP server.
- **Approval gates by default**: Production deploys pause for human sign-off — from Slack or the dashboard.
- **RBAC for humans and agents**: Scope what each teammate — and each MCP token — can deploy, read, or approve.

## How It Works

1. **Describe an outcome** — a feature, a migration, a fix.
2. **The planning agent plans it** — reads your repos and architecture, writes a spec, and decomposes it into ordered work across the right repos.
3. **Agents build it** in sandboxed environments with scoped, least-privilege access to your cloud.
4. **Each change is verified against your build and tests** before it ships.
5. **You approve production** — deploys to your cloud as static sites, web services, or full environments. Nothing ships without your sign-off.

Driving it yourself with your own agent over MCP? The open-source skill teaches the deploy playbook; native MCP tools give the agent the actions to execute.

## What Deployment.io Can Ship

1. **Static Sites**: React, Next.js, Vue, Astro, and other static frameworks — deployed to S3 + CloudFront with HTTPS, cache invalidation, and rollbacks.
2. **Web Services**: Go, Node.js, Python, Ruby, and Rust services — deployed to ECS with auto-scaling, health checks, and zero-downtime rollouts.
3. **Multiple Environments**: Dev, staging, and production — each in its own AWS account if you want. Switch between them with a single MCP call.

## Platform Features

- AI planning + multi-repo Tasks: describe an outcome, get a spec and ordered work across repos
- Build/test self-verification on every change before anything ships
- Native MCP tool surface (`deploy_static_site`, `deploy_web_service`, `get_deployment_logs`, `get_approval_status`, `list_environments`, `create_environment`, `edit_environment`, `list_runners`, `get_job_status`)
- Zero-config auto-detection: Dockerfile, exposed port, health-check path, build command, and output directory read straight from the repo
- Human approval gates with Slack + dashboard sign-off
- Build & runtime logs with regex search and time-range filtering from CloudWatch
- RBAC for humans and MCP tokens
- Auto-deploy on git push (wire a branch to an environment once)
- Custom domains, HTTPS, Slack alerts
- Encrypted secrets per environment (env vars + secret files, scoped per environment)

## MCP Tools

Coding agents interact with Deployment.io through these MCP tools:

- `deploy_static_site` — Deploy static sites (React, Next.js, Vue, Astro, Vite) to S3/CloudFront. Auto-detects build command and output directory.
- `deploy_web_service` — Deploy Dockerized backend services to ECS. Auto-detects port from Dockerfile EXPOSE and health-check path.
- `list_environments` — List all environments with IDs, names, regions, and dashboard links.
- `create_environment` — Create a new environment (Production / Staging / Development).
- `edit_environment` — Rename an existing environment.
- `list_runners` — List available infrastructure runners (shared cloud or custom AWS).
- `get_deployment_logs` — Fetch logs with time-range filtering and regex search. Up to 500 lines per request.
- `get_job_status` — Check the status of an async deployment job.
- `get_approval_status` — Check the status of a pending production-deploy approval.

## The Deployment.io Skill

A SKILL.md-compliant bundle that teaches your coding agent how to deploy on Deployment.io. With the skill, the runbook (framework→publish_directory mapping, is_spa detection, port discovery, health-check routes, git pre-flight, approval flow) loads automatically.

- Source: github.com/deployment-io/skills (MIT, semver)
- Works with: Claude Code, Claude Desktop, Cursor, OpenAI Codex CLI, Gemini CLI, and 25+ other SKILL.md-compliant agents
- Install: clone the repo and copy `skills/deployment-io/` into your agent's skills directory

## Documentation Structure

- `/docs/introduction/` — What Deployment.io is and why it exists
- `/docs/introduction/glossary/` — Definitions of core terms (outcome, Task, Step, job, runner, environment, MCP server, skill)
- `/docs/coding-agents/` — MCP configuration, available tools, and the Deployment.io skill
- `/docs/runner-installation/` — Setting up the runner on AWS
- `/docs/infrastructure/` — AWS infrastructure configuration
- `/docs/dashboard/` — Using the dashboard
- `/docs/role-based-access-controls/` — Team permissions and RBAC
- `/docs/guides/` — Step-by-step tutorials and how-to guides
- `/blog/` — Articles on AI-driven deployments, platform updates, and guides

## Key URLs

- Website: https://deployment.io
- Managed service: https://deployment.io/managed/
- Documentation: https://deployment.io/docs/
- Pricing: https://deployment.io/pricing/
- Security: https://deployment.io/security/
- Blog: https://deployment.io/blog/
- Sign Up: https://app.deployment.io/signup
- Dashboard: https://app.deployment.io
- Skill repo: https://github.com/deployment-io/skills

## Comparisons

How Deployment.io compares to deployment platforms — it adds AI planning, multi-repo Tasks, build/test self-verification, and deploys into your own cloud:

- Deployment.io vs. Vercel: https://deployment.io/blog/deployment-io-vs-vercel-4-reasons-why-you-should-consider-deployment-io/
- Deployment.io vs. Netlify: https://deployment.io/blog/deployment-io-vs-netlify-5-compelling-reasons-to-switch-to-deployment-io/
- Deployment.io vs. Render: https://deployment.io/blog/deployment-io-vs-render-4-reasons-to-switch-from-render/
- One MCP call vs. fifty (why an AI agent needs a platform, not a toolchain): https://deployment.io/blog/one-mcp-call-vs-fifty-platform-abstraction-for-ai-agents/

## Pricing

Self-serve is metered by jobs; Managed is a custom retainer.

- **Free**: 10 jobs/month, 1 user — includes Tasks and planning
- **Team**: from $99/month (200 jobs); $499/month (1,000 jobs)
- **Managed**: custom — our team operates Deployment.io for you, turning outcomes into deployments on your cloud
- **First Outcome Sprint**: $7,500 fixed — one outcome planned, built, verified, and shipped to your cloud in two weeks, with half held back until it's live and verified. The entry point into Managed.

A "job" is any AI, CI/CD, or infrastructure task that runs on your cloud or ours — build creation, preview generation, deployment, custom-domain setup, response-header configuration, or AI-driven log debugging.

## Security

- Runs in your own cloud account — code, secrets, and traffic never leave it
- Scoped role-based access; revoke to cut off access instantly
- Encrypted env vars and secret files at rest
- Open-source runner you can audit or fork
- Sandboxed agent execution with least-privilege access
- Role-based access control (RBAC) for humans and MCP tokens
- Production deploys require human approval by default
- Audit logs for all deployment activity

## Contact

- Email: hello@deployment.io
- Support: Priority email support for Team and Managed plans

## Integration

Deployment.io complements existing CI/CD pipelines (GitHub Actions, Jenkins, etc.) and integrates natively with AWS — one-click environments and multi-account support.