Your AI agent just learned
infrastructure.

An MCP server that turns any AI coding agent into an autonomous deployment engineer — across AWS, GCP, and Azure.

No CLI to learn. No workflow to change. Just plug in and deploy.

Request Early Access

Currently onboarding design partners · No sales call required

See it in action

One prompt. Two clouds. Done.

Your AI agent handles the conversation. Deployant handles the infrastructure.

AI Agent — deployant MCP

Analyze → Recommend → Estimate → Generate → Deploy — all handled by the MCP server under the hood.

How it works

Three steps. Zero context-switching.

Connect

Add Deployant as an MCP server to your agent — Claude Code, Cursor, Windsurf, or any MCP-compatible client.

Describe

Tell your agent what you want deployed, in plain language. "Deploy my app to AWS" is enough.

Confirm

Review the plan, cost estimate, and generated IaC. Then ship — across any combination of clouds.

AnalyzeScans your codebase to detect frameworks, databases, storage, and dependencies.

RecommendMaps your stack to the right managed services on each cloud provider.

EstimateCalculates monthly cost projections based on your usage patterns.

GenerateProduces production-ready Terraform with best practices baked in.

DeployProvisions infrastructure and ships your app with zero manual configuration.

Your agent just knows infra now.

From first deploy to ongoing operations — all through the same conversational interface.

Day 1

Deploy your project

Describe what you need, get a cost estimate, review the generated Terraform, and ship to production. Multi-cloud from the start.

Day 2+

Operate and optimize

Handle drift detection, auto-scaling adjustments, and cost optimization — all through the same agent conversation. No context-switching.

Gets sharper over time.

Because Deployant runs as an MCP server, it maintains persistent context about your infrastructure across every conversation with your agent. It doesn't re-learn your setup each time — it builds on what it already knows.