Orchestrate Enterprise MCP Servers
Archestra.AI is a centralized MCP orchestrator that provides security, observability, and cost controls for deploying MCP servers organization-wide with
Why it matters
Centralize the management of your MCP servers with robust orchestration, security, and cost optimization features. Ensure secure and efficient operation of your AI infrastructure.
Outcomes
What it gets done
Orchestrate MCP servers on Kubernetes.
Manage private MCP registry with team access.
Implement enterprise-wide prompt registry and security agents.
Monitor and optimize AI infrastructure costs.
Install
Add it to your toolbox
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/vb-archestra-ai | bash Overview
Archestra.AI MCP Server
What it does
Archestra.AI is an MCP-native secure AI platform that centralizes MCP server orchestration, moving them from individual machines to a managed environment. It provides teams with observability, cost monitoring and limits, security guardrails to mitigate data exfiltration risks, and a private MCP registry for sharing self-hosted, remote, self-built, and third-party MCP servers.
How it connects
Use Archestra when you need to deploy MCP servers organization-wide with security controls, mitigate data exfiltration risks, manage AI costs across teams, or provide a ChatGPT-like interface with company-specific prompts and tools. It's suitable for running agents on schedules or webhooks, managing credentials and API keys for multiple MCP servers, and bringing MCP adoption to technical and non-technical users. If you're running a single-user MCP setup with no security or cost concerns, or if you need a lightweight solution without orchestration overhead, a standalone MCP server may suffice.
Source README
MCP-native Secure AI Platform
Simplify AI usage in your company, providing user-friendly MCP toolbox, observability and control built on a strong security foundation.
For Platform teams:
- Mitigate MCP chaos, move MCP servers from individual machines to a centralized orchestrator
- Manage how MCPs access data and use credentials
- Mitigate data exfiltration risks
- Manage AI costs
- AI Observability
For Developers:
- Deploy your MCP servers org-wide
- Build and deploy agents without worrying about security
For Management:
- Bring 1-click MCP adoption to the whole organization for technical and non-technical users
- Reduce AI costs up to 96%
- Get full visibility on AI adoption, usage and data access
🚀 Quickstart with Docker
docker pull archestra/platform:latest;
docker run -p 127.0.0.1:9000:9000 -p 127.0.0.1:3000:3000 \
-e ARCHESTRA_QUICKSTART=true \
-v /var/run/docker.sock:/var/run/docker.sock \
-v archestra-postgres-data:/var/lib/postgresql/data \
-v archestra-app-data:/app/data \
archestra/platform;
🧳 Migrating an existing AI pilot
Already have a Claude Code-style project, MCP config, local tools, hooks, or another hand-rolled
agentic PoC? Use the migration kit to turn it into an Archestra pilot.
👩💻 ChatGPT-like chat with MCPs
🎁 with private company-wide prompt registry!
⏰ Agent triggers
Run agents on a schedule or invoke them over HTTP with the A2A protocol.
📋 Private MCP registry with governance
Add MCPs to your private registry to share them with your team: self-hosted and remote, self-built and third-party.
Learn more about Private MCP Registry →
☁️ Kubernetes-native MCP orchestrator
Run MCP servers in Kubernetes, managing their state, API keys, OAuth.
Learn more about MCP Orchestrator →
📚 RAG Knowledge Base
Built-in retrieval-augmented Knowledge Base - no external vector database or separate retrieval service required.
Learn more about Knowledge Base →
🤖 Security sub-agents
Isolating dangerous tool responses from the main agent to prevent prompt injections.
🚫 Non-probabilistic security to prevent data exfiltration
Models could consume prompt injections via MCP uncontrollably (read your inbox, read your GitHub issues, read your customers' inquiries) and follow them resulting in data exfiltration.
Learn more about Tool Guardrails → | The Lethal Trifecta →
Live demo of Archestra security engine preventing data leak from the private GitHub repo to the public repo:
Read more: Simon Willison, The Economist
Examples of hacks:
ChatGPT (April 2023), ChatGPT Plugins (May 2023), Google Bard (November 2023), Writer.com (December 2023), Amazon Q (January 2024), Google NotebookLM (April 2024), GitHub Copilot Chat (June 2024), Google AI Studio (August 2024), Microsoft Copilot (August 2024), Slack (August 2024), Mistral Le Chat (October 2024), xAI's Grok (December 2024), Anthropic's Claude iOS app (December 2024), ChatGPT Operator (February 2025), Microsoft 365 Copilot (EchoLeak) (June 2025), Notion 3.0 (September 2025), Salesforce Agentforce (ForcedLeak) (September 2025), Microsoft Copilot Cowork (May 2026).
💰 Cost monitoring, limits and dynamic optimization
Per-team, per-agent or per-org cost monitoring and limits. Dynamic optimizer allows to reduce cost up to 96% by simply switching to cheaper models automatically for simpler tasks.
Learn more about Costs & Limits →
📊 Observability
Metrics, traces and logs allowing to come to a conclusion about per-org, per-agent and per-team token and tool usage, and performance.
Learn more about Observability →
👍 Ready for production
- ✅ Lightning fast, 31ms at 95p: Performance & Latency benchmarks →
- ✅ Terraform provider →
- ✅ Helm Chart →
🤝 Contributing
We welcome contributions from the community!
Thank you for contributing and continuously making Archestra better, you're awesome 🫶
Discussion
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