Deploy Model Context Protocol (MCP) servers with ease, without having to manage infrastructure. Integrate MCPs in your agentic workflow or directly in your MCP client.
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Hi PH folks,
Me and @valentin_stoican are the makers of MCP-Cloud and we host Model Context Protocol (MCP) servers so you can add tool-calling abilities to LLM agents without touching Docker, Kubernetes, or cloud credentials.
Why we built it?
The MCP spec (open-sourced by Anthropic) standardises how an AI agent calls external tools, but you still have to run an MCP server for every tool you want. That means containers, TLS, auth, scaling, etc.—work most AI/automation devs don’t want to handle.
What it does? - 50+ ready-made templates (GitHub, Slack, Postgres, Notion, etc.)
- < 60 s deploy – click Deploy, get a live HTTPS endpoint
- JWT-secured + SSE streaming out of the box - Copy the code snippet into Claude Desktop, n8n, Cursor, VS Code, Windsurf or any client that speaks HTTP + SSE
Does this remove enough friction to make MCP useful in your projects? Which templates/workflows should we add next? Thoughts on the pricing model (usage-based after the free tier)? We’d love feedback—from “this solved my problem” to “here’s why I wouldn’t use it.” Thanks for reading and for any comments!
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MCP Cloud simplifying model deployment? ☁️🤖 The "Model Context Protocol" servers suggest:
- Standardized containerization for ML models
- Auto-scaling inference endpoints
- Unified monitoring dashboard
Potential to become the "Vercel for AI models" if it supports major frameworks (PyTorch/TensorFlow).
Hi PH folks,
Me and @valentin_stoican are the makers of MCP-Cloud and we host Model Context Protocol (MCP) servers so you can add tool-calling abilities to LLM agents without touching Docker, Kubernetes, or cloud credentials.
Why we built it?
The MCP spec (open-sourced by Anthropic) standardises how an AI agent calls external tools, but you still have to run an MCP server for every tool you want. That means containers, TLS, auth, scaling, etc.—work most AI/automation devs don’t want to handle.
What it does?
- 50+ ready-made templates (GitHub, Slack, Postgres, Notion, etc.)
- < 60 s deploy – click Deploy, get a live HTTPS endpoint
- JWT-secured + SSE streaming out of the box
- Copy the code snippet into Claude Desktop, n8n, Cursor, VS Code, Windsurf or any client that speaks HTTP + SSE
Does this remove enough friction to make MCP useful in your projects?
Which templates/workflows should we add next?
Thoughts on the pricing model (usage-based after the free tier)?
We’d love feedback—from “this solved my problem” to “here’s why I wouldn’t use it.” Thanks for reading and for any comments!
MCP Cloud simplifying model deployment? ☁️🤖 The "Model Context Protocol" servers suggest:
- Standardized containerization for ML models
- Auto-scaling inference endpoints
- Unified monitoring dashboard
Potential to become the "Vercel for AI models" if it supports major frameworks (PyTorch/TensorFlow).