FastMCP is a popular Python framework for building MCP servers. Once the server works locally, the next choice is where to run it. Prefect's Horizon is one managed path, but teams also evaluate specialist MCP platforms and general web hosting. The right choice depends on your runtime, authorization needs, operations capacity, and the clients you need to support.
First, separate framework from hosting
The open source FastMCP framework helps you build a server. Prefect's Horizon is a managed deployment product from the FastMCP team; its documentation describes hosting, authentication, access control, and observability. You can use FastMCP with other hosting choices as well. Do not evaluate “FastMCP versus hosting” as if they were mutually exclusive.
Four deployment paths to compare
| Path | Why it may fit | What to verify |
|---|---|---|
| Prefect Horizon | Managed path aligned with FastMCP projects | Supported runtime, auth model, operational controls, and current terms |
| Manufact Cloud | MCP-focused workflow for teams building and deploying servers and apps | Framework fit, client testing, deployment controls, and current terms |
| Cloudflare Workers | Programmable edge runtime with an official remote MCP guide | State requirements, auth design, runtime limits, and team ownership |
| Render web service | General application hosting with official MCP server examples | Instance behavior, health checks, auth implementation, and current terms |
Cloudflare's remote MCP guide describes Streamable HTTP deployment and authenticated and unauthenticated routes. Render's MCP hosting guide covers Python and TypeScript examples. A general host gives you flexibility, but your team owns more of the MCP-specific integration and verification work.
Use one evaluation checklist
Run the same server and test cases on each candidate. That removes marketing language from the decision. Ask:
- Runtime: Does it deploy your actual Python or TypeScript project without a rewrite?
- Transport: Does the remote endpoint work with the client and transport you plan to support?
- Authorization: Can you implement user identity, tenant isolation, scopes, and token verification correctly?
- Operations: Can you inspect failures, roll back a bad deployment, and keep secrets out of logs?
- Testing: Can you connect the preview server to the intended AI clients and reproduce tool, auth, and UI issues?
- Cost: What is the current price for your expected traffic, environments, and support needs?
Vendor features and pricing change. Check the live product documentation and quote before a purchase; a blog table is not a reliable price contract.
Run a small proof of concept
- Deploy a server with one read tool and one protected write tool.
- Use the MCP Inspector to test discovery, valid and invalid calls, and errors.
- Connect the same endpoint to your target AI clients. Test real prompts and record the tool call selected.
- Try a second tenant and an insufficiently scoped token. Access should fail on the server.
- Deploy an update, review logs, and rehearse rollback.
If your team already uses FastMCP and wants a managed experience close to that framework, Horizon is a sensible product to evaluate. If you need a broader MCP product workflow, evaluate Manufact Cloud. If you prefer to assemble the stack around your own infrastructure, Cloudflare or Render are viable candidates. The proof of concept should decide the winner for your workload.
