mcp.run vs Beam
Detailed side-by-side comparison to help you choose the right tool
mcp.run
🔴DeveloperAI Infrastructure
Serverless platform for running and composing MCP servers (called 'servlets') in a portable WebAssembly sandbox, with a marketplace for installing tools into any MCP client.
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$0Beam
🔴DeveloperAI Infrastructure
Beam is a developer-first serverless platform purpose-built for AI workloads. The pitch is direct: import a Python function, decorate it, push to Beam, and it runs on a GPU somewhere with the right model weights cached, scales to thousands of concurrent invocations, and shrinks back to zero when traffic stops — with cold starts measured in single-digit seconds rather than the minutes most generic serverless platforms take to load model weights. The team built the platform from the ground up for
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mcp.run - Pros & Cons
Pros
- ✓Wasm sandbox is a genuine supply-chain security win over npm-installed MCP servers
- ✓Language-agnostic — author once, run everywhere
- ✓Capability manifest gives you per-tool least-privilege
- ✓Works with every major MCP client via a small local proxy
- ✓Dylibso's Extism heritage means the Wasm tooling is mature
Cons
- ✗Wasm component model still requires a build step authors are learning
- ✗Smaller catalog than Smithery for popular off-the-shelf servers
- ✗Pricing model is still evolving
- ✗Local proxy adds a (small) install step versus pure stdio servers
Beam - Pros & Cons
Pros
- ✓No billing during cold-start / container spin-up — only your code runs are charged
- ✓Storage is free — caching model weights does not add to the bill
- ✓$30 free signup credit makes serious evaluation possible without a card
- ✓Sandboxes give agents a safe place to execute their own generated code
- ✓Python ergonomics — no Dockerfiles or Kubernetes required for the happy path
Cons
- ✗Smaller community and integration ecosystem than Modal
- ✗Region availability is more limited than hyperscaler GPU offerings
- ✗Pro tier per-seat charge ($25) plus usage may add up for larger teams
- ✗Latency-sensitive workloads may still need always-on workers, costing more
- ✗Less mature enterprise governance (RBAC, audit logs) than legacy hyperscalers
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