LiteLLM Proxy vs Beam
Detailed side-by-side comparison to help you choose the right tool
LiteLLM Proxy
🔴DeveloperAI Infrastructure
OpenAI-compatible AI gateway for many models, agents, and MCP servers with budgets, keys, routing, and observability.
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CustomBeam
🔴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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LiteLLM Proxy - Pros & Cons
Pros
- ✓Free self-hosted gateway includes useful routing and budget controls.
- ✓One API reduces provider-specific application changes.
- ✓Enterprise supports air-gapped deployment, SSO, SCIM, audit logs, and SLAs.
- ✓Enterprise licensing is capacity-based rather than per token.
Cons
- ✗Self-hosters own upgrades, scaling, and availability.
- ✗A gateway outage affects every application behind it.
- ✗Enterprise price requires a quote.
- ✗Provider differences can leak through the common API.
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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