Fast LiteLLM vs building your own Rust extension
You can write your own PyO3 extension to accelerate LiteLLM's hot paths, but you then own the fallback logic, circuit breaking, feature flags, wheel-building across platforms, and the ongoing benchmarking. Fast LiteLLM packages all of that as MIT-licensed, drop-in code with published benchmarks — you can also read or fork it.
Verdict: Adopt Fast LiteLLM to skip months of extension plumbing and get published benchmarks and safety rails for free. Roll your own only if you have unusual constraints and the appetite to maintain a cross-platform native extension.
Fast LiteLLM strengths
- ✓Fallback, circuit breaker, and feature flags already implemented
- ✓Prebuilt wheels across Linux/macOS/Windows and Python 3.8–3.13
- ✓Published, reproducible benchmarks — including the losses
- ✓MIT-licensed: read it, fork it, or contribute
building your own Rust extension strengths
- ✓Total control over exactly which paths you accelerate
- ✓No third-party dependency in your supply chain
- ✓Can target internal, non-LiteLLM code paths too
Feature comparison
| Aspect | Fast LiteLLM | building your own Rust extension |
|---|---|---|
| Time to adopt | Minutes | Weeks–months |
| Cross-platform wheels | Provided | You build them |
| Fallback & circuit breaker | Included | You write it |
| Benchmarks | Published | You run them |
| Maintenance burden | On Neul Labs | On you |
| License | MIT | Yours |
Choose Fast LiteLLM when
- →You want acceleration now, not a native-extension project
- →You value the safety rails and cross-platform wheels
- →LiteLLM's hot paths are your actual bottleneck
Choose building your own Rust extension when
- →You have hot paths outside LiteLLM to accelerate as well
- →You must avoid all third-party runtime dependencies
- →You have in-house Rust + packaging expertise to spare
FAQ
Can I read the source before adopting?+
Yes. Fast LiteLLM is MIT-licensed and developed in the open on GitHub. You can audit the Rust and Python code, and fork it if you need something bespoke.
What do I actually save by not rolling my own?+
The unglamorous parts: cross-platform wheel builds, PyO3 glue, automatic fallback, a circuit breaker, feature-flag/canary machinery, and a reproducible benchmark harness.
Try it for yourself
Open source, MIT, one import line. Reproduce the benchmarks on your own workload.