Fast LiteLLM vs the LiteLLM Rust sidecar
Both use Rust to speed up LiteLLM but take different shapes. LiteLLM ships its own Rust sidecar as a built-in option. Fast LiteLLM is an independent, in-process accelerator you install from PyPI and activate with a single import line — no separate process to run. We publish reproducible benchmarks for our own components but do not publish head-to-head numbers against the sidecar; both are legitimate choices.
Verdict: Choose Fast LiteLLM if you want in-process acceleration with a one-line install and no extra process to operate. Consider the built-in sidecar if you prefer an upstream-shipped option. We do not claim to beat the sidecar — we have not benchmarked against it.
Fast LiteLLM strengths
- ✓In-process — no separate service to deploy or supervise
- ✓Activated with a single import; nothing else to run
- ✓Reproducible, published benchmarks for each component
- ✓Automatic Python fallback and circuit breaker built in
the LiteLLM Rust sidecar strengths
- ✓Shipped and maintained as part of upstream LiteLLM
- ✓A first-party option some teams will prefer for support reasons
- ✓No third-party dependency beyond LiteLLM itself
Feature comparison
| Aspect | Fast LiteLLM | the LiteLLM Rust sidecar |
|---|---|---|
| Deployment shape | In-process import | Sidecar process |
| Activation | One import line | Config / run sidecar |
| Maintainer | Neul Labs (independent) | Upstream LiteLLM |
| Published benchmarks | Yes, reproducible | See upstream |
| Head-to-head numbers | Not published | Not published |
| Fallback to Python | Automatic | See upstream |
Choose Fast LiteLLM when
- →You want acceleration without operating an extra process
- →You value transparent, reproducible per-component benchmarks
- →You want a one-line, drop-in adoption path
Choose the LiteLLM Rust sidecar when
- →You prefer an upstream-first-party component
- →You want to minimise third-party dependencies
- →Your ops model already favours sidecar processes
FAQ
Is Fast LiteLLM faster than the sidecar?+
We do not know and we do not claim it is. We publish honest benchmarks for our own components versus production Python, but we have not run a reproducible head-to-head against the sidecar, so we make no such claim.
Can I run both?+
They address overlapping hot paths, so running both is not recommended. Pick the shape that fits your operational model — in-process import or sidecar process.
Try it for yourself
Open source, MIT, one import line. Reproduce the benchmarks on your own workload.