Fast LiteLLM vs stock LiteLLM
Fast LiteLLM is not an alternative to LiteLLM — it is an add-on that keeps stock LiteLLM as the source of truth and swaps only a few hot-path components for Rust. Same API, same providers, faster connection pooling (3.2×), rate limiting (1.6×), and large-text tokenization (1.5–1.7×), with 42× less memory on high-cardinality rate limits and automatic Python fallback.
Verdict: Run stock LiteLLM plus Fast LiteLLM if connection pooling, rate limiting, large-text tokenization, or high-cardinality memory is your bottleneck. If none of those are hot for you, plain LiteLLM is all you need.
Fast LiteLLM strengths
- ✓3.2× faster connection pooling under concurrent load
- ✓1.6× faster rate limiting; 42× less memory at high key cardinality
- ✓1.5–1.7× faster large-text / batch tokenization
- ✓Automatic Python fallback and a per-component circuit breaker
- ✓One import line — no other code changes
stock LiteLLM strengths
- ✓Zero extra dependency to install or reason about
- ✓Fastest path for small-text tokenization and routing (no FFI overhead)
- ✓The canonical, upstream-maintained implementation
- ✓Every LiteLLM feature works identically (it still runs the show)
Feature comparison
| Aspect | Fast LiteLLM | stock LiteLLM |
|---|---|---|
| LiteLLM public API | Unchanged | Native |
| Provider support | Same as LiteLLM | 100+ providers |
| Connection pooling | Rust, 3.2× faster | Python + locks |
| Rate limiting | Rust, 1.6× faster | Python |
| High-cardinality memory | 42× less | Baseline |
| Large-text tokenization | 1.5–1.7× faster | Python |
| Small-text tokenization | Uses Python (auto) | Python (faster) |
| Install footprint | +1 wheel | None extra |
| Failure behaviour | Falls back to Python | n/a |
Choose Fast LiteLLM when
- →Your proxy bottlenecks on connection pooling under concurrency
- →You track thousands of unique API keys and memory is climbing
- →You tokenize long documents on the request path
- →You want the speedup without changing application code
Choose stock LiteLLM when
- →Your workload is dominated by short-text tokenization or routing
- →You want the absolute minimum dependency surface
- →You are not currently CPU- or memory-bound on the hot path
FAQ
Does Fast LiteLLM change any LiteLLM behaviour?+
No. It swaps hot-path implementations only. Cost tracking, provider integrations, the router, callbacks, and the proxy server are untouched, and the public API is identical.
What if the Rust path fails?+
It falls back to the original Python implementation on the same call, and a circuit breaker disables the component after 10 errors — so you never do worse than stock LiteLLM.
Try it for yourself
Open source, MIT, one import line. Reproduce the benchmarks on your own workload.