Rust acceleration for LiteLLM.
Zero code changes.
Fast LiteLLM is a drop-in acceleration layer for the LiteLLM Python library —
3.2× faster connection pooling,
1.6× faster rate limiting,
42× less memory on high-cardinality workloads.
Just import fast_litellm first.
# Using uv (recommended)
uv add fast-litellm
# Or using pip
pip install fast-litellmimport fast_litellm # accelerates LiteLLM
import litellm
response = litellm.completion(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Hi"}],
)Honest benchmarks
Production-grade Python (with thread safety) vs Rust acceleration.
| Component | Speedup | Memory | Best for |
|---|---|---|---|
| Connection Pool | 3.2× faster | Same | HTTP connection management |
| Rate Limiting | 1.6× faster | Same | Request throttling, quota management |
| Large Text Tokenization | 1.5–1.7× faster | Same | Long documents |
| High-Cardinality Rate Limits | 1.2× faster | 42× less memory | Many unique API keys/users |
⚠️ Rust is slower than Python on small-text tokenization and routing — FFI overhead dominates. We document this honestly on the benchmarks page.
Performance you can ship
Drop-in acceleration where it matters: connection pooling, rate limiting, token counting at scale. Production-safe with feature flags and automatic fallback.
Read about the architecture →Deep LiteLLM expertise
Guides, articles, and technical analyses of long-standing LiteLLM issues — root causes, real fixes, broader lessons. Independent, no vendor agenda.
Browse the knowledge base →Consulting from Neul Labs
Half-day audits, perf tuning, multi-provider routing design, incident response. For teams running LiteLLM in production.
See services →From the knowledge base
All knowledge →LiteLLM issue analyses
- When PyPI maintainer accounts get hijacked: the LiteLLM 1.82.7/1.82.8 supply-chain compromise
#24518 · security
- Why `import litellm` takes a second, and what it would take to fix it
#7605 · performance
- Bisecting the LiteLLM 1.80 → 1.81 performance regression
#19921 · performance
- The aiohttp `Unclosed client session` warnings in LiteLLM, explained
#13251 · concurrency
Guides
- Accelerating the LiteLLM proxy with Fast LiteLLM
A production-ready guide to running the LiteLLM proxy server with Fast LiteLLM under gunicorn, Docker, and systemd — including the import-order trap that catches most teams.
- Installing Fast LiteLLM
How to install Fast LiteLLM, verify the Rust acceleration is active, and what to do when it isn't.
- Rate limiting LiteLLM at high cardinality
Why per-user rate limiting in pure Python eats memory at scale, and how Fast LiteLLM gets to 42× less RSS without changing your config.
Articles
- Why LiteLLM needs Rust (in three specific places, not everywhere)
A measured argument for hybrid Python+Rust in LiteLLM's hot path — and the places where Python is still the right answer.
- A deep dive into the Fast LiteLLM token counting benchmark
Why tokenization with tiktoken-rs is 1.5–1.7× faster on long inputs and 0.5× as fast on short ones — the FFI overhead curve, fully explained.
FAQ
More questions →What is fast-litellm? +
Fast LiteLLM is a drop-in Rust acceleration layer for the LiteLLM Python library. It replaces hot-path components — connection pooling, rate limiting, token counting — with Rust implementations compiled via PyO3, giving 1.5–3.2× speedups with zero code changes.
How do I install fast-litellm? +
Run `uv add fast-litellm` or `pip install fast-litellm`. Prebuilt wheels are available for Linux (x86_64, aarch64), macOS (x86_64, ARM64), and Windows (x86_64). Rust is not required to install.
How do I enable acceleration? +
Add `import fast_litellm` before `import litellm`. The package monkey-patches LiteLLM at import time, so no other code changes are required.
Is it safe for production? +
Yes. Fast LiteLLM ships with feature flags, automatic fallback to the Python implementation on any error, and a circuit breaker that disables a Rust component after 10 errors. Performance metrics are exposed for monitoring.
Which Python and LiteLLM versions are supported? +
Python 3.8 through 3.13 on Linux, macOS, and Windows. The latest stable LiteLLM release is supported. CI runs a compatibility matrix on every commit.
Explore Fast LiteLLM
Everything on the site, one click from here.
Product
How the Rust acceleration layer works — architecture and components.
Features
Connection pooling, rate limiting, token counting, one-line activation, and safe fallback.
Quickstart
Install and enable Rust acceleration in under a minute.
Benchmarks
Honest numbers — including the cases where Python wins.
Why Fast LiteLLM
The bottleneck profile and why Rust, specifically.
Compare
Fast LiteLLM vs stock LiteLLM, the Rust sidecar, and rolling your own.
Use cases
High-traffic proxies, multi-tenant rate limiting, long-document processing, and scale-ups.
Knowledge base
Guides, articles, and deep LiteLLM issue analyses.
Glossary
Plain-English definitions of the LiteLLM, Rust, and performance terms used here.
FAQ
Latency, memory, drop-in acceleration, and when not to use it.
Consulting
Audits, perf tuning, routing design, and incident response from Neul Labs.
About
About Fast LiteLLM and Neul Labs.
Running LiteLLM at scale?
Whether you need a perf audit, a tricky multi-provider routing design, or hands-on incident response — Neul Labs helps teams ship LiteLLM reliably.