Show HN: Optimizing LiteLLM with Rust – When Expectations Meet Reality

Summary

Fast LiteLLM High-performance Rust acceleration for LiteLLM - providing 2-20x performance improvements for token counting, routing, rate limiting, and connection management. Why Fast LiteLLM? Fast LiteLLM is a drop-in Rust acceleration layer for LiteLLM that provides significant performance improvements: 5-20x faster token counting with batch processing token counting with batch processing 3-8x faster request routing with lock-free data structures request routing with lock-free data structures 4-12x faster rate limiting with async support rate limiting with async support 2-5x faster connection management Built with PyO3 and Rust, it seamlessly integrates with existing LiteLLM code with zero configuration required. Installation pip install fast-litellm Quick Start import fast_litellm # Automatically accelerates LiteLLM import litellm # All LiteLLM operations now use Rust acceleration where available response = litellm . completion ( model = "gpt-3.5-turbo" , messages = [{ "role" : "user" , "content" : "Hello!" }] ) That's it! Just import fast_litellm before litellm and acceleration is automatically applied. Architecture The acceleration uses PyO3 to create Python extensions from Rust code: ┌─────────────────────────────────────────────────────────────┐ │ LiteLLM Python Package │ ├─────────────────────────────────────────────────────────────┤ │ fast_litellm (Python Integration Layer) │ │ ├── Enhanced Monkeypatching │ │ ├── Feature Flags & Gradual Rollout │ │ ├── Performance Monitoring │ │ └── Automatic Fallback │ ├─────────────────────────────────────────────────────────────┤ │ Rust Acceleration Components (PyO3) │ │ ├── core (Advanced Routing) │ │ ├── tokens (Token Counting) │ │ ├── connection_pool (Connection Management) │ │ └── rate_limiter (Rate Limiting) │ └─────────────────────────────────────────────────────────────┘ Features Zero Configuration : Works automatically on import : Works automatically on import Production Safe : Built-in feature flags, monitoring, ...