Go Beats Rust and Python for LLM Proxy Performance
The article discusses the engineering trade-offs in choosing a programming language for building an LLM proxy. Go emerged as the winner, offering sufficient performance at 5,000+ RPS with low overhead, while providing better development velocity compared to the faster but more complex Rust.
Why it matters
The choice of programming language for an LLM proxy can have a significant impact on the product's performance, development velocity, and scalability, making this a critical decision for AI/ML companies.
Key Points
- 1Go handles 5,000+ RPS with ~11 microseconds of overhead per request, more than enough for most LLM proxy workloads
- 2Rust is faster (sub-1ms P99 at 10K QPS), but the development velocity trade-off isn't worth it unless building for hyperscale
- 3Python (LiteLLM) hits a wall at ~1,000 QPS due to the GIL, making it unsuitable for production traffic despite being easy to prototype with
Details
The article compares three programming languages - Go, Rust, and Python - for building an LLM proxy. Python was quickly ruled out due to its performance limitations at scale, as the Global Interpreter Lock (GIL) serializes CPU-bound work, causing issues at higher request rates. Between Go and Rust, the performance numbers were close enough, but the development velocity and ease of use tilted the scales in favor of Go. Go's lightweight goroutines make concurrent streaming connections trivial, and the standard library includes a production-grade HTTP server, allowing for faster implementation. Rust, while offering sub-millisecond latency at 10,000 QPS, requires more development effort and a smaller hiring pool of specialists. The article concludes that Go is the pragmatic choice for most LLM proxy use cases, unless the requirement is for truly hyperscale performance.
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