Go vs. Python for an LLM Gateway: What We Measured (blog.ai-process.dev)

In a recent comparison of language choices for LLM gateways, a team opted for Go over Python, creating the Keystone Model Switchboard (KTMS). This decision is significant as it challenges the common practice in machine learning, where Python is predominantly favored. The team conducted benchmarks that revealed KTMS significantly outperformed LiteLLM (a Python-based gateway), notably in latency (adding only ~0.6 ms compared to LiteLLM's ~8.5 ms) and throughput, sustaining over 4,000 requests per second (RPS) versus LiteLLM's peak of around 250 RPS. The benchmarks highlighted key advantages of Go, including its lean resource usage and efficient process handling, as KTMS operates as a single process managing multiple requests via goroutines, while LiteLLM requires multiple Python workers due to the Global Interpreter Lock. Additionally, KTMS showcased faster startup times and lower CPU consumption, contributing to overall improved performance for production-level applications. This insight into the execution performance of different gateway architectures underlines the potential of compiled languages like Go in high-demand AI environments, suggesting a shift in how developers might approach building scalable AI applications.