LiteLLM vs. Komilion: Two Different Bets on the Same Problem

I have a PR open on LiteLLM right now (PR #21354 — adding Komilion as a supported provider). So I've spent time reading LiteLLM's routing code carefully. Here's the honest comparison. What LiteLLM is LiteLLM is a Python SDK and proxy that gives you a unified interface across 100+ LLM providers. You write code once, it works with OpenAI, Anthropic, Google, Cohere, and dozens more. # LiteLLM from litellm import completion response = completion ( model = " claude-opus-4-6 " , messages = [{ " role " : " user " , " content " : " your prompt " }] ) # Same code, different provider: response = completion ( model = " gemini/gemini-3-pro " , messages = [{ " role " : " user " , " content " : " your prompt " }] ) The value is: one interface, any provider. You're still choosing the model — LiteLLM handles translation. LiteLLM also has routing features: load balancing across multiple deployments, fallback lists, budget controls. These are powerful features for production deployments. What Komilion a