LLM Cost Tracking and Spend Management for Engineering Teams

Your team ships a feature using GPT-4, it works great in staging, and then production traffic hits. Suddenly you are burning through API credits faster than anyone expected. Multiply that across three providers, five teams, and a few hundred thousand requests per day. Good luck figuring out where the money went. We built Bifrost, an open-source LLM gateway in Go, and cost tracking was one of the first problems we had to solve properly. This post covers what we learned, how we designed spend management into the gateway layer, and what the alternatives look like. You can get started with the setup guide in under a minute. TL;DR: Bifrost gives you per-request cost logging, four-tier budget hierarchies (Customer, Team, Virtual Key, Provider Config), auto-synced model pricing, and cache-aware cost calculations. All at 11 microsecond latency overhead. You can run it right now with npx -y @maximhq/bifrost. Full docs here. The actual problem with LLM costs Cloud compute costs are predictable.