Latest

Compare the best LLM gateways for monitoring Claude Code token spend, with per-developer cost tracking, budgets, and real-time token usage visibility. A single Claude Code session can issue hundreds of model calls across Sonnet, Opus, and Haiku, and without a gateway in front of it, that Claude Code

Claude Code is Anthropic's terminal-based AI coding agent. Out of the box, it connects to Anthropic's API and uses Claude models exclusively. This works well for many use cases, but production engineering teams often need more flexibility: the ability to route Claude Code traffic through

As production AI applications scale, two infrastructure challenges dominate engineering budgets: redundant LLM API calls and inefficient provider routing. Organizations running high-volume inference workloads routinely overpay due to repeated queries hitting provider APIs instead of being served from cache, and static routing configurations that ignore real-time provider performance.

As AI applications scale from prototypes to production systems, two infrastructure challenges consistently surface: redundant LLM API calls that inflate costs and naive routing strategies that ignore provider performance in real time. Semantic caching and intelligent LLM routing solve both problems, but most solutions either lock teams into proprietary platforms

TL;DR LiteLLM's Python-based proxy remains popular for multi-provider LLM access, but its performance ceiling and lack of enterprise governance features push production teams toward stronger alternatives. This article covers five platforms worth evaluating in 2026: Bifrost (the fastest open-source AI gateway, built in Go)

TL;DR Claude Code usage can cost $100-200 per developer per month on API pricing, with heavy users burning through thousands. Without a gateway layer, enterprises have zero visibility into per-team or per-project spend. Bifrost, an open-source AI gateway built in Go, solves this with hierarchical