AI Gateway

As LLM-powered applications move into production, inference costs and response latency become two of the most pressing infrastructure challenges. Every API call to a model provider consumes tokens and adds latency, and users rarely phrase the same question identically. Traditional exact-match caching fails to address this because natural language queries

Claude Code is built around Anthropic's native models by default, but developers increasingly need the flexibility to route requests through models from other providers whether for cost control, latency optimization, or compliance reasons. AI gateways solve this by acting as a unified proxy layer between Claude Code and

As AI applications scale, repeated LLM calls for similar queries drive costs up and latency higher. Semantic caching addresses this by storing and reusing responses based on meaning rather than exact text matches so "What is the refund policy?" and "How do I get a refund?"

TL;DR: As enterprise LLM spending surges to $8.4 billion in 2025, teams building production AI applications need LLM gateways that can handle scale without becoming bottlenecks. While LiteLLM has been a popular choice for multi-provider routing, production teams are increasingly facing performance degradation, memory leaks, and latency overhead

TL;DR: LLM token costs spiral fast once you move past prototyping. Enterprise AI gateways solve this by placing an intelligent layer between your application and LLM providers, enabling semantic caching, smart routing, and automatic failover. This guide covers five production-ready gateways in 2026: Bifrost, LiteLLM, Cloudflare AI Gateway, Kong

TL;DR: Semantic caching lets AI gateways recognize when incoming prompts mean the same thing as previous ones, even when worded differently, and return cached responses instead of making a new LLM API call. This cuts token spend and latency significantly. This article covers five AI gateways that support semantic