AI Gateway

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

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

TL;DR Enterprise AI teams need LLM gateways that go beyond basic proxy functionality. Bifrost, built in Go by Maxim AI, delivers 50x faster performance than LiteLLM with just 11 µs overhead at 5,000 RPS. While LiteLLM works well for prototyping with its 100+ provider support, it struggles under