AI Gateway

Claude Code has become one of the most widely adopted AI-powered coding tools in enterprise engineering teams. Its run-rate revenue has surpassed $2.5 billion since launch, and organizations like Uber, Salesforce, and Accenture are deploying it across hundreds of developers. But scaling Claude Code beyond a handful

Enterprise LLM spending is accelerating rapidly, with nearly 40% of organizations already investing over $250,000 annually on LLM initiatives. As AI applications move from pilots to production, the infrastructure layer between your application and model providers becomes the primary lever for controlling both cost and response time. Without a

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

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,

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