Kuldeep Paul

LLM costs compound fast at scale. A single unoptimized prompt chain can multiply expenses by 10x, and without real-time visibility into token usage, teams often discover budget overruns only after the damage is done. As organizations move from AI prototyping to production deployment, a centralized control layer between applications and

Cloudflare AI Gateway offers a convenient entry point for teams looking to add basic observability and caching to their LLM traffic. But as AI infrastructure requirements grow more complex, many engineering teams are finding that Cloudflare's offering falls short in areas that matter most at scale: multi-provider failover,

Claude Code has captured over half of the AI coding market, establishing itself as the leading agentic coding tool for enterprise development teams. Organizations like Spotify, the New York Stock Exchange, and Novo Nordisk are already using it to reduce engineering time by up to 90% on tasks like large-scale

As AI applications scale from prototypes to production systems handling millions of requests, the infrastructure layer between your application and LLM providers becomes critical. Enterprise AI gateways solve a core architectural challenge: how to route, balance, and govern AI traffic across multiple providers without sacrificing performance or reliability. According to