Kuldeep Paul

LiteLLM served the early wave of multi-provider LLM development well. It simplified API fragmentation, made model switching easier, and gave teams a quick way to prototype across providers. But as AI applications move from experiments to production systems handling real user traffic, the gateway layer becomes critical infrastructure. And

Enterprise AI teams rarely rely on a single model. Production applications typically orchestrate across OpenAI for general tasks, Anthropic for nuanced reasoning, AWS Bedrock for compliance-sensitive workloads, and open-weight models via Groq or Ollama for cost optimization. Managing these providers directly means dealing with fragmented APIs, inconsistent authentication,

Claude Code has quickly become the go-to terminal-based AI coding agent for engineering teams. It handles file operations, terminal commands, and code editing through Anthropic's tool-calling interface directly from the command line. But in enterprise environments, relying on a single provider creates risk. Rate limits,

Claude Code is one of the most capable AI-powered coding agents available today, bringing advanced code generation, file editing, and terminal operations directly into your workflow. By default, it works exclusively with Anthropic's Claude models. But what if you want to route your Claude Code sessions through

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