Kuldeep Paul

Token costs are the silent budget killer for AI applications in production. Every LLM API call burns through tokens for input context, tool definitions, repeated queries, and suboptimal routing, and the bill compounds fast as you scale. Most teams discover this only after their monthly invoice arrives from OpenAI, Anthropic,

API costs are one of the fastest-growing line items for teams building production AI applications. When an application receives hundreds of thousands of requests per day, a significant portion of those requests are semantically identical or near-identical variations of each other. Without an intelligent caching layer, every one of those

LiteLLM earned its place as the go-to open-source proxy for teams unifying access across multiple LLM providers. Its Python-based SDK translates API schemas from OpenAI, Anthropic, AWS Bedrock, and others into a standardized OpenAI-compatible format, making it a solid starting point for prototyping. But as AI applications have matured from

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 critical