Kamya Shah

LiteLLM serves a purpose in development environments. It abstracts provider APIs and enables rapid prototyping with minimal configuration. However, as teams scale their AI applications toward production workloads, the architectural limitations of Python-based gateway solutions become critical constraints. Performance overhead, reliability issues, and missing enterprise features create friction that

As AI applications scale from proof-of-concept to production workloads, the limitations of traditional LLM gateway solutions become apparent. LiteLLM, built on Python, introduces latency overhead, dependency complexity, and observability challenges that hinder teams trying to move fast. For organizations prioritizing performance and enterprise governance, a different approach is

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?"