Navya Yadav

TL;DR: Production AI teams are hitting scaling walls with LiteLLM, from latency overhead that compounds in agent loops to memory management challenges that require constant workarounds. Bifrost by Maxim AI offers a Go-based alternative that adds just 11µs overhead per request at 5K RPS, supports 17+ providers through a

TL;DR: RAG evaluation requires assessing both retrieval (context relevance, precision, recall) and generation (faithfulness, answer quality, hallucination detection). RAG observability demands visibility into retrievals, tool calls, LLM generations, and multi-turn sessions with robust evaluation and monitoring. This guide compares the top five platforms: Maxim AI, LangSmith, Arize Phoenix, Traceloop,

Production AI applications face a critical scaling challenge: GPT-4 costs $10 per million input tokens and $30 per million output tokens, while response times averaging 3-5 seconds create friction in user experiences. For an AI agent handling 10,000 daily conversations with 5,000 tokens per conversation, monthly costs exceed

TLDR Langfuse is an open-source LLM observability platform focused on tracing, prompt management, and basic evaluation workflows. Maxim AI provides comprehensive end-to-end tooling spanning the full AI development lifecycle, including pre-release experimentation, agent simulation and evaluation, production observability, and advanced data management. While Langfuse excels at observability for teams heavily