The 5 best LLMOps platforms in
Blog post from Braintrust
In the rapidly evolving landscape of 2025, AI features have become widespread, but success hinges on effectively managing and optimizing these capabilities, particularly through LLMOps, which adapts MLOps practices for large language models like GPT and LLaMA. LLMOps encompasses the entire lifecycle, from prompt engineering to deployment and continuous improvement, turning prototypes into reliable production systems. Companies excelling in this area utilize AI evaluation platforms and LLM monitoring tools to prevent quality regressions and ensure systematic testing, gaining a competitive edge through faster iteration and improved product quality. Braintrust, an industry leader, focuses on evaluation-first development, emphasizing systematic testing over mere observability, and has been adopted by major firms like Notion and Stripe for its ability to significantly boost accuracy and development speed. In contrast, platforms like PostHog and LangSmith cater to specific needs such as product analytics integration and LangChain-native observability, respectively. The choice of LLMOps platform depends on factors such as evaluation depth, observability, team composition, and infrastructure management, highlighting the importance of aligning tools with organizational goals for optimal outcomes in AI applications.