Paweł Huryn - The Ultimate Guide to AI Observability and Evaluation Platforms - September 2025
FreeThe actionable guide to AI observability and evaluation platforms
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About Paweł Huryn - The Ultimate Guide to AI Observability and Evaluation Platforms - September 2025
The Ultimate Guide to AI Observability and Evaluation Platforms is a comprehensive, hands-on newsletter article by Paweł Huryn (The Product Compass). Based on 100+ hours of testing and real-world experience, the guide breaks down key capabilities of top platforms like LangSmith, Langfuse, Arize, OpenAI Evals, Google Stax, and PromptLayer. It covers prompt management, observability (logging, tracing, capture methods), and evaluations, and includes a step-by-step guide to running the evals loop. The article is aimed at AI product managers and practitioners who want actionable, no-fluff advice beyond theoretical frameworks.
Key Features
Covers three core platform capabilities: prompt management, observability, and evaluations
Comparison of major AI observability and evaluation platforms (LangSmith, Langfuse, Arize, OpenAI Evals, Google Stax, PromptLayer)
Step-by-step walkthrough of the evals loop with real examples
Explains four methods for capturing logs: direct API calls, vendor SDKs/wrappers, OpenTelemetry, and proxy/gateway
Based on 100+ hours of testing and the AI Evals cohort experience
Pros & Cons
Pros
- Actionable, hands-on advice rather than theoretical frameworks
- Based on extensive real-world testing (100+ hours)
- Covers both observability and evaluations in one guide
- Includes practical capture methods and migration considerations
Cons
- Full access requires a paid subscription to The Product Compass newsletter
- Focuses on platforms as of September 2025; landscape may change rapidly
Best For
Learning AI observability and evaluation concepts as an AI PMChoosing the right observability/evaluation platform for a teamImplementing prompt management and versioning in productionDebugging and optimizing LLM applications with proper logging and tracing
FAQ
What is AI observability according to the guide?
AI observability is about logging and understanding what LLMs are actually doing, including what gets logged (requests vs. traces) and how logs are captured (direct API, SDKs, OpenTelemetry, proxy/gateway).
Which platforms are compared in the guide?
The guide compares LangSmith, Langfuse, Arize, OpenAI Evals, Google Stax, and PromptLayer, among others.
Does the guide include a step-by-step tutorial?
Yes, it includes a step-by-step guide on how to run the evals loop yourself, with no fluff.