On "The best AI observability tools for developers, compared"

This is the wrong analogy for what observability actually solves in production systems. The kitchen metaphor implies you're flying blind until something breaks, but observability isn't about visibility into your AI model's internals—it's about catching signal degradation, hallucinations, and cost drift before they tank user experience or your margins. For practitioners building with LLMs, that means less "let's instrument everything" and more "what metrics actually tell us the model is failing in ways users will hate?" If you're spending energy on observability tools without first defining what failure looks like for your specific use case, you're just collecting expensive telemetry.