I tried every major LLM observability platform. Traceport changed how I think about AI gateways.

Most tools just log your prompts. Traceport routes, caches, evaluates, and observes — all through one API. When I started building production LLM applications, my monitoring setup was embarrassingly simple: a few console.log statements around my OpenAI calls and a rough sense of what things cost from the billing page. That worked fine for prototypes. It broke down the moment real users showed up. Over the last year, I've worked my way through most of the major LLM observability platforms — LangSmith, Langfuse, Helicone, Arize Phoenix, and others. Each one solved a piece of the puzzle. Then I found Traceport, and it reframed how I think about the whole problem. Why LLM observability is different from regular monitoring Traditional APM tools — Datadog, New Relic, Grafana — are excellent at answering "is this broken?" They track latency spikes, error rates, and infrastructure health. But with LLMs, the harder questions are qualitative: Is the output actually good? Did the model hallucinat