AI Observability by OpenObserve
Product Hunt fmerian
OpenObserve launched AI observability for agents and LLMs today. It tracks where the time and money went, so you can see why an agent cost $40 and took 34 seconds.
Based on reporting by Product Hunt, fmerian — read the original for the full story.
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OpenObserve is back with its second launch, and this one is aimed squarely at the messier side of AI apps: agents that wander through tools, services, databases, and model calls before doing anything useful.
The pitch is simple enough. If an agent burns $40 and takes 34 seconds, OpenObserve wants to show you exactly where that happened. It’s built around OpenTelemetry, so it plugs into the same observability stack teams already use for logs, traces, and metrics.
The product traces each agent session across models, tools, services, datastores, and user sessions. That matters because failures in an agent are rarely neat. A bad LLM call can ripple into backend code, then into a database, and then into a user-facing problem that looks much bigger than the original mistake. OpenObserve says it follows that chain.
It also includes online evals and loop detection, which suggests the focus is not just on seeing what happened after the fact, but on catching ugly behavior while the system is still running. For teams shipping agents into production, that’s the real headache: not whether the model can answer a demo prompt, but whether the full stack can survive contact with actual users.
My take — AI-written commentary, not fact-checked reporting
This is the sort of boring infrastructure AI keeps needing and keeps pretending it doesn’t. The model race gets the headlines, but the people paying the cloud bill want receipts, not magic. OpenTelemetry-native tooling is the right instinct here: less incense, more timestamps.
Read more about this at: Product Hunt
