It delivers overwhelming performance and cost efficiency compared to Elasticsearch. The open-source version, Slack-based customer support, pricing, and Enterprise tier (200GB per month) are virtually identical to the paid features — there's no real difference.
Hi Product Hunt, Ashish here, Head of Engineering at OpenObserve.
If your AI agent got stuck in a tool-call loop right now, would you know? Would you spot it before your customer did?
We didn't.
During a live demo, our own AI SRE Agent silently stalled. No errors. No timeouts. Nothing useful in the logs.
We had to dump raw spans and debugged through them just to find the loop.
Reading raw spans isn't a debugging strategy—it’s an apology waiting to happen.
So we built AI Observability directly into OpenObserve. We wanted to see inside the black box.
Here is what it actually gives you:
Sessions map out every single turn. You see every LLM request, tool call, token count, cost, and exactly what prompt caching saved you.
Agent Graph plots your agents, tools, and models onto the exact same service map as the rest of your backend infrastructure.
Agent Behavior automatically flags the sessions that loop or fail—long before a user complains.
Online Evals let you score live sessions using any judge model (bring your own provider and key).
Annotation Queues let you turn those ugly, failed sessions into clean datasets so you can regression-test your fixes.
The best part? It's OpenTelemetry-native.
It normalizes OTel GenAI, OpenInference, OpenLLMetry, and Vercel AI SDK and many more out of the box. Nothing you’ve already wired up goes to waste, and you don’t have to ship a second copy of your data to another platform.
Our SRE Agent runs on this daily now, and it's still our harshest critic.
Point it at your own agent traces. I'd love to hear what you find.