AI Observability by OpenObserve

AI Observability by OpenObserve is an AI and LLM monitoring product that traces every agent, tool call, and model request, scores quality on live traffic, and attributes cost to the token. It runs in the same platform that handles the rest of a team's production stack, is OpenTelemetry-native, can be deployed anywhere, and is priced per GB instead of per span. Its stated purpose is to show what agents are really doing: every agent session is traced across models, tools, services, datastores, and user sessions so teams can see exactly where time, money, and quality went. It is aimed at the people who operate agentic applications in production — developers, platform and SRE teams — and it also extends to evaluation and AI SRE workflows. The problem it addresses is that agentic applications are expensive and opaque. As OpenObserve frames it: your agent cost $40 and took 34 seconds — but why? A single request fans out into hundreds of spans, and most LLM tools are a silo bolted onto a real observability stack, metered per span and locked to one cloud. That metering model punishes exactly the workloads agentic apps create. Meanwhile, debugging a bad answer often means grepping logs to reconstruct what an agent did. OpenObserve positions itself as one platform for AI and everything under it: LLM traces land next to logs, metrics, traces, and RUM, so when an agent is slow you can see the pod, database, or vector store behind it — no second tool, no swivel-chair. Tracing and mapping work by treating every agent request as a distributed trace. OpenObserve maps each agent to the models, tools, services, and datastores it calls, with request counts and error health on every edge, so a runaway loop or failing tool is obvious at a glance. The Agent Graph renders the full call tree across sub-agents, tools, and models; border colors flag healthy, degraded, and critical paths by error rate; and filters by environment, agent, and version let teams compare releases. Session debugging opens any session and replays the whole conversation — every turn, every tool call, the model behind it, and where cost and latency actually went. Session ribbons break down cost, duration, and tokens per turn; tool, cost, and latency hotspots surface the expensive, slow steps instantly; and one hop takes you from a turn to its full distributed trace. Evaluations run continuously on production traffic. Online eval jobs score live spans, traces, or full sessions the moment they arrive, using LLM-as-judge with your own provider or a remote HTTP scorer. Built-in scorers cover relevance, hallucination, toxicity, bias, and more. You define a score config with a healthy threshold, choose a sampling rate — on a sample or on everything — and score at span, trace, or session scope. Score configs are versioned, and results roll up into a live Quality dashboard that flags what needs attention, replacing the one-off notebook approach to measuring model quality. The evaluation loop closes by turning production traces into test sets. Real traces can be routed into review queues where humans score them alongside the automatic evaluators, with reviewer scores layered over system scores. A single click distills a reviewed trace into a dataset that future versions can be tested against, and Discovery surfaces the failures worth reviewing in the first place. Agent Behavior catches loops and groups failures by kind. Together these steps connect what happens in production to the eval data used to validate the next release. Architecturally, AI and LLM traffic is a first-class layer in one unified stack: it is just another source flowing through the same correlation engine as the frontend, APIs, databases, and infrastructure. Traces, metrics, logs, LLM observability, evals, and AI SRE live in one platform and are queried together with SQL and PromQL. Instrumentation is standard: OpenObserve ingests OpenTelemetry gen_ai spans and OpenInference conventions, so instrumentation you already have keeps working and can be routed to OpenObserve, another backend, or both. The engine is a Rust engine on columnar Parquet storage and bills per GB. Deployments can be managed cloud, self-hosted single binary, bring-your-own-cloud, or bring-your-own-bucket, with federated search across regions and clouds while keeping egress controlled. The outcomes stated in the content are predictability and correlation. Because pricing is per GB ingested and queried rather than per LLM span, per unit, or per seat, agentic applications that fan out into hundreds of spans per request stay predictable instead of spiking the bill, and users are unlimited. Because LLM traces sit beside the rest of the stack, a slow agent can be traced to the pod, database, or vector store behind it without switching tools. Because evaluations run on live traffic against a healthy threshold, quality is watched continuously on a dashboard instead of measured once. Sampling eval jobs and redacting sensitive fields with VRL pipelines before storage help control cost and data exposure. Concrete scenarios include detecting a runaway agent loop: the Agent Graph shows a failing tool or loop at a glance. Replaying a bad answer: a session view shows every turn, every tool call, the model behind it, and where cost and latency went. Comparing releases: filters by environment, agent, and version expose regressions. Measuring quality in production: online evals score live traffic with LLM-as-judge or a remote scorer. Following a failure end to end: from the LLM call through the backend and database, alongside the logs, traces, and metrics from the rest of the production stack. And building eval datasets: routing real traces to annotation queues and distilling reviewed traces into datasets for testing future versions. Target users are teams running agents and LLMs in production — developers, platform and SRE teams, and organizations that need AI observability alongside their existing stack. Integrations are broad and OpenTelemetry-based: OpenAI (Python and JS/TS), OpenAI Assistants, Anthropic (Python and JS/TS), LangChain, Google Gemini, Amazon Bedrock, Mistral, Ollama, DeepSeek, Cohere, Groq, Hugging Face, vLLM, Together AI, Fireworks AI, and xAI Grok, with the FAQ citing coverage of LangChain, CrewAI, LlamaIndex, OpenAI, Anthropic, LiteLLM, and 80+ more frameworks, providers, and gateways. Deployment spans managed cloud in four regions (US East, US West, Europe, India), self-hosted as a single binary, bring-your-own-cloud, and bring-your-own-bucket. Management as code is supported via a Terraform / OpenTofu provider, with enterprise controls including RBAC and SSO. Plans listed include self-hosted Enterprise free up to 50GB, a 14-day cloud free trial, and Enterprise Premium with enterprise-grade support, SSO, and SLAs for large-scale, multi-region deployments. In summary, AI Observability by OpenObserve answers the question of how an agent run accumulated its cost, latency, and quality. It traces every agent, tool call, and model request with OpenTelemetry, evaluates live traffic, and correlates cost per token with the rest of production — one platform, deployable on your terms, priced per GB.