# Monitor, Evaluate, and Improve Your AI > AI observability is monitoring, evaluating, and improving AI systems in production. OpenObserve traces every agent, scores quality online and offline, and turns human review into datasets. Source: https://openobserve.ai/ai-observability/ --- Most tools log what your AI did. AI observability on OpenObserve also tells you whether it was any good, and helps you make it better - by scoring quality on live traffic, running experiments before you ship, and turning human review into datasets. All beside the logs, metrics and traces underneath. - [Get Started Free](https://cloud.openobserve.ai/web/login/) - [Read the Docs](https://openobserve.ai/docs/integration/ai/) ## What Is AI Observability? AI observability is the practice of monitoring, evaluating and improving AI systems in production. It combines telemetry from LLM and agent applications - traces, tokens, latency, cost - with quality evaluation - is the output faithful, relevant and safe - so teams can debug what an AI did, measure whether it was good, and make it better over time. ### See What Happened Every prompt, tool call and agent handoff captured as a trace, with token cost and latency on each span. ### Know If It Was Good Score outputs for relevance, hallucination, toxicity and bias - on live traffic, and against test sets before you ship. ### Make It Better Compare experiments, review with humans, distill into datasets, and promote the version that actually improved. ## Monitoring Tells You It Ran. Evaluation Tells You It Was Good. An AI app can return a fast, error-free answer that is completely wrong. APM suites and tracing tools capture latency, errors and tokens - but a hallucination returns HTTP 200. AI observability adds the axis they miss. ### It Ran - Latency and errors - Token count and cost - Traces and spans - **Was the answer correct?** - not answered - **Did it improve last release?** - not answered ### It Ran, and It Was Good - Everything on the left, plus - Faithfulness, relevance and safety scores - Online evals on production traffic - Offline experiments before you ship - Human review, distilled into datasets ## From a Live Score to the Next Version You Ship Six views of one loop: score live traffic, test a change before it ships, and keep what the review taught you. ### Score Quality on Live Traffic, Continuously Score production traces the moment they arrive - LLM-as-a-judge on your own provider keys, or your own scoring service over HTTP. - **Span, Trace or Session Scope** - One completion, a whole run, or an entire conversation. - **A Threshold, Not a Bare Number** - Each config knows its own healthy line, so a score reads as a verdict. - **Scores Sit on the Span** - Quality lands beside that span's cost and latency. No second bill. [Read the Eval Docs](https://openobserve.ai/docs/integration/ai/) ### Test a New Version Before It Hits Production Run your agent against a dataset, score every row, and compare two runs side by side - before you promote the change, not after. - **Three Ways to Produce Output** - An inline prompt, your agent's own endpoint, or the SDK in CI. - **Compare, Then Promote** - See which run actually improved instead of shipping on a hunch. - **Trials and Dispersion** - Several trials per row flag where the agent cannot make up its mind. [Read the Eval Docs](https://openobserve.ai/docs/integration/ai/) ### LLM-as-a-Judge, or Your Own Scorer A scorer pairs a judge prompt with a score config, grading an output into a number or a label you can threshold, chart and alert on. - **Built-In Judges** - Relevance, hallucination, toxicity, bias, coherence and more. - **Or Keep the One You Trust** - Remote scorers call your own endpoint, with retries and auth. - **Everything Is Versioned** - Tightening a rubric never rewrites last quarter's numbers. ### Put a Human in the Loop, Keep the Result Route real traces, spans or sessions into a review queue, and let reviewers score them alongside the automatic evaluators. - **Human Scores Beside System Scores** - Not on top of them - so you can see where the two disagree. - **One Click to a Dataset** - An annotated item becomes an input paired with its expected output. - **Reference-Based Scoring** - With an expected output, the judge grades against it. ### Your Test Sets, and Your Code, in the Loop The memory of your eval system: inputs, expected outputs, tags and versions - built from production traces, a CSV, or the SDK. - **Built From Real Traffic** - Distill the traces you care about instead of inventing a test set. - **Score in Your Own Code** - openobserve-python scores on your client or in CI and reports back. - **Versioned Test Sets** - March's experiment stays comparable to one run today. [Read the SDK Docs](https://openobserve.ai/docs/integration/ai/) ### And It Is Still Real Observability Underneath Every score sits on a real OpenTelemetry trace. Replay the session, see the cost and tool hotspots, and open the span behind a low score. - **Every Score Sits on a Trace** - Jump from a low score straight to the span that produced it. - **Sessions and Agent Graph** - Replay a multi-turn conversation, cost and quality rolled up. - **Infrastructure, Same Store** - The Postgres lock behind a slow agent is a row in the same trace. [See LLM & Agent Monitoring](/llm-observability/) ## Eval-Native Depth, APM-Grade Context Pure eval tools cannot see your infrastructure. APM suites cannot score your outputs. OpenObserve is the one platform that does both - and self-hosts. | Feature | OpenObserve | Eval-Native Tools | APM Suites | | --- | --- | --- | --- | | | yes | yes | partial | | | yes | yes | no | | | yes | yes | no | | | yes | yes | partial | | | yes | no | yes | | | yes | partial | no | | | yes | partial | yes | | | yes | partial | no | ## Full-Fidelity AI Traces, Priced to Keep A prompt-and-response payload is kilobytes per span. Sampling to save money is how you lose the one trace the incident lived in. Every number below is measured against a named vendor, not an industry average. Keep full prompts, responses and scores for a year instead of a fortnight. AI observability and the rest of your stack on one bill, not two. Search a month of agent traffic in milliseconds, not minutes. Comparing us to an eval-native tool instead? See how much you would save switching today. - [See All Comparisons](/comparison/) ## What Teams Say Running OpenObserve Correlate between environments and signals across various sources with OpenObserve's built-in correlation engine. ## AI Observability, Answered ### What is AI observability? AI observability is the practice of monitoring, evaluating and improving AI systems in production. It captures telemetry from LLM and agent applications - traces, tokens, latency, cost - and pairs it with quality evaluation, scoring whether outputs are faithful, relevant and safe, so teams can debug what the AI did, measure whether it was good, and improve it. It extends traditional observability with signals that catch failures which never raise an error, such as hallucinations and unsafe responses. ### How is AI observability different from LLM observability? LLM observability usually means the monitoring layer: tracing every prompt, tool call and response, with token cost and latency. AI observability is the broader discipline that adds evaluation and improvement on top - online scoring of production traffic, offline experiments before release, human annotation and datasets. OpenObserve covers both in one platform. ### What is the difference between online and offline evaluation? Online evaluation scores traffic from an agent already in production - the real traces users generate - and tracks quality continuously. Offline evaluation scores an agent before it ships, running it against a fixed dataset so you can compare versions. OpenObserve does both: online eval jobs on live traces, and offline Experiments on datasets. ### How do offline experiments work? An Experiment runs a task against every row of a dataset and scores the output. Output can be generated three ways: an inline task that renders a prompt and calls a provider from the platform, a remote task that calls your agent's own HTTP endpoint, or an SDK task that runs in your code or CI and reports results back. You then compare two experiments on the same dataset and promote the version that improved. ### What is LLM-as-a-judge scoring? LLM-as-a-judge uses a language model, guided by a judge prompt, to grade another model's output against a rubric and produce a score. In OpenObserve a scorer pairs that judge prompt with a versioned score config. When the dataset includes an expected output, the judge uses it for a precise, reference-based score. You can also call your own remote scorer instead. ### How is this different from Datadog or Dynatrace? APM suites monitor AI applications - latency, errors, token cost - but they do not evaluate whether an output was correct, and they do not run offline experiments before release. OpenObserve adds online and offline evaluation, LLM-as-a-judge scoring, human annotation and datasets, while still correlating with the logs, metrics and infrastructure APM covers - self-hostable and priced per GB. ### Do I need to move off Langfuse, Braintrust or Arize? No. Anything that exports OpenTelemetry lands in OpenObserve alongside your other telemetry, and scores from another tool can arrive as span attributes. Many teams keep a prompt-management workflow they like and use OpenObserve as the store that affords full retention and correlates evaluation with infrastructure. ## Observe, Evaluate and Improve Your AI in One Place Point your OpenTelemetry exporter at OpenObserve and watch the first traces, scores and experiments land. - Get Started For Free - [Schedule Demo](/demo/)