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.

AI Observability

Monitor, Evaluate, and Improve Your AI

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.

Read the Docs

Online evaluation Offline experiments LLM-as-a-judge Human annotation Datasets OpenTelemetry-native

Walkthrough of OpenObserve AI Observability: the Quality dashboard scoring answer relevance, hallucination, bias and toxicity across span, trace and session evaluations

Definition

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.

01 · Monitor

See What Happened

Every prompt, tool call and agent handoff captured as a trace, with token cost and latency on each span.

02 · Evaluate

Know If It Was Good

Score outputs for relevance, hallucination, toxicity and bias - on live traffic, and against test sets before you ship.

03 · Improve

Make It Better

Compare experiments, review with humans, distill into datasets, and promote the version that actually improved.

The Gap

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.

Monitoring / APM

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

AI Observability · OpenObserve

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

Pure eval tools skip the infrastructure. APM suites skip the quality. OpenObserve does both, in one store.

Inside the Product

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.

Online Evaluation

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

90.9%healthy

healthy rateover time

Offline Experiments

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

An experiment scoring every row of a dataset across two trials, with progress, dispersion and a compare button

109/109scored

Comparetwo runs

Monitoring

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

A session replay with cost, tool and latency hotspots for a multi-turn agent conversation

33%error rate

tool & cost hotspots

Where It Lands

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.

Capability	OpenObserve	Eval-Native Tools Langfuse, Braintrust, Arize	APM Suites Datadog, Dynatrace
Online evaluation on production traffic Score live spans, traces and sessions	✓	✓	~
Offline experiments before you ship Run a dataset, compare two versions	✓	✓	-
Human annotation into datasets Review queues, distilled into test sets	✓	✓	-
Versioned scorers and score configs History stays comparable	✓	✓	~
Correlate with logs, metrics and Kubernetes The Postgres lock in the same trace	✓	-	✓
Self-host the full product, open source Prompts never leave your infrastructure	✓	~	-
Alerts, SLOs and incidents built in An AI regression pages your on-call	✓	~	✓
Per-GB pricing, full-payload retention Keep prompts for a year, not a fortnight	✓	~	-

✓ full support · ~ partial or add-on · - not available. Category-level summary as of September 2026; individual products vary, and evaluation features on OpenObserve are an enterprise capability.

Benchmarks

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.

vs. Elastic
140×
storage efficiency
Keep full prompts, responses and scores for a year instead of a fortnight.
Read case study
vs. Datadog
8×
more cost-effective
AI observability and the rest of your stack on one bill, not two.
Read case study
vs. Grafana stack
5–15×
faster queries
Search a month of agent traffic in milliseconds, not minutes.
Read case study

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Testimonials

What Teams Say Running OpenObserve

Correlate between environments and signals across various sources with OpenObserve's built-in correlation engine.

“OpenObserve helped us migrate from Datadog in under an hour... reducing observability costs by 4x.”

Lower observability cost

< 1 hour

Migrated off Datadog

Flat pricing

No month-end surprises

AI Observability, Answered

: 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.
: 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.
: 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.
: 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.
: 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.
: 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.
: 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.

Monitor, Evaluate, and Improve Your AI

What Is AI Observability?

See What Happened

Know If It Was Good

Make It Better

Monitoring Tells You It Ran. Evaluation Tells You It Was Good.

It Ran

It Ran, and It Was Good

From a Live Score to the Next Version You Ship

Score Quality on Live Traffic, Continuously

Span, Trace or Session Scope

A Threshold, Not a Bare Number

Scores Sit on the Span

Test a New Version Before It Hits Production

Three Ways to Produce Output

Compare, Then Promote

Trials and Dispersion

LLM-as-a-Judge, or Your Own Scorer

Built-In Judges

Or Keep the One You Trust

Everything Is Versioned

Put a Human in the Loop, Keep the Result

Human Scores Beside System Scores

One Click to a Dataset

Reference-Based Scoring

Your Test Sets, and Your Code, in the Loop

Built From Real Traffic

Score in Your Own Code

Versioned Test Sets

And It Is Still Real Observability Underneath

Every Score Sits on a Trace

Sessions and Agent Graph

Infrastructure, Same Store