Online vs offline evaluation for LLM apps

Offline evaluation runs your application on a fixed dataset before you ship a change. Online evaluation scores real traffic after you ship. Offline answers “did this change break anything we know about?”, and online answers “what is happening to users right now?”. Anthropic describes automated evals as especially useful pre-launch and in CI/CD, and production monitoring as the layer that “kicks in post-launch to detect distribution drift and unanticipated real-world failures.”

Both are part of LLM evaluation. The split is about when and on what data you score, not which metric you use.

Side by side

What offline evaluation is for

A dataset holds test cases with inputs and, often, expected outputs. HoneyHive’s experiments docs describe the loop: run your function on each datapoint, score each output, aggregate the results, then compare runs such as prompt v1 against v2 on the same input. Because the inputs are fixed, a drop in a metric points at your change.

That makes offline evals the right gate for a deploy. The same docs suggest running experiments in CI/CD to catch issues before deployment, and OpenAI recommends continuous evaluation that runs evals on every change. Hamel Husain wrote that cheap assertion-style tests should run fast as you develop, with costlier model and human review and A/B tests running less often.

The weakness is coverage. Anthropic warns that automated evals can create false confidence if they do not match real usage patterns, and OpenAI lists datasets that do not faithfully reproduce production traffic patterns as a common design mistake. A golden dataset only protects against failures someone already thought of.

What online evaluation is for

Online evaluation scores traces from production. Anthropic says production monitoring reveals real user behavior at scale and catches issues that synthetic evals miss. This is where drift shows up: a provider changes a model, users start asking new things, a tool returns something unexpected. It is the scoring half of LLM monitoring and builds on observability, since you can only score what you trace. Hamel Husain notes that logging traces is a prerequisite for model-based and human evaluation.

Production traffic rarely comes with an expected answer, so online scoring leans on code checks (format, length, required fields) and LLM-as-a-judge graders that need only the input and output. You can still add human review on a sample.

Practical limits, taken from HoneyHive’s online evaluation docs as one concrete implementation:

• Evaluators run asynchronously after ingestion, so they add no latency, but scores appear after a short delay.
• Evaluators match traces by event type, name and properties, and a sampling percentage sets how many matches get scored. The docs suggest 100% under 1K events a day, 25 to 50% for 1K to 10K, and 5 to 25% above that.
• LLM evaluators cost model calls for every evaluation, so Python evaluators can often run at 100% while LLM judges are sampled.
• Because they run after ingestion, online evaluators cannot block an unsafe response. The docs send checks that must happen during execution, such as guardrails, to client-side evaluators (see AI guardrails).

Closing the loop

The two modes feed each other. Anthropic recommends converting user-reported failures into test cases so the suite reflects actual usage, and says capability evals can graduate into regression suites that run continuously. OpenAI advises logging everything so you can mine your logs for good eval cases and using a mix of production data in test sets. Husain describes updating tests continuously from new failures seen in the data.

In practice the cycle is: an online score drops or a user complains, you read the failing traces (error analysis is the method), you add those cases to the offline dataset, you fix the prompt or pipeline, and the offline run now guards against the same failure.

Choosing where to start

• Before launch you have no traffic, so start offline. Build a small dataset, write graders, and run it on every change.
• After launch, add online scoring on a sample of traces for the failure modes you care about most, and alert on drops.
• If you only have capacity for one more thing, add the loop: route flagged production traces into the dataset.

Running both in HoneyHive

In HoneyHive, experiments cover the offline side and online evaluations cover the online side. The online docs say evaluators run on all ingested traces that match their filters, including both production and experiment traces, so one evaluator can score both.

FAQ

What is the difference between offline and online evaluation?

Offline evaluation runs your app on a fixed dataset before release. Online evaluation scores real production traffic. Anthropic describes automated evals as especially useful pre-launch and in CI/CD, and production monitoring as the layer that detects drift and unanticipated real-world failures after launch.

Do I need both?

Usually yes. Anthropic says the most effective teams pair automated evals for fast iteration with production monitoring for ground truth.

Can online evaluation block a bad response?

Not in HoneyHive. Its docs say online evaluators run asynchronously after ingestion and point checks that must happen during execution, such as guardrails and blocking unsafe responses, to client-side evaluators.

How do production failures become offline tests?

Anthropic recommends converting user-reported failures into test cases so the suite reflects actual usage. OpenAI advises logging everything so you can mine your logs for good eval cases.