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Online evaluations run your evaluators automatically on production traces after ingestion. This gives you continuous quality scores alongside your cost and latency metrics, without adding latency to your application.

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How It Works

When you enable online evaluation for an evaluator, HoneyHive runs it asynchronously on incoming production traces:
  1. Your application sends traces to HoneyHive
  2. HoneyHive matches traces against your evaluator’s event filters
  3. Matching events are evaluated (subject to your sampling rate)
  4. Results appear as metrics in your dashboard and on individual traces
Online evaluations only run on production traces (where source is not evaluation or playground). Experiment traces are always evaluated at 100%.

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Enabling Online Evaluation

You can enable online evaluation on any server-side evaluator (Python or LLM):
1

Go to the Evaluators page

Navigate to the Evaluators tab in HoneyHive.
2

Create or select an evaluator

Create a new Python or LLM evaluator, or select an existing one. Configure event filters, return type, and your evaluation logic.
HoneyHive LLM evaluator editor showing event filters set to model type, OpenAI gpt-4o provider, evaluation prompt with template syntax, sampling percentage, and return type configuration
3

Enable the evaluator

Toggle the Enabled switch in the evaluators table. This tells HoneyHive to run this evaluator on matching production traces.
4

Set a sampling percentage

Set the Sampling percentage to control what fraction of matching events get evaluated (e.g., 25%). This controls cost for LLM-based evaluators at high volumes.

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Event Filters

Each evaluator has event filters that determine which traces it runs on. You can filter by event type, event name, and any event property from your schema. For example, you might run a hallucination evaluator only on model events named generate_response, or add a filter like metadata.environment is production to limit evaluation to specific contexts. See Event Filters for the full list of supported filter options and operators.

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Sampling

LLM-based evaluators incur model costs for every evaluation. At production scale, use sampling to control spend:
VolumeSuggested SamplingRationale
< 1K events/day100%Full coverage is affordable
1K - 10K events/day25 - 50%Good signal with moderate cost
10K+ events/day5 - 25%Statistical significance with controlled spend
Python evaluators are much cheaper to run than LLM evaluators. You can often run Python evaluators at 100% sampling even at high volumes.

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Viewing Results

Online evaluation results are available in two places:
  • Dashboard charts: Select your evaluator as a metric in Custom Charts to track quality over time, group by properties, and set up alerts
  • Individual traces: Each evaluated trace shows its evaluator scores alongside inputs, outputs, and other metadata
HoneyHive monitoring dashboard showing charts for session duration, LLM call duration, token usage, and custom evaluator metrics like Search Relevance and Agent Execution Quality
You can also use the Discover view to build custom queries on evaluator scores, filter by source, and drill into individual events.
HoneyHive Discover view showing a Search Relevance evaluator metric charted over time for a tool_search_web event, grouped by source

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Choosing Between Client-Side and Server-Side

Client-SideServer-Side (Online)
RunsIn your applicationOn HoneyHive after ingestion
Latency impactAdds to request timeNone
Best forGuardrails, format checks, PII detectionLLM-as-judge, complex quality scoring
Managed inYour codeHoneyHive UI
Use client-side evaluators for checks that need to happen during execution (guardrails, blocking unsafe responses). Use online evaluations for quality scoring that can happen asynchronously.

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Next Steps

Python Evaluators

Create code-based evaluators for programmatic checks

LLM Evaluators

Use LLMs to score quality, relevance, and tone

Custom Charts

Visualize evaluator scores in dashboards

Alerts

Get notified when quality metrics drop