What it does
HoneyHive brings agent traces, evaluations, user feedback, and operational metrics into a monitoring workspace. Its dashboards track cost, latency, token usage, and evaluator scores, while aggregate and drift alerts notify teams when configured measures move beyond expected levels. Developers can follow an alert back to the traces that explain it, then use experiments and datasets to test a change. HoneyHive monitors and helps investigate behavior; it does not independently correct an agent or guarantee output quality.
HoneyHive's monitoring workflow depends on instrumenting the AI application so production traces, evaluations, and feedback reach the platform. The monitoring dashboard aggregates those signals and lets a team build custom charts for cost, latency, token usage, quality, and other event fields. Online evaluations score live traffic; alerts can watch an absolute threshold or compare a metric with an earlier period to detect drift. When an alert fires, developers can inspect the associated traces and investigate which model call, tool action, or change in usage preceded it.
The main advantage is that measurement and trace investigation live in the same workflow. A quality score that declines is a signal to inspect, not proof of a single cause; developers may need to compare prompts, retrieval data, or application versions before deciding what to change. HoneyHive's documentation also describes offline experiments and datasets for testing fixes against collected cases.
The pricing page lists a Free Developer plan and a Custom Enterprise tier; the amount and detailed commercial terms for Enterprise are not public. Teams should confirm those terms directly before estimating production cost.
Strengths
- Monitoring charts can combine production traces, evaluations, and user feedback rather than presenting those signals separately.
- Aggregate and drift alerts cover thresholds, trend changes, and custom fields.
- Triggered alerts can be investigated through the underlying traces and evaluation results.
Watch out for
- Monitoring dashboards depend on application traces, evaluations, and user feedback being sent to HoneyHive.
- The public page lists a Free Developer tier and a Custom Enterprise tier but does not publish an Enterprise rate.
Best for
- Teams operating LLM agents that need to monitor production quality, latency, and cost together.
- Teams that need to investigate alerts alongside HoneyHive traces and evaluation data.
Sources
- HoneyHive - Agent Observability & Evaluation Platform (Oct 26, 2026)
- Monitor AI Application Performance in HoneyHive - HoneyHive AI Docs (Oct 26, 2026)
- Overview - HoneyHive AI Docs (Oct 26, 2026)
- HoneyHive Pricing (Oct 26, 2026)