What is honeyhive.ai?
HoneyHive provides end‑to‑end AI observability and evaluation for teams deploying agents in production. The platform supports OpenTelemetry‑native distributed tracing across more than 100 LLMs and agent frameworks, enabling teams to debug failures and standardize telemetry.
Live evaluations run on real traffic, tracking quality, safety, latency, and cost while generating alerts and drift detection for silent failures. Experimentation tools let developers and data scientists test agents offline against curated datasets, compare workflows side‑by‑side, and detect regressions before releases.
Annotation queues route flagged traces to subject‑matter experts for manual review, with custom rubrics and a Git‑native dataset versioning system. Custom dashboards and rich analytics slice metrics to track business‑specific KPIs, and the platform integrates into CI/CD pipelines.
Enterprise‑grade security includes SOC‑2 Type II, GDPR, and HIPAA compliance, with SSO, SAML, RBAC, and optional self‑hosting options. The system is designed for developers, ops engineers, product managers, and compliance teams who need reliable monitoring, evaluation, and expert feedback for AI agents.
honeyhive.ai pricing Free
Verify on the official pricing page.
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honeyhive.ai's key features
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Distributed tracing across AI frameworks
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Online live evaluation of agent traffic
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Session replay with filters and groups
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Custom dashboards and rich analytics
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Experimentation with CI/CD integration
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Annotation queues for human review
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OpenTelemetry-native telemetry support
honeyhive.ai use cases
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Monitor real‑time latency, cost, and safety metrics for a fleet of LLM agents across production environments, leveraging HoneyHive’s OpenTelemetry tracing to trigger drift and safety alerts before they impact users
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Integrate HoneyHive into CI/CD pipelines to automatically evaluate new model versions against predefined quality baselines, detect regressions in safety or performance, and enforce enterprise security compliance before deployment
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Utilize the trace annotation workflow to let domain experts review anomalous agent interactions, annotate root causes, and feed structured insights back into continuous training and fine‑tuning loops
Who is it for?
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Innovation leaders
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Llm developers
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System administrators
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Data analysts
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Test engineers
