HoneyHive
What is HoneyHive?
HoneyHive is an AI observability platform for AI platform teams that traces agents end to end and turns those traces into monitoring, evaluation, and prompt-improvement workflows. It includes Traces, Experiments, Dashboard, Alerts, Annotation Queues, Granular RBAC, and Docs MCP, with OpenTelemetry-native SDKs and APIs. Teams use it alongside Gartner, NYSE, Business Insider, Commonwealth Bank, AIM Research, and CBA. Plans run Developer free and Enterprise custom.
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At a glance
- HoneyHive is best for AI platform teams who need production tracing, evals, and prompt control in one workflow.
- Developer Free; Enterprise Let's chat
- Yes — The page advertises SDKs and APIs for integrating with application logic and building custom automations, plus OpenTelemetry-native tracing.
What does HoneyHive do?
HoneyHive traces AI agents end to end, then turns those traces into monitoring, evaluation, and prompt-improvement workflows. Its OpenTelemetry-native SDK logs application data synchronously and asynchronously, while traces, agent graphs, online evaluations, and custom dashboards help teams spot anomalies, debug cascading failures, and compare runs without leaving the product. The same workspace also supports prompt versioning, external tools, and 1-click deployments so changes can move from experiment to production with less friction. At scale, HoneyHive is built for production systems that need quantitative oversight across cost, latency, and quality. The platform supports 100+ LLMs and agent frameworks, and its evaluation infrastructure is designed for large runs spanning thousands of test cases. Customers include Gartner, NYSE, Business Insider, and Commonwealth Bank, where HoneyHive is used to support AI systems serving 17M+ consumers. Enterprise deployments can run SaaS, hybrid, or self-hosted, with custom SSO, audit logging, and SIEM forwarding available.
Why use HoneyHive?
- OpenTelemetry-native tracing lets teams plug HoneyHive into existing observability workflows instead of rebuilding instrumentation from scratch.
- Continuous evaluations and regression tracking help catch failures before they spread across production agent flows.
- Self-hosting, hybrid, and single-tenant options give regulated teams more control over deployment and data boundaries.
- Granular RBAC, SSO & SAML, and audit logging support tighter governance across shared AI workspaces.
- Docs MCP, SKILL.md, and CLI support make it easier for coding agents and developers to automate tracing and eval setup.
Who is HoneyHive for?
- AI platform engineers who need to trace agent behavior and debug failures in production.
- ML engineers who want automated evaluations and regression checks before release.
- Prompt engineers who manage shared templates, versions, and deployments across a team.
- Security and IT teams who need SSO, audit logs, and self-hosting options.
- Field experts who review outputs and add human feedback or annotations.
What are HoneyHive's key features?
Traces
Capture production agent traces with OpenTelemetry-native instrumentation to inspect each step, debug failures, and understand behavior across application logic.
Experiments
Run experiments across 100+ models and agent frameworks to compare prompts, tools, and outputs before shipping changes to production.
Dashboard
Track traces, evaluations, and user feedback in a single dashboard, helping teams spot regressions and prioritize fixes faster.
Alerts
Set monitoring and alerts on agent behavior so teams can catch failures early and respond before issues affect users.
Annotation Queues
Route traces and outputs into annotation queues for review by field experts, improving evaluation quality and labeling consistency.
Granular RBAC
Control access with granular RBAC, SSO & SAML, and audit logging for enterprise teams that need tighter governance and reviewability.
Docs MCP
Connect documentation workflows through Docs MCP and SKILL.md to keep agent instructions, context, and operational knowledge in sync.
OpenTelemetry-native
Use OpenTelemetry-native tracing with SDKs and APIs to plug HoneyHive into existing systems and custom automations without changing core workflows.
What does HoneyHive integrate with?
- Calendly
- OpenTelemetry
- Okta
- Azure AD
- Ping
- Splunk
- Datadog
- GitHub Actions
- Cursor
- Claude Code
- VS Code
- Windsurf
- Codex
- OpenAI
- Anthropic
- Pinecone
- SerpAPI
What are HoneyHive's use cases?
Platform debugging in production
AI platform engineers use HoneyHive to trace agent behavior when a workflow fails in production, using Traces and Distributed Tracing to pinpoint where prompts, tools, or model calls break down. They then use Alerts to catch regressions early and shorten time to root cause.
Release checks for ML teams
ML engineers use HoneyHive to compare prompt or model changes before shipping, using Experiments and Evaluation Reports to validate quality against a baseline. With Continuous Integration, they can block regressions before users see them.
Shared prompts for teams
Prompt engineers use HoneyHive to manage reusable templates across collaborators, using Automatic version control and 1-click deployment to keep prompt changes organized and safely rolled out. Live collaboration helps the team review updates without losing track of what changed.
Human review for edge cases
Field experts use HoneyHive to review tricky outputs and add feedback, using Annotation Queues and Annotations to label failures, correct responses, and guide future improvements. Involve field experts helps turn subject-matter review into a repeatable workflow.
How does HoneyHive work?
- Connect your first data source or app using the SDKs, APIs, or OpenTelemetry-native tracing so HoneyHive can capture agent activity from the start.
- Inspect Traces and Trajectories in the Dashboard to follow each request through prompts, tools, and model calls, then filter patterns with Filters and groups.
- Set up Experiments and Online evaluations to compare versions, run Code, AI, and Human Evaluators, and generate Evaluation Reports before release.
- Route failures into Alerts and Monitoring & Alerts, then use Annotation Queues and Annotations to collect human feedback from field experts.
- Lock down access with Granular RBAC, SSO & SAML, and Audit Logging, then keep improving with Continuous Integration and 1-click deployment.
How much does HoneyHive cost?
Developer
Free- No credit card required
- 10K events per month
- Up to 5 users
- Single workspace
- 30d data retention
- Full observability and evaluation suite
Enterprise
Let's chat- Ideal for large organizations
- Custom usage limits
- Unlimited users and workspaces
- Choose between SaaS, hybrid, or self-hosting
- Custom SSO & SAML
- Dedicated support, SLA and team trainings
Frequently asked questions
What is HoneyHive?
HoneyHive is an AI observability platform for AI platform teams that traces agents end to end and turns those traces into monitoring, evaluation, and prompt-improvement workflows. It includes Traces, Experiments, Dashboard, Alerts, and Annotation Queues, with OpenTelemetry-native SDKs and APIs. Teams use it alongside Gartner, NYSE, Business Insider, and Commonwealth Bank. Plans run Developer free and Enterprise custom.
How much does HoneyHive cost? Is it free?
HoneyHive has a free plan, with paid tiers including Enterprise at Let's chat.
What is HoneyHive used for? Who is it for?
HoneyHive is used for Traces, Experiments, and Dashboard. It's built for AI platform engineers, ML engineers, and Prompt engineers.
Does HoneyHive have an API and what does it integrate with?
The page advertises SDKs and APIs for integrating with application logic and building custom automations, plus OpenTelemetry-native tracing. It integrates with Calendly, OpenTelemetry, Okta, Azure AD, Google, and 13 more.
Editor's read
Check the Developer plan's 10K events-per-month cap and 30-day retention before using it for high-volume production tracing. If your agent traffic or review history will exceed either limit, Enterprise custom usage limits are the relevant baseline.
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