HoneyHive

HoneyHive is the leading AI observability and evals platform, trusted by next-gen AI startups to Fortune 100 enterprises. We make it easy and repeatable for modern AI teams to debug, evaluate, and monitor AI agents, and deploy them to production with confidence. HoneyHive’s founding team brings AI and infrastructure expertise from Microsoft OpenAI, Amazon, Amplitude, New Relic, and Sisu. The company is based in New York and San Francisco.

Agent Operations

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Founded

2022

Location

New York, NY

Employees

Funding

$6.5M Seed

HoneyHive: AI Observability and Evaluation for LLM Apps and Multi‑Step Agents

HoneyHive is an AI observability and evaluation platform built for teams shipping production LLM applications and multi-step agents. It provides distributed tracing, online and batch evaluation, and always-on monitoring to measure and improve quality, cost, and latency. Positioned as the DevOps layer for AI systems—where prompts, tools, datasets, and models change frequently—HoneyHive helps teams debug failures, compare experiments, and push safe updates.

Visit: [HoneyHive Homepage](https://www.honeyhive.ai)

Product pages: [Observability](https://www.honeyhive.ai/observability) | [Evaluation](https://www.honeyhive.ai/evaluation)

Monitoring docs: [Monitoring Overview](https://docs.honeyhive.ai/monitoring/overview)

What HoneyHive Does

**Distributed tracing for agents and RAG pipelines**: Trace multi-step tool use, retrievals, model hops, and guardrail decisions with production-grade telemetry. Built on **OpenTelemetry** for clean, vendor-agnostic trace data.

**Online and batch evaluations**: Run automatic evals (quality, safety, policy), human review, and regression tests. Manage eval datasets and rubrics centrally.

**Prompt and experiment versioning**: Version prompts, compare runs, and A/B changes to prevent regressions before release.

**Always-on monitoring and alerts**: Track quality, latency, and cost over time. Catch drifts and failures with alerting and dashboards.

**Governance and team workflows**: Enterprise-focused features emphasize governance, RBAC, auditability, and safe release workflows.

How It Works (Architecture)

**Model and framework agnostic**: Works across common LLM stacks; no hard vendor lock-in.

**OpenTelemetry-based tracing**: Standardized trace formats for clean, composable data and easy integration.

**SDK integration**: Instrument your app to stream traces and metrics; see the [Monitoring Overview](https://docs.honeyhive.ai/monitoring/overview) for quick-start guidance.

Who It’s For

Product and platform teams operating production LLM apps and multi-step agents

Data/ML engineers responsible for reliable traces, evals, and regression testing

Enterprises that require **governance, auditability, RBAC**, and cross-team workflows

Common Use Cases

**Agent reliability**: Diagnose brittle tool calls, fix failing steps, and improve chain-of-thought execution without exposing sensitive content.

**RAG quality**: Monitor retrieval performance, hallucination risk, and answer relevance end-to-end.

**Prompt lifecycle management**: Version, experiment, and A/B prompts; enforce eval gates to prevent regressions.

**Cost and latency optimization**: Track token spend and response times per step to meet SLAs and budgets.

**Safety and compliance**: Run online evals and human review for policy adherence; maintain audit trails.

**Release management**: Validate changes in sandbox; promote configurations when eval thresholds pass.

Why Teams Choose HoneyHive

**Focused on agent observability and online evals**: Strong alignment with enterprise reliability needs; see [Insight Partners’ investment note](https://www.insightpartners.com/ideas/behind-the-investment-honeyhive/).

**Unified testing + monitoring**: Reduces glue code, spreadsheets, and bespoke dashboards by centralizing datasets, rubrics, and metrics across the lifecycle .

**Clean developer experience**: Clear onboarding via docs; start with tracing and monitoring quickly .

Market validation and discussion: See the [Hacker News thread](https://news.ycombinator.com/item?id=37777683) on agent observability needs.

Considerations

**Limited public reviews**: The [G2 seller page](https://www.g2.com/sellers/honeyhive) currently shows no public reviews.

**Crowded category**: Buyers often compare against LangSmith, Langfuse, Helicone, and Humanloop; expect side-by-side pilots. Competitive context also appears in the [Hacker News discussion](https://news.ycombinator.com/item?id=37777683) and broader Reddit threads on LLM observability (example: [tooling fatigue discussion](https://www.reddit.com/r/LLMDevs/comments/1jb1knr/why_the_heck_is_llm_observation_and_management/)).

Integrations

**OpenTelemetry** for tracing and instrumentation

**SDK-based app integration**: Works with common LLM providers and frameworks through standard tracing and SDK hooks; details in the [Monitoring Overview](https://docs.honeyhive.ai/monitoring/overview).

Pricing and Trial

Primary CTA is **book a demo**: [Request a demo](https://www.honeyhive.ai)

No public self-serve free trial listed at this time.

Company and Funding

Offices in New York and San Francisco; team is small and focused (LinkedIn snapshot: [HoneyHive on LinkedIn](https://www.linkedin.com/company/honeyhiveai)).

In 2025, HoneyHive announced **$7.4M** in Seed and Pre-Seed funding led by Insight Partners and made the platform generally available:

[Funding announcement](https://www.honeyhive.ai/post/honeyhive-raises-7-4m)

[Insight Partners: Behind the Investment](https://www.insightpartners.com/ideas/behind-the-investment-honeyhive/)

Additional coverage: [AlleyWatch](https://www.alleywatch.com/2025/04/honeyhive-ai-observability-platform-mohak-sharma/)

At-a-Glance

Product: AI observability and evaluations for LLM apps and agents

Focus: Tracing, online evals, simulation testing, and quality/cost/latency monitoring

RAG: Monitors retrieval steps, tool use, and guardrails

Architecture: OpenTelemetry-based, model/framework agnostic

Compliance: Enterprise posture with governance and RBAC

GTM: Demo-first; enterprise and high-growth AI teams

Additional Resources

[Homepage](https://www.honeyhive.ai)

[Observability](https://www.honeyhive.ai/observability)

[Evaluation](https://www.honeyhive.ai/evaluation)

[Monitoring Overview Docs](https://docs.honeyhive.ai/monitoring/overview)

[Funding Announcement](https://www.honeyhive.ai/post/honeyhive-raises-7-4m)

[Insight Partners Investment Note](https://www.insightpartners.com/ideas/behind-the-investment-honeyhive/)

[LinkedIn](https://www.linkedin.com/company/honeyhiveai)

Community discussion: [Hacker News](https://news.ycombinator.com/item?id=37777683)

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