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HoneyHive
Agent observability and evaluation platform from New York that traces every step of AI agents in production and scores them with automated and human evaluations.
Enterprises running customer-facing AI agents in regulated settings, such as banks and insurers, that need tracing, evals, and audit evidence together.
Hobby projects that only need a simple prompt log, or teams that want public pricing for higher usage tiers.
Verdict
HoneyHive is an AI observability company in New York, New York, led by co-founders Mohak Sharma, its chief executive and a former product manager at Templafy, and Dhruv Singh, its chief technology officer, who worked on Microsoft's OpenAI innovation team; the two met as roommates at Columbia University. Its platform traces agent runs through OpenTelemetry, evaluates live traffic with code, LLM judges, and expert review queues, and runs as managed SaaS, hybrid, or fully self-hosted. It matters because Commonwealth Bank of Australia and other enterprises need proof that agents serving millions of customers behave as intended.
Score Breakdown
How HoneyHive scores in the categories that matter to its buyers.
Pricing
HoneyHive has a free Developer plan. Enterprise pricing is quoted.
| Plan | Price | What stands out |
|---|---|---|
| Developer | Free | Tracing, automated and human evaluations, prompt versioning; no credit card |
| Enterprise | Contact sales | Unlimited users and workspaces, SLA, self-hosted, hybrid, or single-tenant |
The Field at a Glance
How HoneyHive compares with other Eval & quality vendors we reviewed, by Overall Score and relative typical engagement cost.
HoneyHive scores 7.7 in Eval & quality, under Braintrust (7.8), level with Confident AI (7.7), and ahead of Hamming AI (7.5), Vellum (7.0), and Patronus AI (6.6). Relative cost lands mid-band for this subcategory.
Use-case matrix
| Use case | Fit | Notes |
|---|---|---|
| Banks and regulated industries | Strong | Runs agents serving 17M+ consumers at CBA. |
| Self-hosted observability | Strong | Fully self-hosted or hybrid data plane on Enterprise. |
| Expert review of outputs | Strong | Annotation queues and custom rubrics. |
| Coding-agent session tracking | Strong | Tracks cost, latency, and tool use across vendors. |
| Evals as code in CI | Mixed | Evaluators and datasets can be versioned in Git. |
| Public mid-tier pricing | Weak | Only the free and Enterprise plans are listed. |
Who it’s for
Good fit
- Bank and insurer AI teams
- Platform teams standardizing agent telemetry
- Risk and compliance reviewers
Poor fit
- Single-prompt hobby apps
- Teams needing a priced mid-tier
- Buyers who want only offline evals
Review Excerpts
Below are excerpts from public reviews. Our team scoured public reviews, forums, and chat rooms to get a balanced view of customers' experience with this company. Paid reviews and pay-for-play sites such as Clutch were excluded.
HoneyHive reports that it powers observability and evaluation across dozens of mission-critical AI applications at Commonwealth Bank of Australia, supporting AI agents that serve more than 17 million consumers.
The Enterprise plan offers multi-tenant SaaS, single-tenant SaaS, hybrid, or fully self-hosted deployment, and the platform is audited to SOC 2 Type II with GDPR EU data residency and a HIPAA BAA available.
HoneyHive lists only a free Developer plan and a contact-sales Enterprise plan, so teams that outgrow the free event limits cannot see a price before a sales conversation.
Company Facts
- Founded
- 2022 (CB Insights)
- Headquarters
- Albany, New York, United States (CB Insights)
- Total funding raised
- $7.4M (CB Insights)
- Key investors
- Zero Prime Ventures, Insight Partners, 468 Capital, MVP Ventures, AIX Ventures (CB Insights)
- Website
- honeyhive.ai
Key Leadership
Mohak Sharma
Co-founder and CEO
Methodology
This page is an independent evaluation of HoneyHive for buyers comparing options in eval & quality. AI Industry Reviews accepts no sponsorships, advertising, or pay-for-placement fees. HoneyHive did not pay for this review.
What we scored
The headline number is an Overall Score on a 0-10 scale. Eight criteria sit under it in two groups.
- Buyer outcomes (for eval & quality)
- Agent tracing depth
- Evaluation workflow
- Enterprise deployment options
- Pricing transparency
- Company & commercial
- Innovation & product leadership
- Project management & communication
- Pricing
- Contract fairness
Pricing measures whether the price looks fair for the value delivered, including packaging and renewal friction that show up in real buying cycles.
Score Composition
| Input | Weight | What it covers |
|---|---|---|
| Reviews | 40% | A proprietary read of what practitioners say about likes, complaints, and day-to-day use, including public review sites, forums, and private chat rooms. Paid reviews and pay-for-play sites such as Clutch are out of scope. |
| Product | 35% | Hands-on look at screens and workflows. |
| Pricing | 15% | Whether the price looks fair for what you get. |
| Docs & training | 10% | Docs, tutorials, and training material. |
How we balanced the evidence
The Overall Score is the simple average of the eight criteria. Recommendation language follows that score and the fit pattern described above.
Scope
HoneyHive is graded here as eval & quality. Criteria scores can move as more review volume and product checks are added.