HoneyHive

Observability and evaluation platform for AI agents and LLM applications

Editorially reviewedChecked Sep 2026How we review

llm-observability#llm-observability#evaluation#tracing#agent-monitoring

Free plan Free trial API Self-hosted Teams

Toolglade’s take

Best for agentic apps where you need to visualize multi-step workflows as DAGs and run many online evals on live traffic, with self-hosted and hybrid deployment options for regulated setups. Depth means a steeper learning curve than lighter monitors.

Should you use HoneyHive?

Best for: Enterprises running production agents · Teams needing data-residency control · Cross-functional AI product teams
Not ideal for: Hobbyists wanting a free permanent plan · Teams wanting fully transparent self-serve pricing · Non-LLM general observability needs
Biggest strength: End-to-end tracing, evaluation, and monitoring
Biggest weakness: Pricing is sales-led and not fully public
Pricing: Free plan, paid from Contact
Top alternative: Vellum · see all

Facts & pricing last checked September 2026. How we review

About HoneyHive

HoneyHive is an AI observability and evaluation platform offering tracing, evals, testing, and production monitoring for LLM apps and agents, with data-residency support and a sales-led pricing model.

HoneyHive helps engineering, product, and subject-matter teams collaborate on building reliable AI applications. It combines distributed tracing of LLM and agent calls, offline and online evaluation, dataset management, and prompt experimentation with production monitoring — so teams can debug failures, measure quality, and catch regressions across the development lifecycle. A distinguishing focus is enterprise readiness, including data-residency options, positioning HoneyHive for regulated organizations that need control over where their evaluation and trace data lives. It supports human-in-the-loop review alongside automated evaluators, letting domain experts and engineers jointly assess agent behavior. HoneyHive uses a sales-led pricing model: there is a free tier for getting started and trials, but production monitoring and enterprise features generally require an Enterprise conversation with custom pricing. As of 2026, teams should contact HoneyHive for current pricing based on usage and requirements.

Weighing your options?See how HoneyHive compares to the alternatives.

TL;DR

HoneyHive is an enterprise-focused AI observability and evaluation platform for LLM apps and agents, combining tracing, evals, and monitoring with data-residency support and sales-led pricing.

Company overview

HoneyHive is an LLMOps company building observability and evaluation tooling for teams shipping AI agents to production. It positions itself around trust and enterprise readiness.

The company serves organizations that need to observe, evaluate, and rely on mission-critical agents, offering data-residency and collaboration features under a sales-led commercial model.

Product features

HoneyHive unifies distributed tracing, offline and online evaluation, dataset management, and production monitoring, letting teams debug, measure quality, and catch regressions across the lifecycle.

It supports both automated evaluators and human-in-the-loop review, integrates via SDKs and OpenTelemetry, and emphasizes data-residency for regulated enterprises.

Target market

HoneyHive targets enterprises and cross-functional AI teams building production LLM applications and agents that require rigorous evaluation, monitoring, and data control.

Buyer personas

End users

AI engineers, product managers, and subject-matter experts evaluating and monitoring agents.

Buyers

Engineering and AI leaders adopting LLMOps and observability tooling.

Key influencers

ML and platform practitioners focused on evaluation and reliability.

Ideal customer profile

Enterprises running production agents that need evaluation rigor, monitoring, and data-residency control.

Funding & performance

HoneyHive is a venture-backed startup; specific funding details are not prominently public. Verify with the vendor or funding databases.

Pros & cons

Pros

End-to-end tracing, evaluation, and monitoring
Enterprise-grade data-residency options
Collaboration across engineering and SMEs
Human-in-the-loop plus automated evaluators
OpenTelemetry-based instrumentation
Free tier for getting started

Cons

Pricing is sales-led and not fully public
Production use pushes toward Enterprise plans
Less community mindshare than open-source rivals
Text/LLM-focused rather than broad APM
Setup requires instrumentation effort

Pricing plans

Free

$0 / month

Getting-started tracing
Basic evaluation
Trial access
Limited usage

Enterprise

Custom

Production monitoring
Data-residency options
SSO and governance
Dedicated support
Custom usage limits

Key features

API

Team collaboration

Self-hosted

Integrations

OpenAI, Anthropic, LangChain, LlamaIndex, OpenTelemetry

Input types

text

Output types

text

Best For

LLM app observability, agent evaluation, production monitoring

Compare key features

View all alternatives →

Feature

HoneyHive

Vellum

Helicone

Pricing

Freemium

Free plan

Yes

Free trial

Yes

API

Yes

Self-hosted

Yes

Team support

Yes

Frequently asked questions

What does HoneyHive do?+

It provides observability and evaluation for LLM applications and agents, including tracing, automated and human evaluation, testing, and production monitoring in one platform.

How much does HoneyHive cost?+

HoneyHive uses sales-led pricing. There is a free tier for trials, but production and enterprise use require a custom quote from their team.

Does HoneyHive support data residency?+

Yes. HoneyHive emphasizes enterprise readiness including data-residency options, appealing to regulated organizations.

Can HoneyHive evaluate agents?+

Yes. It supports evaluating agent behavior with automated evaluators and human-in-the-loop review, plus tracing of tool calls and reasoning steps.

How do I instrument my app?+

You can use the HoneyHive SDK or OpenTelemetry-based instrumentation to capture traces of prompts, tool calls, and outputs.