HoneyHive is an observability and evaluation platform tailored for AI developers, ML engineers, and product teams building complex AI applications. Designed to increase the reliability of systems like large language models (LLMs), retrieval-augmented generation (RAG) pipelines, and multi-agent frameworks, HoneyHive enables organizations to confidently assess, debug, and maintain AI systems at scale. With features for evaluation, tracing, dataset management, and collaboration, it supports the full AI lifecycle from development to production.

What are the main features of HoneyHive?

Comprehensive evaluation framework

HoneyHive allows AI teams to rigorously test model performance through a structured, repeatable process:

• Supports both automated metrics and human reviews.

• Custom metrics can be defined for task-specific evaluations.

• Regression testing helps detect performance drops over time.

• Seamlessly integrates with CI/CD pipelines for ongoing quality control.

Advanced observability tools

The platform provides deep visibility into AI system behavior in real-world usage:

• Distributed tracing (via OpenTelemetry) captures detailed metadata for each AI interaction.

• Session replay helps debug errors by visualizing system execution step-by-step.

• Custom dashboards offer monitoring of performance trends and KPIs.

• Configurable alerts and safety guardrails ensure rapid response to critical issues.

Dataset management and curation

HoneyHive streamlines the creation and control of evaluation and training datasets:

• Enables curation of datasets from real user logs.

• Built-in labeling and annotation tools support expert involvement.

• Tracks dataset versions to maintain consistency across training runs.

Artifact and prompt management

Collaboration and versioning tools are built in to support prompt engineering and evaluator development:

• Prompts and evaluators can be version-controlled in team environments.

• Code-based or LLM-based evaluators can be authored and reused.

• Git integration simplifies workflow integration for development teams.

Flexible deployment options

HoneyHive offers adaptable deployment modes for different security and infrastructure requirements:

• Multi-tenant SaaS for quick adoption.

• Dedicated cloud environments for larger organizations.

• Self-hosted option for teams requiring full control over infrastructure.

Why choose HoneyHive?

• Boosts reliability: Continuous evaluation and observability prevent performance regressions in live AI systems.

• Streamlines collaboration: Shared dashboards, prompt libraries, and labeling tools improve cross-functional teamwork.

• Adapts to scale: Works effectively across simple single-agent systems to complex, distributed AI architectures.

• Fits existing workflows: Compatible with modern development stacks and flexible deployment models.

• Reduces debugging time: Fine-grained tracing and monitoring tools help detect and fix issues early in the development cycle.