HoneyHive

What is HoneyHive?

HoneyHive is an all-in-one AI observability and evaluation platform designed for modern teams developing and deploying AI agents in production. It serves as a comprehensive hub where engineering, product, and domain expert teams can collaboratively observe, evaluate, and improve the performance, reliability, and safety of their AI-powered applications. By providing deep visibility into AI workflows, HoneyHive helps organizations confidently build, monitor, and scale intelligent agents.

What are the Key Features of HoneyHive?

HoneyHive offers a unified platform for the entire Agent Development Lifecycle (ADLC), featuring several core modules:

• Traces & Distributed Tracing: Gain end-to-end visibility into any AI agent or workflow. Debug failures, understand execution paths, and standardize telemetry across 100+ LLMs and agent frameworks with native OpenTelemetry support.

• Monitoring & Alerts: Continuously track agent health and performance at scale. Set up online evaluations on live traffic, monitor key metrics like quality, latency, and cost, and receive real-time alerts for failures or performance drift.

• Experiments & Evaluations: Test and compare AI agents and workflows offline against curated datasets. Run automated evaluations, catch regressions before deployment, and integrate testing into your CI/CD pipeline.

• Agents & Playground: Visualize and debug complex multi-agent systems with graph and timeline views. Replay chat sessions in an interactive playground to understand agent behavior.

• Annotation Queues: Integrate human expertise into the development loop. Route flagged traces to domain experts for review, gather feedback, and curate high-quality datasets based on real-world business context.

• Custom Evaluators & Dashboards: Build custom LLM-as-a-judge or code-based evaluators. Create tailored dashboards and analytics to track the specific KPIs and business metrics that matter to your team.

How to Use HoneyHive?

Getting started with HoneyHive is straightforward. Teams can begin with a free tier to explore core functionalities. The platform integrates seamlessly into existing development workflows:

1. Integration: Instrument your AI applications using HoneyHive's OpenTelemetry-native SDKs or APIs, compatible with a vast ecosystem of LLMs and frameworks.

2. Observation: Immediately start seeing traces of your agents' execution in production within the HoneyHive dashboard. Use filters and search to analyze performance.

3. Evaluation: Set up online evaluations to monitor live traffic or create offline experiments to test new prompts, models, or workflows against benchmark datasets.

4. Collaboration: Use annotation queues to involve subject matter experts in reviewing edge cases and defining quality standards.

5. Optimization: Use insights from traces, evaluations, and human feedback to iteratively improve your AI agents' prompts, logic, and overall performance.

What is the Price for HoneyHive?

HoneyHive offers a ### free tier to get started, allowing users to explore its observability and evaluation features. For teams requiring higher volumes, advanced security, enterprise support, and self-hosting options, HoneyHive provides scalable subscription plans. Specific pricing details are available upon request via their website or by scheduling a demo.

Helpful Tips for Using HoneyHive

• Start with Traces: Begin by integrating tracing to get a baseline understanding of your agents' behavior and identify any obvious failure points or inefficiencies.

• Leverage Open Standards: Utilize the OpenTelemetry-native approach to ensure vendor flexibility and future-proof your instrumentation across different tools and frameworks.

• Involve Domain Experts Early: Use the Annotation Queues feature to incorporate feedback from non-technical stakeholders (e.g., customer support, legal, product managers) early in the development cycle to ensure your AI aligns with business goals.

• Automate Your Testing: Integrate HoneyHive's evaluation suites into your CI/CD pipeline to automatically catch regressions every time you make a change to your AI application.

• Define Business-Centric Metrics: Go beyond technical metrics like latency. Use custom evaluators and dashboards to track KPIs that directly relate to user satisfaction and business outcomes.

Frequently Asked Questions about HoneyHive

Is HoneyHive secure and compliant for enterprise use?

Yes. HoneyHive is SOC 2 Type II certified and compliant with GDPR and HIPAA regulations. It offers enterprise-grade security features including fine-grained RBAC (Role-Based Access Control), SAML/SSO, and options for hybrid or fully self-hosted deployments to meet stringent security and data sovereignty requirements.

Which AI frameworks and models does HoneyHive support?

HoneyHive is OpenTelemetry-native and works across a wide ecosystem, supporting over 100+ Large Language Models (LLMs) and popular agent frameworks like LangChain, LlamaIndex, and others, providing flexibility regardless of your tech stack.

Can I use HoneyHive for both online (production) and offline (development) evaluation?

Absolutely. HoneyHive is built for the entire agent lifecycle. You can run ### online evaluations on live production traffic to detect issues in real-time and set up ### offline experiments to test new versions of your agents against datasets before deployment.

How does HoneyHive handle data privacy?

HoneyHive gives you control over your data. You can choose a deployment model that suits your needs, from multi-tenant SaaS to full self-hosting. The platform is designed with privacy in mind, ensuring your proprietary prompts, model outputs, and user data are handled according to your compliance standards.

My team includes non-engineers. Can they use HoneyHive?

Yes. HoneyHive is designed for cross-functional collaboration. Features like the intuitive Playground for testing, visual trace debugging, and the user-friendly Annotation Queues interface allow product managers, domain experts, and other stakeholders to participate directly in the evaluation and improvement process without writing code.