HoneyHive
Monitor and improve AI application performance throughout development cycles
Target Audience
- AI Engineering Teams
- ML Operations Engineers
- Enterprise AI Developers
- LLM Application Builders
Hashtags
Overview
HoneyHive helps teams build better AI applications by providing tools to track, test, and manage every aspect of their AI systems. It enables collaboration between engineers and domain experts to maintain quality control from development through production. The platform focuses on catching errors early and ensuring reliable performance at scale.
Key Features
AI Tracing
End-to-end visibility into AI workflows using OpenTelemetry
Automated Evaluations
Run large test suites with every code commit
Production Monitoring
Track cost, latency, and quality metrics in real-time
Prompt Versioning
Manage and track prompt changes across teams
Team Collaboration
Central platform for engineers and domain experts
Use Cases
Debug complex AI agent workflows
Test model changes with automated evaluations
Monitor production LLM performance
Manage version-controlled prompts
Collaborate on human-in-the-loop reviews
Pros & Cons
Pros
- Comprehensive evaluation tools for LLM development
- OpenTelemetry integration for distributed tracing
- Enterprise-grade security and compliance
- Supports 100+ AI models and frameworks
Cons
- Focuses on enterprise-scale needs (may be overkill for small projects)
Frequently Asked Questions
What frameworks does HoneyHive support?
Integrates with any framework through OpenTelemetry, including 15+ pre-instrumented AI frameworks and vector databases
How do you ensure data security?
SOC-2 compliant with options for private cloud deployment and GDPR-aligned practices
Can non-engineers use this platform?
Yes, domain experts can review outputs and collaborate via web UI while engineers work in code
Integrations
Reviews for HoneyHive
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