HoneyHive is an observability and evaluation platform that utilizes OpenTelemetry for tracing, automated evaluations, and real-time monitoring of AI applications. It enables teams to debug, assess quality, and optimize performance of their AI products, ensuring reliability and accuracy throughout the development and deployment process.
Funding
Funding not disclosed
Founders
Product
Problem
AI applications often lack sufficient observability, making it difficult to trace data flow, debug errors, and evaluate performance effectively. Existing tools may not provide the necessary insights into the complex interactions within AI systems, hindering optimization and quality assurance.
Solution
HoneyHive is an AI observability and evaluation platform that leverages OpenTelemetry to provide end-to-end tracing, automated evaluations, and real-time monitoring for AI applications. The platform enables AI engineering teams to debug issues, measure quality and accuracy, and continuously improve prompts and models in both UI and code. By providing comprehensive visibility into AI application performance, HoneyHive helps teams ship AI products with greater confidence and reliability.
Target Audience
HoneyHive targets AI engineering teams, including startups and Fortune 100 enterprises, building and deploying LLM-powered applications who need to ensure quality, performance, and reliability.
Features
- OpenTelemetry-native tracing for auto-instrumentation of 15+ frameworks, model providers, and vector databases
- Automated evaluations to benchmark applications against datasets and identify improvements or regressions
- Real-time monitoring of key metrics with customizable alerts for critical failures
- Prompt management and version control synced between UI and code, with Git integration
- Dataset curation, labeling, and versioning across projects
- Custom evaluators using LLMs or code to measure quality and performance
- Online evaluation to run asynchronous evals on traces in the cloud
- Support for logging up to 2M tokens per span and monitoring large-context requests