Powerful observability & analytics, purpose-built for LLMs

Analyze performance and user feedback from your application in production to detect anomalies, address issues, and drive continuous improvement.

Monitoring

The path to improving performance starts by measuring it.

User feedback & actions

Capture implicit or explicit feedback from your users to track effectiveness, performance, and user experience across your LLM apps.

Filters & metadata

Slice and dice your data across segments, compare performance across user cohorts, and get detailed insights into application performance.

Self-serve analytics

Add saved charts with custom metrics and filters to your team dashboard to quickly get insights on the questions that matter.

Online evaluations

Compute integrity and performance metrics across your data to detect LLM failures in production.

Cluster & topic analysis

Log embeddings from your production data to analyze common themes, topics, and clusters of interest across user queries.

Async logging

Log application data synchronously and asynchronously, depending on your specific needs. No proxy required.

Get deep visibility into performance and failures

LLMs often lead to unexpected failures in production. HoneyHive allows you to monitor your LLM apps with quantitative rigor and get actionable insights to continuously improve your app.

Log LLM application data with just a few lines of code

Enrich logs with user feedback, metadata, and user properties

Query logs and save custom charts in your team dashboard

Curate fine-tuning datasets from your production data

HoneyHive enables you to filter and label your data from production to curate fine-tuning datasets for continuous improvement.

Filter and add underperforming test cases from production

Invite domain experts to annotate and provide ground truth labels

Manage and version fine-tuning datasets across your project

Run evaluations on your live or historical data

Every use-case is unique. HoneyHive allows you to define your own evaluators and guardrails to build custom test suites for your app.

Evaluate faithfulness and context relevance across RAG pipelines

Write assertions to validate JSON structures or SQL schemas

Implement moderation filters to detect PII leakage and unsafe responses

Semantically analyze text for topic, tone, and sentiment

Calculate NLP metrics such as ROUGE-L or METEOR

Instrument your app with our customizable SDK

Sync and async logging. Does not require proxying your requests via our servers.

Any model, any framework, any cloud. Works with any model, orchestration framework, or GPU cloud.

Event-centric data model. Helps you monitor your LLM chains, agents, and RAG pipelines with end-to-end visibility.

Continuously improve your LLM-powered products.