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Evaluating logs in production is the only way to know if your LLM application is working correctly in the real world.
Online evals are a critical part of running a successful LLM application. They allow you to measure the quality of your LLM application over time, detect performance and safety issues, and prevent regressions.

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Why use Athina for Online Evals?

  • 50+ preset evals
  • Support for custom evals
  • Support for popular eval libraries like Ragas, Guardrails, etc
  • Sampling: sample a subset of logs
  • Filtering: only run on logs WHERE X is true
  • Rate limiting: intelligent throttling to avoid rate limiting issues with your LLM provider
  • Use any model provider for LLM evals
  • View aggregate analytics
  • View traces with eval results
  • Track eval results over time

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How does it work?

This is a simplified view of the architecture used to run evals on logged inferences in production at scale.

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Key Features

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👋 Athina

We spent a lot of time working through these problems so you don’t need a dedicated team for this. You can see a demo video here. Website: Athina AI (Try our sandbox ). Sign Up for Athina. Github : Run any of our 40+ open source evaluations using our Python SDK to measure your LLM app.