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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

Athina’s core evaluation framework is open source and can be used to run the same evaluations in development, CI / CD, and production.See it on Github: athina-evals
Athina provides a complete observability platform for your LLM application:
  • Detailed trace inspection
  • Manual annotation capabilities
  • Unified online/offline metrics
  • PagerDuty and Slack integrations
  • Data export functionality
  • API/GraphQL access
Evaluate your LLM applications in production with confidence:
Maximize evaluation coverage while minimizing costs:
  • Smart sampling strategies
  • Configure evals to run on only a subset of logs based on filters
  • Comprehensive cost tracking and optimization
  • Configurable evaluation frequency
Comprehensive evaluation capabilities:
  • Rich library of 50+ preset evals
  • Customizable evaluation configurations
  • Build and deploy custom evals
  • Multiple model provider support
  • Seamless integration with popular eval libraries (Ragas, Guardrails, etc)
Fully automated evaluation pipeline:
  • Scalable evaluation infrastructure
  • Centralized eval configuration management
  • Smart eval-to-prompt matching
  • Intelligent rate limiting
  • Multi-provider model support
  • Historical log evaluation capabilities
Seamlessly adapt to any LLM stack:
  • Multi-provider support (OpenAI, Gemini, etc)
  • Framework-agnostic (Langchain, Llama Index, custom)
  • Complex trace and agent support
  • Flexible architecture adaptation
  • Standardized evaluation layer
Deep insights into your LLM application:
  • Application performance metrics
  • Retrieval quality analytics
  • Resource utilization tracking
  • Safety and security monitoring
  • Temporal analysis
  • Statistical distribution insights
  • Multi-dimensional segmentation
Enterprise-ready collaboration features:
  • Team workspaces
  • Role-based access control
  • Workspace isolation
  • Shared evaluation insights

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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.