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Opik

Opik

Evaluate, test, and ship LLM applications with a suite of observability tools to calibrate language model outputs across your dev and production lifecycle.

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What is Opik?

Opik is an end-to-end LLM evaluation platform built by Comet that enables AI developers to systematically test, evaluate, and monitor language model applications throughout their entire lifecycle. The platform provides observability tools designed to calibrate and validate LLM outputs across development, staging, and production environments. Opik addresses a critical challenge in LLM development: ensuring consistent, reliable, and safe model behaviour before and after deployment. It's particularly valuable for teams building production-grade AI applications who need to move beyond manual testing and implement rigorous evaluation frameworks. By offering integrated evaluation, testing, and monitoring capabilities, Opik helps developers reduce hallucinations, improve output quality, and maintain model performance over time as data distributions shift.

Key Features

LLM Evaluation Suite

thorough tools for testing language model outputs against custom criteria and benchmarks

Observability Across Lifecycle

Monitor and track model behaviour from development through production deployment

Output Calibration

Systematically evaluate and improve language model responses using metrics and scoring

Integration Support

Connect with popular LLM frameworks and development workflows via APIs

Comparative Analysis

A/B test different models, prompts, and configurations to identify best performance

Quality Metrics

Built-in and customizable evaluation metrics to assess accuracy, safety, and user satisfaction

Pros & Cons

Advantages

  • Addresses critical need for rigorous LLM testing before production deployment
  • Freemium model allows teams to get started without immediate investment
  • Provides continuous monitoring capabilities to catch performance degradation in production
  • Supports data-driven decision making for model and prompt optimization

Limitations

  • Learning curve required to set up thorough evaluation frameworks and metrics
  • Effectiveness depends on quality of evaluation criteria and test data defined by users
  • May require integration work with existing development pipelines and tools

Use Cases

Pre-production evaluation: Systematically test LLM applications before shipping to ensure quality and safety

Prompt optimization: Compare different prompts and configurations to identify the best performing variants

Continuous monitoring: Track model performance in production and alert teams to quality degradation

Regulatory compliance: Maintain audit trails and evaluation records for compliance and governance

Model comparison: Evaluate different LLM providers or fine-tuned models to select the best option

Pricing

FreeFree

Basic evaluation tools, community access, limited evaluation runs and storage

ProCustom pricing

Advanced evaluation features, priority support, higher usage limits, team collaboration

EnterpriseCustom pricing

Dedicated support, custom integrations, advanced security features, SLA guarantees

Quick Info

Pricing
Freemium
Platforms
Web, API
Categories
Design, Developer Tools, Code

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