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opik Review

Opik is an open-source logging, debugging, and optimization platform for AI agents and LLM applications.

shipped Apr 17, 2026updated May 27, 2026aifreemium
ai
opik - AI tool

Why it matters

1Offers a free tier for development and evaluation of LLM applications.
2Compliant with ISO/IEC 27001:2022, ISO 9001:2015, and SOC 2 Type 2 standards.
3Secured $20 million in Series A funding.
4Provides an open-source component (Apache 2.0 license) and supports self-hosting via Docker Compose or Kubernetes.

Stork’s verdict on opik

Opik provides comprehensive LLM observability and agent optimization, but its extensive feature set might be overkill for simpler applications.

opik reviewed by Stork AI · stork.ai/en/opik

About opik

Business Model
Freemium SaaS
Headquarters
New York, USA
Team Size
51-100
Funding
Series A
Total Raised
$20 million
Target Audience
Developers and data scientists working with AI applications
API DocsGitHubOpen Source

overview

What is opik?

opik is an open-source logging, debugging, and optimization platform for AI agents and LLM applications developed by Comet. It enables developers and data scientists to debug, evaluate, and monitor LLM applications, RAG systems, and agentic workflows. Opik serves as Comet's comprehensive platform for LLM observability, evaluation, and monitoring, supporting the entire LLM lifecycle from development to production. It provides tools for tracing complex LLM workflows, automating evaluations with over 30 built-in metrics, managing and optimizing prompts, and monitoring performance in real-time. The platform is designed to facilitate the building, testing, and optimization of generative AI applications, including integration with CI/CD pipelines through 'model unit tests'.

features

Key Features of opik

Opik provides a comprehensive suite of features designed to support the development, evaluation, and deployment of Large Language Model applications and agentic systems.

  • Comprehensive tracing and logging of LLM calls, inputs, outputs, token usage, latency, and cost across complex workflows.
  • Automated evaluation with over 30 built-in metrics for hallucination detection, RAG quality (context precision, answer relevance), and agent-specific scoring.
  • Support for LLM-as-a-judge evaluations and human annotation queues.
  • Versioned prompt storage, a playground for side-by-side testing, and AI-powered prompt refinement.
  • Agent Optimization SDK with six algorithms to automatically tune prompts, parameters, and tool selection.
  • Production monitoring through quality dashboards, tracking feedback scores, trace counts, token usage, and performance metrics in real-time.
  • Guardrails to prevent risky outputs and PII anonymization for production deployments.
  • A/B testing and regression testing capabilities for comparing models, prompts, or configurations.
  • Integration of evaluation checks into CI/CD pipelines using 'model unit tests' with Pytest.
  • Native OpenClaw Observability plugin for insights into LLM calls, tool execution, memory steps, and agent handoffs.

use cases

Who Should Use opik?

Opik is primarily designed for developers and data scientists who are building, testing, and deploying AI applications, particularly those involving Large Language Models, Retrieval-Augmented Generation (RAG) systems, and agentic workflows.

  • Developers and Data Scientists: For debugging, evaluating, and monitoring LLM applications throughout their lifecycle, from development to production.
  • AI Engineers: For defining and computing evaluation metrics, scoring LLM outputs, and comparing performance across different models or prompts.
  • MLOps Teams: For tracking LLM performance in real-time, detecting issues like hallucinations, and ensuring application quality in production.
  • Prompt Engineers: For automated prompt engineering, agent optimization, and managing versioned prompts.
  • Quality Assurance Teams: For testing LLM applications with 'model unit tests' and integrating evaluation into CI/CD pipelines.

pricing

opik Pricing & Plans

Opik operates on a freemium business model, offering a free tier that includes core features for development and evaluation. This allows users to get started with logging, debugging, and basic evaluation of their LLM applications without an initial investment. For production-scale monitoring, advanced features, and higher usage limits, Comet provides paid tiers. Specific pricing details for these paid tiers are not publicly disclosed on the Opik documentation or primary website, requiring direct inquiry for enterprise-level solutions.

  • Free Tier: Includes core features for development and evaluation of LLM applications.
  • Paid Tiers: Available for production-scale monitoring, advanced features, and increased usage (specific pricing details require direct inquiry).

Similar Tools

opik vs Competitors

Opik operates within a competitive landscape of LLM observability and evaluation platforms, distinguishing itself through its comprehensive lifecycle support, automated optimization capabilities, and open-source component.

1
LangSmithOn StorkCompare

Provides deep, native integration and comprehensive tracing for applications built with LangChain and LangGraph, offering a unified platform for observability, evaluations, and prompt engineering.

Similar to opik in offering tracing, evaluation, and monitoring for LLM applications and agents. LangSmith is particularly strong for users within the LangChain ecosystem, providing seamless integration and AI-powered debugging features. It offers a free tier with 5,000 traces a month.

2
LangfuseOn StorkCompare

An open-source and self-hostable LLM observability platform that provides full data ownership, detailed logging for traces, and prompt management.

Like opik, Langfuse offers tracing and evaluation capabilities for LLM applications. Its open-source nature and self-hosting option differentiate it, appealing to teams prioritizing data control, whereas opik is described as a freemium managed service. Langfuse has a free self-hosted version and cloud plans starting at $29 per month.

3
Arize AI (Phoenix)On StorkCompare

Offers enterprise-grade ML telemetry and LLM observability, built on OpenTelemetry and OpenInference standards, providing vendor-agnostic tracing and advanced evaluation capabilities including embedding clustering and drift detection.

Arize AI, similar to opik, provides comprehensive observability, evaluation, and debugging for LLM applications and agents. It stands out with its focus on enterprise-scale telemetry, open standards, and advanced ML monitoring features, which might cater to a larger, more established ML engineering audience than opik. Phoenix is its open-source component.

4
BraintrustOn StorkCompare

An end-to-end platform that integrates LLM production monitoring, AI quality evaluation, and experimentation in a single solution, with strong support for complex multi-step agent workflows.

Braintrust offers a similar all-in-one approach to opik for monitoring, evaluation, and debugging LLM applications. It emphasizes a complete debugging workflow, including converting production failures into evaluation datasets and validating changes through CI/CD, which might offer a more integrated development-to-production loop than opik. It has a free tier with 1M trace spans and 10K scores.

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