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opik Review
Opik is an open-source logging, debugging, and optimization platform for AI agents and LLM applications.
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.
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opik at a Glance
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About opik
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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'.
quick facts
Quick Facts
| Attribute | Value |
|---|---|
| Developer | Comet |
| Business Model | Freemium-SaaS |
| Pricing | Freemium (includes a free tier) |
| Platforms | Web, API, Self-hosted (Docker/Kubernetes) |
| API Available | Yes |
| Integrations | OpenClaw, Gemini 3.1, Claude Sonnet 4.6, OpenAI TTS, Ollama, Pytest |
| Founded | Not publicly specified |
| HQ | New York, USA |
| Funding | Series A ($20 million) |
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.
- 1Comprehensive tracing and logging of LLM calls, inputs, outputs, token usage, latency, and cost across complex workflows.
- 2Automated evaluation with over 30 built-in metrics for hallucination detection, RAG quality (context precision, answer relevance), and agent-specific scoring.
- 3Support for LLM-as-a-judge evaluations and human annotation queues.
- 4Versioned prompt storage, a playground for side-by-side testing, and AI-powered prompt refinement.
- 5Agent Optimization SDK with six algorithms to automatically tune prompts, parameters, and tool selection.
- 6Production monitoring through quality dashboards, tracking feedback scores, trace counts, token usage, and performance metrics in real-time.
- 7Guardrails to prevent risky outputs and PII anonymization for production deployments.
- 8A/B testing and regression testing capabilities for comparing models, prompts, or configurations.
- 9Integration of evaluation checks into CI/CD pipelines using 'model unit tests' with Pytest.
- 10Native 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.
- 1Developers and Data Scientists: For debugging, evaluating, and monitoring LLM applications throughout their lifecycle, from development to production.
- 2AI Engineers: For defining and computing evaluation metrics, scoring LLM outputs, and comparing performance across different models or prompts.
- 3MLOps Teams: For tracking LLM performance in real-time, detecting issues like hallucinations, and ensuring application quality in production.
- 4Prompt Engineers: For automated prompt engineering, agent optimization, and managing versioned prompts.
- 5Quality 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.
- 1Free Tier: Includes core features for development and evaluation of LLM applications.
- 2Paid Tiers: Available for production-scale monitoring, advanced features, and increased usage (specific pricing details require direct inquiry).
competitors
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.
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LangSmith↗
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.
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Langfuse↗
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.
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Arize AI (Phoenix)↗
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.
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Braintrust↗
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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Frequently Asked Questions
+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.
+Is opik free?
Yes, opik offers a freemium model that includes a free tier. This tier provides core features for development and evaluation of LLM applications. Paid tiers are available for production-scale monitoring and advanced features, though specific pricing details for these tiers are not publicly disclosed.
+What are the main features of opik?
The main features of opik include comprehensive tracing and logging for LLM workflows, automated evaluation with over 30 built-in metrics, prompt management and optimization, real-time production monitoring with quality dashboards, A/B testing, regression testing, and integration with CI/CD pipelines via 'model unit tests'. It also offers an Agent Optimization SDK and native OpenClaw observability.
+Who should use opik?
Opik is intended for developers, data scientists, AI engineers, MLOps teams, and prompt engineers working with Large Language Models, Retrieval-Augmented Generation (RAG) systems, and agentic workflows. It supports the entire LLM lifecycle, from debugging during development to monitoring in production.
+How does opik compare to alternatives?
Opik distinguishes itself from competitors like LangSmith, Langfuse, Arize AI (Phoenix), and Braintrust through its comprehensive LLM lifecycle support, automated optimization capabilities via its Agent Optimizer, and its open-source component. While competitors may offer deep integrations (LangSmith), emphasize data ownership (Langfuse), or focus on enterprise-grade telemetry (Arize AI), opik provides an all-in-one platform for tracing, evaluation, and monitoring with a strong emphasis on automated prompt and agent optimization.