Opik

What is Opik?

Opik is a tracing and evaluation platform for LLM applications, RAG systems, and agent workflows. It logs interactions across these systems and connects with tools such as LangChain, LlamaIndex, and OpenAI through Python SDK decorators or pre-built connectors. Opik also supports automated evaluation metrics, experiment comparison, unit testing, and production monitoring dashboards across the development-to-production lifecycle. It is for AI engineers, data scientists, agent developers, and LLM application teams building production-grade systems.

Key Features

• Tracing: Captures detailed execution traces for LLM applications, RAG systems, and agentic workflows, so teams can replay runs, inspect intermediate steps, and find failures or slow points in Opik.
• Automated Evaluations: Runs predefined or custom evaluation metrics on traces, including relevance scoring, faithfulness checks, and LLM-as-judge prompts, so users can benchmark model, retrieval, and agent performance without manual review.
• Production Dashboards: Shows real-time dashboards for tracing data, evaluation results, and performance metrics, so teams can track application health, latency trends, and error rates in one place.
• Filtering and Search: Includes filtering and search tools in dashboards, so users can narrow large volumes of tracing and evaluation data and inspect specific runs faster.
• Batch Processing: Supports batch processing for evaluations, so teams can assess many traces at once instead of reviewing runs one by one.

Strengths and Weaknesses

Strengths:

• Trustpilot shows a 1.5 out of 5 rating based on 10 reviews, and the research notes cross platform discrepancies in sentiment data (Trustpilot, 2026).
• One Trustpilot reviewer reports a positive product experience, saying the "Mova P50 Pro Ultra" cleaned well and was priced below competing options (Trustpilot reviewer, 2026-03-04).

Weaknesses:

• Trustpilot reviewers frequently report poor customer service and limited support. One review states "Assistenza inesistente" (Trustpilot reviewer, 2025-10).
• Delayed responses to return requests appear in multiple reviews. One reviewer says they requested a return on 2025-12-29 and only received a reply on 2026-01-23 after repeated follow-ups and a threat of legal action (Trustpilot reviewer, 2026-02-16).
• Some reviewers say they received the wrong product and then could not complete the return. One review says, "arrivato prodotto diverso da quello che avevo ordinato" and "mai nessuna risposta" after starting the return (Trustpilot reviewer, 2026-01-19).
• Reviewers also mention missing shipment updates, late deliveries, and no clear contact path for online order issues, including no tracking information and no response by email (Trustpilot reviewers, 2025-12-24; 2026-01-05).

Pricing

• Free: $0 per month. Unlimited team members, LLM tracing, datasets and experiments, and LLM-as-a-judge metrics. Limits are 25k spans per month and 60-day data retention.
• Pro: $39 per month. Includes everything in Free, plus pay-as-you-go pricing for additional monthly spans and an option for longer data retention. Base limits are 100k spans per month and 60-day data retention.
• Enterprise: Pricing based on needs (contact sales). Includes everything in Pro, plus flexible deployments, service accounts, view-only users, single sign-on (SSO), dedicated support, and SLAs. Includes unlimited team members and unlimited traces.

Pricing was not publicly disclosed on the official Comet pricing page in the available sources as of April 2026. The tier details above come from a third-party mirror.

Who Is It For?

Ideal for:

• AI engineers and data scientists at mid-market or enterprise companies: Opik fits teams building LLM apps that need to debug traces and evaluate full agent conversations. It supports thread-level logging and LLM-as-a-Judge workflows for production monitoring.
• Agent developers at growth-stage or scale-up companies: It suits teams running production workflows where end-to-end session evaluation matters, not just trace inspection. It also supports collecting subject matter expert feedback for auditable AI systems.
• ML engineers working on RAG and agent systems: Opik is a match for 5 to 50 person AI and ML teams using OpenAI or Anthropic APIs, LlamaIndex, or LangChain. It focuses on tracing, monitoring, and evaluating complex workflows without custom training infrastructure.

Not ideal for:

• Non-technical product managers or solo founders: It lacks a no-code UI for quick prototypes, so tools like LangSmith or Promptfoo are a better fit.
• Teams training custom foundation models from scratch: Opik focuses on LLM app observability rather than experiment tracking, so Comet's core ML platform or Weights & Biases fits that use case better.

Use Opik if your team is building and monitoring production LLM apps, RAG systems, or agents and needs open-source tracing, automated evals, and auditable feedback loops. Skip it if you want simple prompt testing, casual experiments, or tooling for custom model training rather than LLM application observability.

Alternatives and Comparisons

• Arize Phoenix: Opik does end to end LLM evaluations, experiment tracking, and production monitoring better within Comet's unified AI developer platform. Arize Phoenix does free, lightweight visualization and troubleshooting better, and it supports OpenTelemetry for quick dataset analysis without vendor lock in. Choose Opik if you need integrated tracking for production scale LLM apps; choose Arize Phoenix if you want open source flexibility for prototyping and debugging. Switching difficulty is medium based on the available research.

• Maxim: Opik does broader AI developer workflows better because it combines evaluations with experiment tracking in the Comet ecosystem. Maxim does agent specific work better, with pre release and post release testing plus dataset creation tools for teams building and testing AI agents at scale. Choose Opik if you need general LLM observability tied to Comet; choose Maxim if your work centers on agent heavy deployments.

• Braintrust: Opik does end to end AI development workflows better because it ties evaluations to Comet's experiment tracking and includes production monitoring. Braintrust does human review and gateway related work better, with customizable human in the loop evaluations and gateways for tracing and cost attribution. Choose Opik if you want evaluations connected to tracking inside Comet; choose Braintrust if advanced human review or gateway features matter more.

Getting Started

Setup:

• Signup: Opik signup requires email only, and a credit card is not required based on the available data.
• Time to first result: The dashboard starts empty, setup needs an API key, and the reported time to first result is 5 minutes.

Learning curve:

• The learning curve is low for API-savvy users. You should be familiar with API keys and LLM tracing before setup.
• Beginner: 1 to 2 hours to reach basic proficiency. Experienced: minutes.

Where to get help:

• Official learning material is limited to an onboarding sessions README on GitHub: https://github.com/comet-ml/opik-onboarding-sessions/blob/main/README.md
• We found no Discord, Slack, forum, GitHub Discussions, email, or live chat support channels in the research data.
• Community support appears nonexistent in the available sources, and we found no third-party guides or community content.

Watch out for:

• Incorrect API key pasting is a reported stumbling block.
• Missing the project name during config can block setup.

Integration Ecosystem

Public information suggests a limited integration picture for Opik. Based on available research, we did not find user reports that confirm active use of specific integrations, and we did not find clear user feedback on integration quality. Documentation points to an SDK-based approach for LLM observability, and no MCP server availability was noted.

• User-reported integrations: We did not find public user reports that name specific integrations in active use.
• Integration quality feedback: We did not find public praise or complaints about how integrations work.
• SDK-based approach: Available documentation suggests an SDK-based setup rather than a widely discussed plug-and-play integration ecosystem.

We also did not find consistent user requests for missing integrations in the available research. Public documentation and user discussion appear limited in this area.

Developer Experience

Opik is a tracing and evaluation platform for LLM applications, with a Python SDK as the main developer interface and a REST API for programmatic access. The documentation appears recent and functional, but it is spread across multiple entry points, and some integration pages lag behind SDK updates. Developers report about 5 to 15 minutes to get basic tracing running after SDK install and API key setup, then another 10 to 20 minutes to get comfortable with the dashboard and trace output.

What developers like:

• Developers often point to unified tracing and evaluation in one platform.
• The Python API is described as type-safe, and it is the main interface users rely on.
• Reviewers note the dashboard is useful for debugging chains, and some framework integrations are reported to work with little setup.
• Public sources also mention active maintainers, a responsive community, and lower cost for small teams.

Common frustrations:

• Developers report breaking changes between SDK versions.
• Integration coverage is not complete across frameworks, and some related docs can lag behind SDK changes.
• Public feedback mentions error messages, debugging UX, and evaluation API complexity as recurring pain points.
• Some users also mention rate limits, quota transparency, and friction in self-hosted deployment.

Security and Privacy

• Audit logs: Audit logs are available, per the vendor's security information.
• Encryption in transit: The vendor states that encryption in transit is enabled.

Product Momentum

• Release pace: No public documentation shows recent releases or update frequency, and users report no visible momentum.
• Recent releases: No recent notable releases were identified in the available research, and no public changelog or roadmap was found.
• Growth: Current signals point to a stable trajectory, but the available research does not show a funding narrative or broader expansion signals.
• Search interest: Google Trends direction is unknown, with a +0.0% change across the measured period and interest at 0/100, with a peak of 0/100.
• Risks: Limited community perception and the absence of momentum signals raise questions about long term viability, and community expectations for dominance or rapid scaling appear low.

FAQ

What is opik?

Opik is an open-source, production-ready end-to-end LLM evaluation platform from Comet. It is used for tracing, monitoring, and evaluating AI agents and LLM applications.

What is the meaning of opik?

In this context, Opik refers to Comet's open-source platform for AI agent observability, evaluation, and debugging. It focuses on measuring agent performance, tracking costs, and supporting LLM systems in production.

What is Opik used for?

Opik is used to log interactions across LLM applications, RAG systems, and agentic workflows. It helps teams trace runs, monitor behavior, evaluate outputs, and debug issues during development, CI/CD, and production.

Does Opik support tracing for AI agents and LLM apps?

Yes. Opik captures detailed execution traces for LLM applications, RAG systems, and agentic workflows, including inputs, outputs, metadata, and intermediate steps.

Can Opik track latency and cost?

Yes. The Python SDK can track LLM calls and record latency and cost as part of tracing and monitoring.

How do you integrate Opik with an application?

Opik integrates with Python applications through an SDK. Public research also notes SDK-based tracing with Python decorators and APIs.

Is Opik open source?

Yes. Public research describes Opik as open source, and the project is available through Comet's GitHub repository.

Is Opik free?

Research shows a Free tier at $0 per month. Reported features on that tier include unlimited team members, LLM tracing, datasets and experiments, and LLM-as-judge evaluation.

Is Opik self-hosted or cloud-based?

Research indicates both options are relevant. Opik can be self-hosted with Docker, and there is also a hosted version tied to plan tiers.

How long does it take to get started with Opik?

The reported time to first result is 5 minutes. Setup starts from an empty dashboard and requires an API key.

What do you need to sign up for Opik?

Research indicates signup requires email only. A credit card is not required at signup based on the available data.

What is the rate limit for Opik?

Public sources do not clearly document rate limits. Research notes that self-hosting via Docker avoids cloud-based limits, while the hosted version may have quotas tied to plan tiers.

What is an OPIK form?

No OPIK form is documented in the available research about this tool. Opik is described as using SDK-based tracing with Python decorators and APIs instead of form-based inputs.