Comet
Comet brings together two product families on one platform: Opik for observing and evaluating generative AI applications and agents, and MLOps for machine-learning workflows. Opik traces execution paths, including context retrieval, model responses and tool calls. Its evaluation tools include test suites, datasets, experiments, and built-in LLM-as-a-judge and heuristic metrics. It lists integrations with more than 40 AI frameworks, model providers and gateways, including LangChain, OpenAI, Google ADK, LangGraph and CrewAI, and supports OpenTelemetry for applications written in Ruby, Java and other languages. Comet MLOps provides experiment tracking, dataset management, a model registry and production monitoring. Opik’s open-source version can be self-hosted; Enterprise lists flexible deployment options. Free Cloud allows up to 10 team members, 25k spans per month and 60-day data retention. Pro Cloud costs 19.00 USD per month, with limits of 50 team members and 100k spans monthly. MLOps Pro costs 19.00 USD per user/month. Comet was founded in 2017 and is headquartered in New York City.
Who it is for
Opik suits teams building applications or agents that make LLM calls. MLOps is designed for teams building and training machine-learning models.
What is good
- Traces context retrieval, model responses and tool calls
- Evaluation includes test suites, datasets and experiments
- Integrates with more than 40 AI frameworks and providers
- Open-source Opik can be self-hosted
What to know first
- Free Cloud limits teams to 10 members and 25k monthly spans
- Free Cloud retains data for 60 days
- MLOps Pro is 19.00 USD per user/month
Verdict
Comet covers both LLM observability and evaluation through Opik and machine-learning workflows through MLOps. Free plans are listed, while paid cloud and MLOps options begin at 19.00 USD at their stated billing terms.
Comet plans and pricing
All plansCompared on ML experiment tracking software
- Free plan
- Yes
- Paid from
- $19/user/mo
- Run comparison
- Yes
- Artifact tracking
- Yes
- Dataset versioning
- Yes
- Model registry
- Yes
- Deployment options
- hybrid
- API and SDK access
- Yes
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