Flexible Plans for All Teams

Systematically record, compare, and analyze your machine learning training runs to accelerate development and improve model performance.

Create tailored visualizations using Python to customize your Comet dashboards, enhancing data insights for your specific needs.

Model Registry centralizes and organizes machine learning models, enabling versioning, tracking, and seamless deployment across your team’s workflow.

Track and version training datasets, linking them to experiments for complete model lineage and reproducibility.

Optimize model performance with automated hyperparameter tuning, tracking all results within the Comet platform.

Monitor and analyze the performance of your language models during development and in production, capturing inputs, outputs, and metadata for each inference.

Manage and version collections of prompts and examples used for evaluating LLM application performance, ensuring consistent and reproducible assessments.

Track your LLM applications in production with our production ready LLM Evaluation platform.

Automatically identify data drift for your Machine Learning models in production, helping maintain model performance over time.

Visualize and monitor input feature distributions to detect shifts and anomalies in your data.

Create and track tailored performance metrics using SQL queries on your logged data.

Set up custom notifications for critical events via Slack, email, and other channels.

Choose from multiple deployment options, including cloud-based, on-premises, and fully managed solutions to fit your infrastructure needs.

Streamline authentication with enterprise-grade SSO, supporting OAuth 2.0, SAML, and LDAP protocols.

Create dedicated machine-to-machine accounts for secure, automated data logging and API access.

Grant read-only access to team members for data exploration without the ability to modify or log new data.

Access tiered support options including email, Slack, phone, and dedicated team assistance based on your plan.

A span represents a single tracked operation within your LLM pipeline, such as a model request, a function call or an agent action.

Increase span data retention from 60 days to 400 days.