MLflow 3.10.0
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Daniel Lok
Feb 24, 2026, 1:06:30 AM (9 days ago) Feb 24
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We're excited to announce the release of MLflow 3.10.0! MLflow 3.10.0 is a major release that enhances MLflow's AI Observability and evaluation capabilities, while also making these features easier to use, both for new users and organizations operating at scale. This release brings multi-workspace support, evaluation and simulation for chatbot conversations, cost tracking for your traces, usage tracking for your AI Gateway endpoints, and a number of UI enhancements to make your apps and agent development much more intuitive. Major features include:
1. 🏢 Workspace Support in MLflow Tracking Server. MLflow now supports multi-workspace environments. Users can organize experiments, models, prompts, with a coarser level of granularity and logically isolate them in a single tracking server.
2. 💬 Multi-turn Evaluation & Conversation Simulation. MLflow now supports multi-turn evaluation, including evaluating existing conversations with session-level scorers and simulating conversations to test new versions of your agent, without the toil of regenerating conversations. Use the session-level scorers introduced in MLflow 3.8.0 and the brand new session UIs to evaluate the quality of your conversational agents and enable automatic scoring to monitor quality as traces are ingested.
3. 💰 Trace Cost Tracking. Gain visibility into your LLM spending! MLflow now automatically extracts model information from LLM spans and calculates costs, with a new UI that renders model and cost data directly in your trace views. Additionally, costs are aggregated and broken down in the "Overview" tab, giving you granular insights into your LLM spend patterns.
4. 🎯 Navigation bar redesign. As we continue to add more features to the MLflow UI, we found that navigation was getting cluttered and overwhelming, with poor separation of features for different workflow types. We've redesigned the navigation bar to be more intuitive and easier to use, with a new sidebar that provides a more relevant set of tabs for both GenAI apps and agent developers, as well as classic model training workflows. The new experience also gives more space to the main content area, making it easier to focus on the task at hand.
5. 🎮 MLflow Demo Experiment. New to MLflow GenAI? With one click, launch a pre-populated demo and explore tracing, evaluation, and prompt management in action. No configuration, no code required. This feature is available in the MLflow UI's homepage, and provides a comprehensive overview of the different functionality that MLflow has to offer.
6. 📊 Gateway Usage Tracking. Monitor your AI Gateway endpoints with detailed usage analytics. A new "Usage" tab shows request patterns and metrics, with trace ingestion that links gateway calls back to your experiments for end-to-end AI observability.
7. ⚡️ In-UI Trace Evaluation. Run custom or pre-built LLM judges directly from the traces and sessions UI, no code required! This enables quick evaluation of individual traces and individual without context switching to the Python SDK.
For visual demos of all the above features, please check out the release post on our website!
As always, we'd love to hear your feedback and experience:
1. 🏢 Workspace Support in MLflow Tracking Server. MLflow now supports multi-workspace environments. Users can organize experiments, models, prompts, with a coarser level of granularity and logically isolate them in a single tracking server.
2. 💬 Multi-turn Evaluation & Conversation Simulation. MLflow now supports multi-turn evaluation, including evaluating existing conversations with session-level scorers and simulating conversations to test new versions of your agent, without the toil of regenerating conversations. Use the session-level scorers introduced in MLflow 3.8.0 and the brand new session UIs to evaluate the quality of your conversational agents and enable automatic scoring to monitor quality as traces are ingested.
3. 💰 Trace Cost Tracking. Gain visibility into your LLM spending! MLflow now automatically extracts model information from LLM spans and calculates costs, with a new UI that renders model and cost data directly in your trace views. Additionally, costs are aggregated and broken down in the "Overview" tab, giving you granular insights into your LLM spend patterns.
4. 🎯 Navigation bar redesign. As we continue to add more features to the MLflow UI, we found that navigation was getting cluttered and overwhelming, with poor separation of features for different workflow types. We've redesigned the navigation bar to be more intuitive and easier to use, with a new sidebar that provides a more relevant set of tabs for both GenAI apps and agent developers, as well as classic model training workflows. The new experience also gives more space to the main content area, making it easier to focus on the task at hand.
5. 🎮 MLflow Demo Experiment. New to MLflow GenAI? With one click, launch a pre-populated demo and explore tracing, evaluation, and prompt management in action. No configuration, no code required. This feature is available in the MLflow UI's homepage, and provides a comprehensive overview of the different functionality that MLflow has to offer.
6. 📊 Gateway Usage Tracking. Monitor your AI Gateway endpoints with detailed usage analytics. A new "Usage" tab shows request patterns and metrics, with trace ingestion that links gateway calls back to your experiments for end-to-end AI observability.
7. ⚡️ In-UI Trace Evaluation. Run custom or pre-built LLM judges directly from the traces and sessions UI, no code required! This enables quick evaluation of individual traces and individual without context switching to the Python SDK.
For visual demos of all the above features, please check out the release post on our website!
As always, we'd love to hear your feedback and experience:
- GitHub Issues - Report bugs or request features
- MLflow Roadmap - See what's coming next and share your ideas
- ⭐️ Star us on GitHub - Show your support for the project
Daniel Lok
Feb 24, 2026, 11:14:47 AM (9 days ago) Feb 24
to mlflow...@googlegroups.com, eng-m...@databricks.com, ml-...@databricks.com
Apologies for the broken link to the release post, the correct link should be https://mlflow.org/releases/3.10.0/
On Feb 24, 2026, at 2:06 PM, Daniel Lok <danie...@databricks.com> wrote:
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