Tecton adds real-time feature serving

The key move was turning Mosaic AI from a model building layer into a system that can feed live facts into predictions and agents at request time. Tecton adds the machinery for taking raw events, like a user click, card swipe, or inventory change, turning them into model features, and serving those features from an online store with low latency while keeping them aligned with the historical data used for training.1 2 3 4

• A feature store is not just a database table for model inputs. It defines the transformation logic, materializes features into offline and online stores, and exposes a serving API, which matters because production ML breaks when training data and live inference data drift apart.1 2 4
• Tecton was built specifically for real-time serving. Its docs describe managed feature computation, online serving, monitoring, and HTTP retrieval of feature vectors, which is the missing piece if Databricks wants agents to react to fresh behavioral or operational signals instead of stale batch snapshots.1 2 5 6
• This also fits Databricks broader consolidation pattern. The company profile and related research show it expanding from the lakehouse into adjacent workflow layers, and a Prequel interview calls out Tecton as white space expansion beyond Spark jobs and the warehouse itself.7 8

From here, the stack gets tighter around real-time decisioning. Databricks can use Tecton to make Mosaic AI and Agent Bricks better at fraud, recommendations, personalization, and agent context retrieval, while pulling one more specialized MLOps category inside the lakehouse instead of leaving it to an external vendor.3 6 7