Altana deploys GenAI models 20x faster with up to 50% improved performance on Databricks Mosaic AI
Before Databricks, Altana's team was forced to build and maintain boilerplate GenAI launch infrastructure and evaluation tooling instead of focusing on product functionality, while managing competing tensions between vendor lock-in, total cost of ownership, and information security across multiple cloud deployments.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Delta Lake data ingestion
Delta Lake provides real-time data ingestion and robust data management for diverse customer data sources.
After migrating GenAI development workflows to Databricks, Altana trains and deploys models more than 20 times faster to production and has seen model performance improve by 20–50%, freeing the team to focus on building AI-driven products rather than maintaining infrastructure.
What failed first
Altana's prior infrastructure offered no unified ML lifecycle platform, requiring engineers to create their own boilerplate tooling for every GenAI launch and leaving them unable to resolve competing demands around vendor lock-in, cost, and security without sacrificing product velocity.