Focus on data workloads, not infrastructure

Serverless GPU compute enables ML training and GenAI workloads without the need for infrastructure management.

Each user's code runs in a sandboxed container, preventing access to other users' data or network connections, giving every team enterprise-grade isolation without requiring separate clusters.

Databricks automatically optimizes table data layouts in the background for better performance and cost-efficiency, without manual tuning.

Serverless analyzes query patterns and automatically prefetches and indexes data in the background, accelerating query performance without any configuration.

Run distributed ML training on serverless compute using SparkML, without managing clusters, environments, or infrastructure.

Databricks automatically selects the optimal instance type for each workload. No CPU-optimized vs memory-optimized vs instance-class decisions, no manual VM selection, no need to decide between single VMs, warehouses, or Spark clusters, no infrastructure tuning.

Track compute spend at the workload level through system.billing.usage, giving platform teams full visibility into which jobs, pipelines, and notebooks drive cost.

Monitor execution in real time with Query History and Query Profile, giving engineers and analysts instant visibility into performance and bottlenecks.