Data Cloud Cost Optimization for Snowflake and Databricks
Keebo automatically rightsizes Snowflake and Databricks compute resources based on real workload patterns. Reduce spend, protect performance, and eliminate manual optimization work.
Trusted by Modern Data Teams
Purpose-Built for Data Warehouse Cost Optimization
Compute Optimization
- Warehouse size adjustments
- Cluster scaling optimization
- Scheduling-based resizing
Proactive Controls
- Intelligent auto-suspend tuning
- Idle resource elimination
- Proactive suspension algorithms
Advanced Intelligence
- Independently verifiable savings
- Algorithm aggressiveness tuning
- Outside change detection
Key Use Cases
- Snowflake cost optimization
- Databricks cost optimization
- FinOps and cloud cost governance
- Data platform efficiency initiatives
FAQ
Data cloud cost optimization is the process of reducing compute and infrastructure spend in platforms like Snowflake and Databricks while maintaining performance and scalability. It includes optimizing warehouse sizing, query execution, and workload efficiency.
Snowflake costs can be optimized by rightsizing warehouses, improving query efficiency, reducing idle compute, and aligning usage with workload demand. Keebo automates these optimizations continuously.
No. Keebo enforces strict performance guardrails to ensure SLAs are maintained.
Snowflake usage patterns evolve as your data and workloads grow. Continuous monitoring ensures you can spot inefficiencies early, adjust warehouse configurations promptly, and apply automated optimizations to keep Snowflake compute costs under control at all times.