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Welcome to Warehouse Optimization for Snowflake

What Is Warehouse Optimization?

Keebo Warehouse Optimization for Snowflake is an automation platform that optimizes Snowflake data warehouses for cost savings without compromising performance.

Automated Warehouse Configurations

Warehouse Optimization continuously analyzes workload patterns and adjusts warehouse size in real time to match demand. This prevents spend on idle resources during low-activity periods while maintaining capacity for peak loads.

Fine-Tuned Performance Control

Warehouse Optimization provides configurable settings to customize optimization strategies. These settings enable organizations to tailor optimization behavior to specific performance requirements and risk tolerance.

Secure and Data-Centric Approach

Warehouse Optimization operates solely on performance metadata accessed through a dedicated Snowflake user and role with read-only permissions. It does not access or interact with sensitive data.

How Does Warehouse Optimization Benefit an Organization?

Cost Savings

Automated optimization reduces wasted Snowflake credits by right-sizing warehouses and improving resource utilization.

Improved Efficiency

Dynamic warehouse adjustments maintain optimal performance for query workloads by allocating the right resources at the right time while reducing costs.

Reduced Administrative Overhead

Warehouse Optimization eliminates the need for manual warehouse management tasks, freeing teams to focus on higher-value work.

Enhanced Visibility and Control

The Keebo portal provides dashboards, reporting, and audit logs with clear insight into optimization actions and cost savings across the Snowflake environment.

Who Should Use Warehouse Optimization?

Warehouse Optimization is designed for organizations of all sizes that rely on Snowflake for data warehousing and face challenges with:

  • Simplifying warehouse administration and reducing manual intervention
  • Controlling Snowflake spending and reducing wasted credits
  • Managing fluctuating query volumes and resource demands