Automated management of Databricks/Snowflake
Automated management of Databricks/Snowflake
Simplify cloud management, while retaining control. Use approval workflows, rule-based automation, or autonomous optimization for scale.
Snowflake WH rightsizing
Dynamically rightsize your Snowflake warehouses
Slingshot analyzes warehouse performance and metadata to recommend dynamic sizing schedules that improve workload cost or performance.
Auto-tune clusters
Automatically tune Databricks job clusters
Slingshot uses self-improving ML models that are automatically trained on target workloads to provide custom tailored optimizations.
Continuous monitoring
Continuous monitoring of Databricks and Snowflake
Slingshot constantly monitors the configuration, cost, and performance of workloads, and alters of inefficiencies and misconfigurations.
Retain full control
Your data clouds. Your control.
Whether you use Slingshot for passive recs, rule-based actions, or autonomous optimization you retain full control of your data clouds.
Save engineering time
Regain valuable ENG hours
Unburden the team from manually optimizing data clouds. At Capital One, Slingshot enables us to gain back +50K ENG hours every year.
