Optimize Databricks & Snowflake with Slingshot
From granular reports and cost allocation to optimizations, Slingshot has the tools to help you maximize your investment.
Meet Slingshot
Maximize your cloud data platform efficiency
With transparency into your financial and compute spend, you can track and forecast use more accurately, maximizing your investment—including your AI programs.
Key benefits
Optimize & do more
Cost efficiencies let you invest in new business uses.
Gain visibility
Transparency helps you intelligently manage resources.
Manage your way
Federated management lets you tailor to your policies.
Hear from our customers
Supported platforms
Optimization hub
Databricks & Snowflake automated optimization
Leverage tried and true optimizations for compute, queries, and data storage. Click to apply, or select workloads to fully automate.
Advanced ML models
ML-powered optimization for Databricks Jobs
The ease of serverless without the limitations. Slingshot's advanced ML models provide custom-tailored optimization for Databricks Jobs.
Data visualization
Visualizations and insights for data clouds
Allocate costs to LOBs, assess performance and uncover inefficiencies and opportunities to save up to 40% from Slingshot optimization.
Governance & IAM
Governance and federation for Databricks & Snowflake
Built internally out of need, Slingshot was created to enable the modern enterprise to manage massive amounts of data.
Solutions
Related content
Slingshot Frequently Asked Questions
How do I know that the Snowflake recommendations applied are actually saving money?
The value report contains a detailed history of cost savings generated by Slingshot recommendations. The cost savings can be viewed by all recommendations as well as per recommendation applied.
Additionally, the warehouse details page visualizes the cost over time and will include a change history so that you can see why there were cost changes.
What types of Snowflake cost optimizations does Slingshot cover?
Slingshot optimizes the following objects in Snowflake:
- Schedules for warehouse size, cluster size and auto-suspend
- Warehouse statement timeout
- Just-right time travel provisioning
- Ideal COPY file sizes
- Drop unused tables
How are warehouse schedule recommendations formulated?
Slingshot analyzes the workload and query attributes of a warehouse. It tracks factors such as, but not limited to, query size, query load, queued queries and data spillage. Based on these usage patterns, Slingshot generates recommendations that will optimize the Snowflake resources.
Do recommendations support Warehouses with QAS?
Yes, we analyze warehouses that have Query Acceleration Services (QAS) enabled and provide recommendations.
Additionally, in some cases, we can enhance QAS cost-efficiency by adjusting the time slots in the scheduling recommendations to account for delayed queries or queries that don't meet the eligibility criteria.
Slingshot next steps
Start your free trial
Visit the Snowflake Marketplace today to see how Slingshot can help you maximize your Snowflake investment.
Snowflake optimization guide
Learn Snowflake optimization techniques for balancing cost and performance at scale.
