
Meet Slingshot
With transparency into your financial and compute spend, you can track and forecast use more accurately, maximizing your investment—including your AI programs.

Cost efficiencies let you invest in new business uses.

Transparency helps you intelligently manage resources.

Federated management lets you tailor to your policies.

"At the end of our POV period, Dynata saw a substantial 15% in savings afforded by Slingshot, along with significant credit reductions. Slingshot helped to lead, guide and even gamify the lever pulling of our Snowflake instance that could make such a noteworthy impact to reducing our yearly spend.”
Dynata
- Bradley White | Sr. Director, Enterprise Data Management

“Capital One Slingshot has been helpful to manage, and optimize our Snowflake resources. What sets it apart is its visualization of usage patterns across days and weeks, giving us a granular view of how workloads and costs evolve over time.
With just a few clicks, we can apply cost-saving recommendations—such as rightsizing or shutting down idle resources. Equally important is the ability to revert warehouse schedule changes effortlessly. This functionality gives us peace of mind; if a change doesn’t yield the desired outcome, we can quickly return to a previous configuration. This flexibility reduces the risk of disruption and encourages experimentation—something that's crucial for a high-velocity engineering environment.”
GumGum
- Ruchi Singh | Manager, Analytics Engineering
“The Slingshot tool has been a game-changer for us, delivering significant time and cost savings while helping us accelerate our data growth initiatives. It’s been an invaluable resource, and we’re thrilled with the results.
The Capital One team has been fantastic to work with—supportive, responsive and truly collaborative at every step of this journey. Their guidance and partnership have made the process smooth and efficient.”
- Manager of Data Services Engineering Platform at a Large Enterprise Digital Entertainment Company


Optimization hub
Leverage tried and true optimizations for compute, queries, and data storage. Click to apply, or select workloads to fully automate.
Advanced ML models
The ease of serverless without the limitations. Slingshot's advanced ML models provide custom-tailored optimization for Databricks Jobs.
Data visualization
Allocate costs to LOBs, assess performance and uncover inefficiencies and opportunities to save up to 40% from Slingshot optimization.
Governance & IAM
Built internally out of need, Slingshot was created to enable the modern enterprise to manage massive amounts of data.
Related content

Assess the health of your Databricks environment across Serverless, Jobs, DBSQL, APC and DLT with our powerful, free dashboard.
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Snowflake optimization guide
Learn Snowflake optimization techniques for balancing cost and performance at scale.
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:
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.