ML-powered optimization for Databricks Jobs

Slingshot feature showing custom-tailored optimization

Custom-tailored optimization

High-impact optimization for your workloads

Slingshot's ML models are automatically fine-tuned on your workloads for data-driven modeling that result in high-impact optimizations.

Slingshot feature showing self-improving optimization

Self-improving optimization

Self-improving machine learning models

Slingshot uses closed-loop feedback to continuously improve its recommendations based on the impact of the previous ones.

Slingshot feature showing variable-sized data

Variable-sized data

Seamlessly adapt to workload changes

A closed feedback loop enables ML models to adapt to changes, like data size growth or cost anomalies, before they impact the business.

Slingshot feature showing how it works with Databricks Jobs

Any workload

Works with your jobs

Our ML models can automatically train on any workload, including ETL jobs, LLM jobs, ML jobs, periodic jobs, large jobs and short jobs.

Additional features

SEE SLINGSHOT IN ACTION

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