ML-powered compute management

Gain full visibility and control of your Databricks Jobs with AI cluster management. Save up to 50% on compute and consistently meet your runtime SLAs.

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Explore additional
use cases

“I’d be surprised if there was any data team on the planet that wouldn’t save money and time by using Gradient.”

Jesse Lancaster, CTO, Forma.ai

“Sync [Gradient] is an excellent tool that tackles a challenging problem and delivers real results that you can see in your AWS bill.”

Deniz Parmaksız, Senior data engineer, Insider

“With its automated optimization and scalable solutions, Gradient guarantees seamless pipeline operation, enabling us to focus on delivering products and features to our customers.”

Blaine Elliot, Data platform engineer, Abnormal

Get started in 3 easy steps:

Discover

opportunities

Run the free health check notebook to identify opportunities for optimization and estimate cost savings.

Monitor

job metrics

The ML models are trained and fine-tuned on your jobs. They’re 100% customized and adapt to your data pipelines, even those that vary in size.

Auto-optimize

to lower costs

Review recommendations and apply them in a click, or enable auto-apply for scalability when you have 100s of jobs.

50

cost savings

2

faster

100

SLAs met

25

hours saved

How

Gradient

helps:

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50% cost savings
with zero effort

Cut your Databricks spend in half with automated cluster optimization. Simply connect Gradient by Sync to your Databricks workspace to start saving.

Hit all your
critical SLAs

Consistently hit critical SLAs at lower costs. Gradient ensures you meet SLAs in the most cost-effective manner, even with varying data size.

25% engineering
hours saved

Stop manually optimizing clusters – there’s a SaaS for that. Gradient uses advanced machine learning algorithms to optimize your clusters 24/7.

Continuous monitoring
and optimization

Our machine learning models work around the clock using a closed feedback loop to continuously test, learn, and optimize clusters to meet your goals.

Complete control over Job cost and performance

Complete cost and performance visibility

Control runtimes, compute resources, instances, and more. Every optimization is logged and can be reverted in one click, even when Gradient is set to auto-apply recommendations.

Optimize complex
data pipelines

Variable size data pipelines, DAG dependencies, and parallel jobs, are no problem. Gradient was built to tackle these hard use-cases at scale.

Fully customizable
ML models

Each job is paired with a ML model trained using historical Spark event logs. This enables custom optimization that dynamically adapts to your workloads.

Optimization
at scale

Gradient can provide recommendations, or auto-apply changes to your clusters. If you’re running 100s of jobs, auto-apply enables optimization at scale.

Fine-grained reporting

Gain full visibility into job costs and performance. Get all the granular information as well as overall spend, total savings, and ROI

Compliant and secure

Gradient is SOC2 type II certified. And it automatically logs every recommendation and change it makes for easy compliance and auditability.

Works with your stack

Supports AWS, Azure, and soon GCP. Integrates with data orchestration and transformation services, such as Airflow, Azure Data Factory, and dbt.