ML-Powered Databricks Cluster
Optimizations for All

Enable self-improving Databricks Jobs clusters to hit
your SLA goals and lower costs by 25-50%

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Features

How it works

We built Gradient to provide the best optimization and integration experience possible

See Gradient’s ROI with cost and runtime optimizations

Monitor your jobs total costs across both DBUs and cloud fees in real-time to stay informed

Ensure your job runtimes and SLAs are met

Learn what anomalies are impacting your jobs’ performances

See Gradient’s value in action by watching your cost and runtime goals being met

 

Cluster integrations with AWS and Azure

Granular compute metrics are obtained by retrieving cluster logs beyond what Databricks exposes in their system tables

Integrate with Databricks Workflows or Airflow to plug Gradient into how your company runs your infrastructure

Easy metrics gathering as Gradient does the heavy lifting for you and automatically compiles and links information across both Databricks and cloud environments

Auto-import and setup all of your jobs with a single click

Gradient connects to your Databricks workspace behind the scenes to make importing and setting up job clusters as easy as a single click

Non-invasive webhook integration is used to link your environment with Gradient without any modifications to your code or workflows

 

Machine learning algorithm trains on each job

Historical log information is used to train custom models for each of your jobs.  Since no two jobs are alike, custom models are critical to optimizing at scale.

Accuracy is ensured by training on real job performance data

Stability is obtained with small incremental changes and monitoring to ensure reliable performance

View and approve recommendations with a click

View recommendations in the Gradient UI for your approval before any changes are actually made

Click to approve and apply a single recommendation so you are always in control

Enable auto-apply for
self-improving jobs

Focus on business goals by allowing Gradient to constantly improve your job clusters to meet your ever changing business needs

Optimize at scale with auto apply, no need to manually analyze individual jobs – just watch Gradient get to work across all of your jobs

Free your engineers from manually tweaking cluster configurations and allowing them to focus on more important work

 

Change your SLA goals at any time

Runtime SLA goals ultimately dictate the cost and performance of your jobs.  Longer SLAs can usually lead to lower costs, while shorter SLAs could lead to higher costs.

Goals change constantly for your business, Gradient allows your infrastructure to keep up at scale

Business lead infrastructure allows you to start with your business goals and work backwards to your infrastructure, not the other way around

“Sync Autotuner is an excellent tool that tackles a challenging problem and delivers real results that you can see in your AWS bill.” Deniz Parmaksız Sr. Data Engineer, Insider
“If our current data load stays consistent for the rest of the year, we will be projected to save around $100k from the autotuner.” Matthew Weingartner Sr. Data Engineer, Disney
“Sync helped me optimize my Databricks jobs and reduced costs by 70%. They make optimizing Databricks easy and pain free so I can focus on other important business tasks.” VY Sr. Engineer at Global Fortune 500 SaaS company with multi-million dollar Databricks spend

Read our latest Blog Posts:

Gradient New Product Update Q4 2023

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5 Lessons learned from testing Databricks SQL Serverless + DBT

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What’s the difference between Databricks’s Overwatch and System Tables tools?

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Is Databricks’s autoscaling cost efficient?

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Are Databricks clusters with Photon and Graviton instances worth it?

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Databricks driver sizing impact on cost and performance

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