ML-Powered Databricks Cluster
Optimizations for All
Enable self-improving Databricks Jobs clusters to hit
your SLA goals and lower costs by 25-50%









How Gradient helps
Empower platform teams with control
Lower Databricks costs by 25-50%
Hit your SLA’s
no matter what
Unified Metrics Monitoring
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

Read our latest Blog Posts:
5 Lessons learned from testing Databricks SQL Serverless + DBT

Blog, Case Study
5 Lessons learned from testing Databricks SQL Serverless + DBT
What’s the difference between Databricks’s Overwatch and System Tables tools?

Blog
What’s the difference between Databricks’s Overwatch and System Tables tools?
Is Databricks’s autoscaling cost efficient?

Blog, Case Study
Is Databricks’s autoscaling cost efficient?
Are Databricks clusters with Photon and Graviton instances worth it?

Blog
Are Databricks clusters with Photon and Graviton instances worth it?
Databricks driver sizing impact on cost and performance

Blog, Case Study
Databricks driver sizing impact on cost and performance
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