FEATURES

Automatically fine-tuned ML
models

Leverage Gradient’s advanced models that are automatically fine-tuned to your workloads using historical logs

Each job is paired with a model that is automatically fine tuned to offer customized recommendations

100% customized models

Get maximum impact with minimal effort with optimizations tailored to your
data pipelines and goals

Custom tailored
recommendations

Get the right optimization recommendations for your jobs. Gradient uses advanced machine-learning models that are fine-tuned to your jobs , based on historical logs. This is what enables Gradient to provide high-impact optimizations.

Each job is paired with a ML model that is trained on it to offer customized optimizations

Precision-engineered
accuracy

Leverage cutting-edge models straight out of MIT to optimize your compute infrastructure. We’ve built a hallucination-free model that uses statistical trends to increase accuracy, with a goal of reaching 99% accuracy.

Gradient's models improve over time using a closed-loop feedback system

Self-Improving
models

Gradient’s closed feedback loop continuously improves its recommendations. Each optimization builds upon the performance results of previous adjustments, ensuring ever-improving efficiency.

Closed-loop feedback ensures our models improve over time

One engineers,
1000s of jobs

Empower a single engineer to efficiently manage thousands of jobs. Gradient’s machine learning powered optimization gives back 10 hours per week per engineer, dramatically increasing productivity.

Save 10 hours a week per data engineer with Gradient's automation

Fully transparent modeling

See what we use to train our models. Gradient is a fully transparent system, allowing you to access the historical cluster information and Spark event logs our machine-learning models use to train, and even use them yourself.

See the data Gradient is training on, every recommendation made, and every change applied

Works with your jobs

Gradient’s models can be trained on any job you are running on Databricks Jobs compute and cut compute costs while improving performance. This includes ETL jobs, LLM jobs, ML jobs, period jobs, large jobs, short jobs, or any other job.

Get custom tailored optimizations based for your data pipelines

Key
benefits:

Fully customized models,
trained on historical logs

Hyper-focused
optimization

Every recommendation is tailored to your specific data pipelines, ensuring maximum relevance and impact.

Reduce costs
and hit SLAs

Gradient helps organizations save up to 50% on their Databricks Jobs compute bill, while obeying strict runtime SLAs.

Understand the tradeoffs between compute cost and job runtime performance

Understand SLA tradeoffs

Hitting your SLA might increase your compute cost. Easily understand the impact of hitting runtime SLAs on costs with Gradient.

Built-in scalability

Gradient was built to scale, allowing a single engineer to manage thousands of clusters with machine learning powered recommendations.

Flexibile automation options

Gradient is SOC 2 type II complaint and only makes changes to your compute infrastructure. There is no risk to data or application code.

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

Jesse Lancaster, CTO, Forma.ai

“Gradient is an excellent tool that delivers real results that you can see in your monthly [data infrastructure] bill.”

Deniz Parmaksız, Sr. Data Engineer, Insider

“With automated optimization and scalable solutions, Gradient guarantees seamless pipeline operation.”

Blaine Elliot, Data Engineer, Abnormal

Explore
additional
capabilities

Get started today

Leverage customized machine-learning models to take your compute to the next level

Book a demo

FEATURES

Automatically fine-tuned ML models