Sync Computing develops Gradient, a machine learning-based optimization processing unit that automates compute resource management for data infrastructure on cloud platforms. By reducing Databricks costs by up to 50% and saving engineering hours, Gradient ensures organizations meet their runtime service level agreements efficiently.
Funding
$22.9M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Data infrastructure management on cloud platforms can be complex and costly, especially for organizations utilizing Databricks. Manually optimizing compute resources requires significant engineering effort and often fails to achieve optimal cost efficiency or consistent service level agreement (SLA) adherence.
Solution
Sync Computing's Gradient is an AI-powered compute optimization solution designed to automate resource management for data infrastructure, specifically targeting Databricks environments. Gradient leverages machine learning algorithms developed at MIT to analyze historical Spark event logs and dynamically adapt to varying workloads. By continuously testing and learning, Gradient optimizes cluster configurations, enabling organizations to reduce compute spend, consistently meet runtime SLAs, and minimize manual intervention. The platform provides complete cost and performance visibility, allowing users to control runtimes, compute resources, and instances, with every optimization logged and easily revertible.
Target Audience
The primary target audience includes data engineers, data platform teams, and CTOs who are responsible for managing and optimizing data infrastructure costs and performance within Databricks environments.
Features
- AI-driven compute optimization for Databricks environments, resulting in up to 50% cost savings
- Continuous monitoring and automated cluster tuning using machine learning models
- SLA management, ensuring consistent runtime performance even with variable data sizes
- Fine-grained reporting, providing complete visibility into job costs, performance, and ROI
- Support for complex data pipelines with DAG dependencies and parallel jobs
- Integration with data orchestration and transformation services like Airflow, Azure Data Factory, and dbt
- SOC2 Type II certification, ensuring compliance and security
- Customizable ML models that adapt to specific workloads based on historical Spark event logs