Audience

Data engineering and cloud infrastructure teams who run Databricks workloads and want to automate performance tuning and cost optimization with minimal manual effort

About Zipher

Zipher is an autonomous optimization platform specifically designed to improve the performance and cost efficiency of Databricks workloads by eliminating manual tuning and resource management and continuously adjusting clusters in real time. It uses proprietary machine learning models and the only Spark-aware scaler that actively learns and profiles workloads to adjust cluster resources, select optimal configurations for every job run, and dynamically tune settings like hardware, Spark configs, and availability zones to maximize efficiency and cut waste. Zipher continuously monitors evolving workloads to adapt configurations, optimize scheduling, and allocate shared compute resources to meet SLAs, while providing detailed cost visibility that breaks down Databricks and cloud provider costs so teams can identify key cost drivers. It integrates seamlessly with major cloud service providers including AWS, Azure, and Google Cloud and works with common orchestration and IaC tools.

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Pricing

Free Trial:
Free Trial available.

Integrations

API:
Yes, Zipher offers API access
See Integrations

Ratings/Reviews

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Company Information

Zipher
Founded: 2023
United States
zipher.cloud/

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Product Details

Platforms Supported
Cloud
Training
Documentation
Support
Online

Zipher Frequently Asked Questions

Q: What kinds of users and organization types does Zipher work with?
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Q: What kind of support options does Zipher offer?
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