The new solution eliminates wasted costs in Databricks — by using Sedai’s patented AI to understand and safely right-size clusters without manual tuning.
Traditional tools provide recommendations for how to optimize Databricks clusters, which engineers then need to investigate and implement themselves. By contrast, Sedai autonomously applies its optimizations with a single click, such as right-sizing clusters or tuning auto-termination settings. Sedai’s AI technology performs these optimizations safely with its patented reinforcement learning-based approach. Sedai is the only solution that safely and autonomously optimizes cloud services across AWS, Azure, GCP, Kubernetes, Databricks, and more.
“As companies scale up their Databricks workloads, costs become increasingly difficult to manage and predict,” said Suresh Mathew, CEO of Sedai. “AI is the only safe and efficient way to solve this problem at scale. That’s why we built the first autonomous cloud management platform that can safely optimize Databricks and the rest of your cloud services. Now, engineers can focus on innovating with data, not micromanaging their clusters.”
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Sedai’s key capabilities include:
- Understand. The platform monitors and analyzes costs across both Databricks and underlying cloud infrastructure, providing a deep understanding of spend. Sedai examines cluster behavior, job patterns, and resource usage in Databricks to find inefficiencies and recommend configuration changes that eliminate waste.
- Optimize. Sedai identifies and applies optimizations instantly, with a single click. It adjusts auto-termination settings and cluster size based on real-time usage, while thoroughly testing these optimizations to ensure they are non-disruptive and production-safe.
- Operate. The platform gives engineering teams actionable insights into waste and potential savings, based on usage, cost, and performance. Sedai continuously adjusts resources to ensure efficiency as teams scale their Databricks usage.
The Databricks solution is part of Sedai’s broader autonomous cloud management platform, which optimizes compute, storage, data, and AI workloads across multi-cloud environments. This gives teams unified visibility and optimization across their entire stack, not just Databricks.
Source: Businesswire