Sedai is the world’s first self-driving cloud.™ Our platform uses patented AI to safely optimize your cloud resources for cost, performance, and availability — freeing your engineers from routine work. Whatever your cloud looks like, Sedai learns how to drive it and fixes issues in seconds, before they waste money or cause outages. Today, we save millions of dollars for engineering leaders at Palo Alto Networks, Experian, HP, Experian, and Capital One.
Sedai goes beyond recommendations. Our platform takes action to autonomously manage and optimize your cloud resources, without manual intervention. It analyzes your production environment to find cost and reliability optimization opportunities in real time — learning from behavior patterns and adapting to new releases. Under the hood, Sedai uses deep reinforcement learning to predict the impact of potential optimizations, enabling the platform to take these actions safely.
Sedai's unique approach makes it the only way way to manage and optimize the cloud at scale. Unlike conventional automation, Sedai doesn't follow fixed rules and scripts. Instead, it acts like an experienced SRE: incrementally adjusting the configuration of your resources until the exact point where performance or availability would be impacted.
The result? Sedai has executed more than 25 million autonomous actions in production — with 0 incidents.
Ready to save 30–50% on your cloud costs? Visit sedai.io to speak with our cloud optimization experts.
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Product Description
Sedai incorporates key autonomous system characteristics in a cloud context. By leveraging a massive influx of data streams, Sedai builds a layer of intelligence via its core decision engine, which derives concepts from probability theory and applied machine learning techniques. Its self-learning and self-correcting model seamlessly manages cloud platforms with a focus on explainable decisions.
Our products
S.Watch
Sedai connects with various monitoring tools, including Prometheus, Datadog, Cloudwatch, etc., and tracks four golden signals: Latency, Traffic, Errors, and Saturation. S-Watch distills noise to provide insights and recommendations to bring key KPIs such as MTTD, MTTF, MTBF, and MTTR to acceptable levels.
S.Run
Sedai distills data into an explainable and tunable knowledge base that powers its machine learning models. These models fuel Sedai’s core decision engine, which determines efficient and corrective workflows for all identified drifts to infer optimal strategies for detection and safe remediations. Its true closed-loop learning model enables self-configuration at optimal levels, ensuring the highest levels of availability. Armed with vast data, deep insights, and a rich knowledge base, platforms that are managed by Sedai are able to achieve a self-optimized state.
Overview by
John Jamie