Sedai provides an AI-driven platform for continuous cloud cost optimization and performance tuning across multiple services, including Kubernetes, VMs, and serverless architectures. By autonomously adjusting resource allocations and configurations, Sedai enables cloud teams to achieve significant cost reductions—up to 90%—while enhancing application performance and availability.
Funding
$18.5M 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.
NVFounders
Product
Problem
Cloud environments often suffer from resource inefficiencies, leading to overspending and suboptimal application performance. Traditional optimization methods are often manual, reactive, and lack the real-time adaptability required to address dynamic workload demands. This results in wasted resources, increased operational overhead, and potential performance bottlenecks.
Solution
Sedai offers an AI-powered autonomous cloud management platform that continuously optimizes cloud infrastructure for cost, performance, and availability. The platform leverages machine learning to analyze application behavior and traffic patterns, automatically adjusting resource allocation and configurations across various services, including Kubernetes, VMs, serverless functions, and storage solutions. Sedai proactively identifies and remediates potential availability issues, ensuring optimal performance and minimizing failed customer interactions. By implementing intelligent rightsizing, rate optimization, and autonomous concurrency adjustments, Sedai enables organizations to achieve significant cost savings while enhancing application performance and release quality.
Target Audience
Sedai targets platform engineering, FinOps, DevOps, and SRE teams within enterprises and high-growth companies seeking to automate cloud optimization, reduce costs, improve application performance, and enhance overall reliability.
Features
- AI-driven autonomous optimization for compute, storage, and data services across AWS, Azure, and Google Cloud.
- Real-time resource adjustments based on application behavior and traffic patterns.
- Autonomous remediation of availability issues, including timeouts, memory settings, and restarts.
- Release intelligence scorecards providing quantitative insights into production performance, cost, and error rates for each release.
- Smart SLOs that enable the platform to automatically optimize applications to meet defined performance and availability goals.
- Support for a wide range of technologies, including Kubernetes, AWS Lambda, AWS ECS, Azure VMs, and Google Dataflow.
- Integration with popular APM tools such as CloudWatch, Prometheus, and Datadog, as well as notification providers like Slack and PagerDuty.
- Multi-layered safety checks to ensure safe and reliable autonomous operations.