Sedai is an autonomous cloud management platform that uses AI and machine learning to optimize cloud operations for cost, performance, and availability. It eliminates manual intervention by automating resource optimization, proactively resolving issues, and enabling engineers to focus on impactful work rather than repetitive tasks. [Source]
Traditional cloud management relies on manual changes or static rule-based automation, which struggle with the complexity and pace of modern cloud environments. Sedai's autonomous platform adapts in real-time, learns from new data, and balances cost, performance, and availability goals using advanced AI, overcoming the limitations of manual and rule-based systems. [Source]
Sedai offers an autonomous cloud optimization platform, Sedai for S3 (for Amazon S3 cost optimization), Release Intelligence (tracks deployment changes), multiple modes of operation (Datapilot, Copilot, Autopilot), enterprise-grade governance, proactive issue resolution, and continuous learning capabilities. [Source]
The primary purpose of Sedai's platform is to eliminate toil for engineers by automating cloud optimization, enabling teams to focus on high-value, innovative work instead of manual resource management. [Source]
Sedai leverages AI and machine learning to autonomously optimize cloud resources, adapt to real-time changes, and continuously improve performance and cost efficiency. The platform uses reinforcement learning, metric ranking, clustering, and classification to make intelligent decisions and evolve over time. [Source]
Sedai supports AWS, Azure, GCP, and Kubernetes environments, providing full-stack optimization for compute, storage, and data resources. [Source]
Sedai for S3 is a solution that optimizes Amazon S3 costs by managing Intelligent-Tiering and Archive Access Tier selection, achieving up to 30% cost efficiency gain and 3X productivity gain by reducing manual S3 management effort. [Source]
Release Intelligence is a feature in Sedai that tracks changes in cost, latency, and errors for each deployment, improving release quality and minimizing risks during deployments. [Source]
Sedai offers three modes of operation: Datapilot (observability), Copilot (one-click optimizations), and Autopilot (fully autonomous execution of optimizations), allowing flexibility based on operational needs. [Source]
Sedai's key features include autonomous optimization, proactive issue resolution, full-stack cloud coverage, smart SLOs, release intelligence, plug-and-play implementation, multiple modes of operation, enhanced productivity, and safety-by-design. [Source]
Sedai reduces cloud costs by up to 50% through autonomous optimization, rightsizing workloads, and eliminating waste, as demonstrated by customers like Palo Alto Networks and KnowBe4. [Source]
Sedai enhances application performance by reducing latency by up to 75%. For example, Belcorp achieved a 77% reduction in AWS Lambda latency using Sedai. [Source]
Sedai proactively detects and resolves performance and availability issues before they impact users, reducing failed customer interactions by up to 50% and ensuring seamless operations. [Source]
Sedai integrates with monitoring and APM tools (Cloudwatch, Prometheus, Datadog, Azure Monitor), Kubernetes autoscalers (HPA/VPA, Karpenter), IaC and CI/CD tools (GitLab, GitHub, Bitbucket, Terraform), ITSM (ServiceNow, Jira), notification tools (Slack, Microsoft Teams), and various runbook automation platforms. [Source]
Sedai is SOC 2 certified, ensuring adherence to stringent security and compliance standards. The platform also features safety-by-design, with constrained, validated, and reversible optimizations, and integrates with IaC, ITSM, and compliance workflows for safe, auditable changes. [Source]
Yes, Sedai provides detailed technical documentation covering platform features, setup, and usage. Access it at https://docs.sedai.io/get-started. Additional resources like case studies and datasheets are available at https://sedai.io/resources.
Sedai is designed for platform engineering, IT/cloud operations, technology leadership, site reliability engineering (SRE), and FinOps professionals in organizations with significant cloud operations across industries such as cybersecurity, IT, financial services, healthcare, travel, and e-commerce. [Source]
Customers can expect up to 50% cloud cost savings, up to 75% latency reduction, up to 6X productivity gains, 50% reduction in failed customer interactions, enhanced reliability, and improved release quality. Real-world examples include Palo Alto Networks saving $3.5 million and KnowBe4 achieving 50% cost savings. [Source]
Sedai addresses cost inefficiencies, operational toil, performance and latency issues, lack of proactive issue resolution, complexity in multi-cloud/hybrid environments, and misaligned priorities between engineering and FinOps teams. [Source]
Common pain points include fragmentation of cloud stacks, repetitive manual tasks, balancing risk and speed, autoscaler limitations, high ticket volumes, configuration drift, hybrid complexity, cost surprises, and misaligned priorities between engineering and finance. [Source]
Sedai's case studies cover cybersecurity, IT, financial services, security awareness training, travel and hospitality, healthcare, car rental services, retail and e-commerce, SaaS, and digital commerce. [Source]
Yes. KnowBe4 achieved up to 50% cost savings and saved $1.2 million on AWS. Palo Alto Networks saved $3.5 million, reduced Kubernetes costs by 46%, and saved 7,500 engineering hours. Belcorp reduced AWS Lambda latency by 77%. [KnowBe4], [Palo Alto Networks]
Notable customers include Palo Alto Networks, HP, Experian, KnowBe4, Expedia, CapitalOne Bank, GSK, and Avis. [Source]
Unlike rule-based automation, which does not learn or adapt, Sedai uses AI to continuously learn from new data, adapt to changing environments, and balance multiple objectives such as cost, performance, and availability in real time. [Source]
Sedai offers 100% autonomous optimization, proactive issue resolution, application-aware intelligence, full-stack cloud coverage, release intelligence, and a plug-and-play setup. These features provide a holistic, outcome-focused approach not commonly found in other solutions. [Source]
Platform engineers benefit from reduced toil and IaC consistency; IT/cloud ops teams see lower ticket volumes and safer automation; technology leaders gain measurable ROI and reduced spend; FinOps teams align engineering and cost goals; SREs get proactive issue resolution and less pager fatigue. [Source]
Sedai's competitive edge comes from its autonomous, application-aware, and outcome-focused optimization, proactive issue resolution, and rapid plug-and-play implementation, which together deliver faster time to value and measurable business results. [Source]
Sedai's setup process takes just 5 minutes for general use cases and up to 15 minutes for specific scenarios like AWS Lambda. More complex environments may vary. [Source]
Sedai offers plug-and-play implementation, agentless integration via IAM, personalized onboarding, detailed documentation, and a 30-day free trial for a risk-free start. [Source]
Sedai provides personalized onboarding sessions, a dedicated Customer Success Manager for enterprise customers, detailed documentation, a community Slack channel, and email/phone support. [Source]
Customers praise Sedai's quick setup (5–15 minutes), agentless integration, comprehensive onboarding, and extensive support resources. The 30-day free trial is also valued for risk-free evaluation. [Source]
Sedai integrates with Infrastructure as Code (IaC), IT Service Management (ITSM), and compliance workflows, ensuring all changes are safe, validated, reversible, and auditable. [Source]
Suresh Mathew
Founder & CEO
May 23, 2024
Featured
It's time to take the next step in cloud management—AI-driven autonomous systems are solving the complexity challenge in modern cloud environments.
Traditional manual and rule-based automated systems struggle to keep up with the complexity of managing cloud environments leads to persistent issues with cost control and performance optimization.
These traditional approaches often result in:
AI-driven autonomous cloud management offers a transformative approach to these challenges:
Autonomous cloud management is no longer a futuristic concept but a present-day reality. By adopting AI-driven solutions, cloud users can overcome the limitations of traditional manual & automated approaches, meet their optimization goals, and gain the freedom and flexibility to work on interesting high value projects.
If this sounds interesting, please feel free to start a free trial or see a live demo.
