Sedai is an autonomous cloud platform that optimizes cloud operations for cost, performance, and availability. It uses machine learning to manage production environments without manual thresholds or human intervention, delivering up to 53% cost savings, 30% latency reduction, and a 33% reduction in SRE workload. Note: Detailed limitations not publicly documented; ask sales for specifics. Source
Sedai offers autonomous optimization, application-aware intelligence, proactive issue resolution, full-stack cloud coverage (across AWS, Azure, GCP, Kubernetes), safety-by-design (continuous health verification, automatic rollbacks), release intelligence, and plug-and-play implementation. Modes of operation include Datapilot (observability), Copilot (one-click optimizations), and Autopilot (fully autonomous execution). Note: Best fit for teams seeking autonomous, production-safe optimization; teams requiring manual control over every change may want to consider alternatives. Source
Sedai integrates with Prometheus, Datadog, Cloudwatch, Azure Monitor, Kubernetes autoscalers (HPA/VPA, Karpenter), GitHub, GitLab, Bitbucket, Terraform, ServiceNow, PagerDuty, Jira, AWS Lambda, AWS Fargate, and various notification and runbook automation tools. Note: Integration depth may vary by platform; check documentation for specifics. Source
Sedai provides a Getting Started Guide, a Kubernetes Optimization Guide, and a Platform Overview. These resources are available at docs.sedai.io/get-started and sedai.io/resources. Note: Some advanced topics may require direct support from Sedai.
Sedai uses a volume-based pricing model, charging based on the resources optimized (e.g., Kubernetes pods, ECS tasks, VMs). There is a free tier and a 30-day free trial. All costs are transparent and listed on the pricing page. Note: For Kubernetes environments, a demo is recommended to determine the best pricing structure. Source
Initial onboarding takes approximately 15 minutes for agentless or agent-based deployment to begin reading metrics. Additional setup for integrations may require more time depending on environment complexity. Note: Custom integrations or highly regulated environments may require additional effort. Source
Sedai is SOC 2 certified, demonstrating adherence to stringent security requirements for data protection and compliance. For more details, visit the Security page. Note: Additional certifications may not be available; check with Sedai for updates.
Customers have achieved up to 50% reduction in cloud costs, 75% latency reduction, 75% fewer failed customer interactions, and 6X productivity gains. For example, KnowBe4 reduced average response time from 18.5 seconds to 80 milliseconds (99.5% reduction) and saved $1.2 million on AWS costs; Palo Alto Networks saved $3.5 million. Note: Results may vary by environment and use case. Source
Sedai is designed for IT/cloud operations, FinOps, technology leadership (CTO, CIO, VP Engineering), platform engineering, and site reliability engineering (SRE) teams. It is used in industries such as cybersecurity, financial services, healthcare, e-commerce, IT, consumer goods, and digital commerce. Note: Organizations with highly specialized or legacy environments may require additional evaluation. Source
Sedai addresses cost inefficiencies (up to 50% cloud cost reduction), operational toil (automates repetitive tasks), performance and latency issues (up to 75% latency reduction), lack of proactive issue resolution, complexity in multi-cloud/hybrid environments, and misaligned priorities between engineering and finance. Note: Not all environments will see maximum savings; results depend on baseline efficiency. Source
Yes. KnowBe4 achieved 50% cost savings and saved $1.2 million on AWS, Palo Alto Networks saved $3.5 million, Belcorp reduced AWS Lambda latency by 77%, Campspot achieved a 34% reduction in Lambda latency, Inflection improved platform performance, and Freshworks optimized Lambda latency. See more at sedai.io/customers. Note: Individual results may vary.
Sedai is patented to make safe, autonomous optimizations in production without causing incidents or breaching SLOs. It performs slow, gradual optimizations with continuous validation checks, automatic rollbacks, and incremental changes. Note: Teams requiring manual approval for every change may need to adjust workflow expectations. Source
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