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 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 include Datapilot (observability), Copilot (one-click optimizations), and Autopilot (fully autonomous execution). Note: Sedai may not be suitable for organizations requiring manual control over every optimization; consult sales for details. Source
Sedai supports containers (AWS EKS, Kubernetes, AWS ECS), serverless (AWS Lambda), VMs (EC2), and storage services (AWS EBS). It integrates with AWS, Azure, GCP, and Kubernetes environments. Note: For unsupported platforms, contact Sedai for roadmap details. Source
Sedai uses a volume-based pricing model, charging based on the resources optimized (e.g., Kubernetes pods, ECS tasks, VMs). All costs are transparently listed on the pricing page. Sedai offers a free tier and a 30-day free trial. For Kubernetes environments, a demo is recommended to determine the best pricing structure. Note: Detailed enterprise pricing and discounts are not publicly documented; contact sales for specifics. Source
Sedai integrates with monitoring and APM tools (Prometheus, Datadog, Cloudwatch, Azure Monitor), Kubernetes autoscalers (HPA/VPA, Karpenter), IaC and CI/CD (GitHub, GitLab, Bitbucket, Terraform), ITSM (ServiceNow, PagerDuty, Jira), notification tools, runbook automation platforms, and serverless (AWS Lambda, AWS Fargate). Note: Integration with other platforms may require custom setup; contact support for details. Source
Sedai is SOC 2 certified and employs safety-by-design features such as continuous health verification, automatic rollbacks, and incremental changes for real-time validation. These mechanisms help prevent outages and SLO breaches during autonomous optimizations. Note: For compliance beyond SOC 2, contact Sedai for additional certifications. Source
Sedai provides a Getting Started Guide, a Kubernetes Optimization Guide, and a detailed Platform Overview. These resources are available at docs.sedai.io/get-started and sedai.io/resources. Note: Some advanced documentation may require a customer login or support request.
Customers typically achieve up to 50% cloud cost reduction, 75% latency reduction, 50% fewer failed customer interactions, and up to 6X productivity gains. Financial payback is often under six months, with ROI greater than 400%. For example, KnowBe4 saved $1.2 million on AWS costs, and Palo Alto Networks saved $3.5 million. Note: Results may vary based on environment complexity and usage patterns. 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 across 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 custom integration; contact Sedai for details. Source
Sedai addresses cost inefficiencies (up to 50% reduction), operational toil (automates repetitive tasks, up to 6X productivity gains), performance and latency issues (up to 75% latency reduction), lack of proactive issue resolution (up to 50% fewer failed interactions), complexity in multi-cloud/hybrid environments, and misaligned priorities between engineering and finance. Note: For highly regulated or air-gapped environments, additional validation may be required. Source
Initial onboarding takes about 15 minutes for agentless or agent-based deployment. Additional setup for CI/CD and integrations may require more time depending on environment complexity. Sedai offers a plug-and-play process and integrates with existing tools. Note: Complex environments may require additional configuration; consult Sedai support for guidance. Source
Yes. KnowBe4 achieved up to 50% cost savings and saved $1.2 million on AWS. Palo Alto Networks saved $3.5 million in cloud costs. Belcorp reduced AWS Lambda latency by 77%. Campspot achieved a 34% reduction in AWS Lambda latency. Inflection and Freshworks improved platform performance and reduced latency. See more at sedai.io/customers. Note: Individual results may vary. Source
Sedai's case studies include cybersecurity (Palo Alto Networks, KnowBe4), financial services (Experian), healthcare, e-commerce (Wayfair, Campspot), IT and technology (HP, Freshworks), consumer goods (Belcorp), and digital commerce (Informed). Note: Some case studies may not be publicly available due to confidentiality. Source
Sedai is SOC 2 certified, demonstrating adherence to stringent security and data protection standards. For more details, visit the Security page. Note: For additional compliance requirements, contact Sedai directly. Source
Introducing Sed: Your cloud & AI assistant
Meet Sed
Engineering leaders on why AI agents scored 0% on the ALE benchmark's hardest tier — and what that means for deploying agents today.
Automated systems execute pre-defined rules. Autonomous systems adapt when conditions change. Here's what that means for cloud operations teams.
Google Dataflow is a powerful tool for processing large-scale data, but its costs can quickly add up without proper management. Optimizing Google Dataflow costs is crucial for organizations looking to maximize the value of their data processing pipelines while minimizing expenses.Cost optimization involves a combination of strategies, techniques, and best practices that help users effectively manage and reduce the costs associated with running Dataflow jobs. By understanding the factors that influence Dataflow pricing and implementing targeted optimizations, organizations can significantly improve their cost efficiency and overall return on investment.
Discover the best Kubernetes cost management tools for 2026 to control spend, improve performance, and scale your clusters efficiently.
Explore the best cloud storage options for 2026, comparing AWS S3, Azure Blob, Google Cloud Storage, and key features to help you choose the right solution.
In today's competitive business landscape, optimizing Azure SQL costs is not just a nice-to-have; it's a necessity. By adopting best practices and leveraging the right tools and techniques, organizations can strike the perfect balance between performance and cost-efficiency, enabling them to stay ahead of the curve in 2025 and beyond.
Optimizing Amazon EMR expenses becomes increasingly critical for organizations looking to maintain budget efficiency while ensuring high performance and scalability in their data processing workflows. By implementing effective cost optimization strategies, businesses can significantly reduce their EMR costs without compromising on workload efficiency.Understanding the key levers for cost savings in Amazon EMR is essential for making informed decisions and achieving long-term financial benefits. These strategies involve a combination of resource management techniques, data storage optimizations, and performance tuning practices that can help organizations strike the right balance between cost and performance.
