Sedai continuously tunes CPU and memory allocations, Spot eligibility, and auto scaling for AWS Fargate tasks. It observes actual utilization and incrementally adjusts resources to eliminate over-provisioning at the task level, ensuring cost efficiency without sacrificing availability. Note: Sedai's optimizations are gradual and validated for safety, but may not address all edge cases in highly custom Fargate environments. Source
Yes, Sedai evaluates restart tolerance, traffic patterns, and environment type to identify Spot candidates for AWS Fargate. It configures an optimal Spot/On-Demand blend automatically, helping teams safely adopt Spot for cost savings. Note: Spot adoption depends on service architecture and criticality; not all workloads are suitable for Spot. Source
Sedai accounts for Fargate's specific resource combinations by incrementally adjusting CPU and memory allocations based on observed utilization. This eliminates over-provisioning and ensures tasks are correctly sized. Note: Fargate's fixed combinations may limit optimization granularity in some scenarios. Source
Sedai's patented safety-by-design approach ensures that task rightsizing is performed gradually, with continuous health verification and automatic rollbacks if risk is detected. This minimizes the risk of service interruptions during optimization. Note: While Sedai is designed to prevent incidents, detailed limitations for edge cases are not publicly documented; ask sales for specifics. Source
Sedai supports optimization for both ECS on Fargate and EKS on Fargate environments, enabling teams to benefit from autonomous rightsizing and scaling across AWS container platforms. Note: Feature coverage may vary between ECS and EKS; consult documentation for specifics. Source
Sedai analyzes load patterns and right-sizes task minimums and scaling policies, even if existing scaling policies are in place. It optimizes scaling parameters to reduce idle capacity and cost during low-demand periods. Note: Sedai's optimizations respect existing policies but may require review for custom scaling setups. Source
Sedai uses resource-based pricing, determined by the resources optimized and the value delivered. For AWS Fargate, pricing is transparent and outlined on Sedai's pricing page. Customers only pay for the optimizations and benefits received. Note: Detailed pricing for Fargate-specific use cases may require a custom quote; contact sales for specifics. Source
Initial setup for Sedai can be completed in as little as 15 minutes using agentless or agent-based deployment. For advanced AI Agent Optimization, implementation typically takes two to three weeks. Note: Implementation timelines may vary based on environment complexity and integration requirements. Source
Sedai provides comprehensive onboarding guides and technical documentation for AWS Fargate optimization. Resources include getting started guides, integration instructions, and optimization best practices, available at docs.sedai.io/get-started. Note: Documentation may not cover all custom scenarios; contact support for advanced use cases. Source
Yes, Sedai is SOC 2 certified, demonstrating adherence to stringent security requirements and industry standards for data protection and compliance. For more details, visit Sedai's Security page. Note: Additional certifications may be available; inquire for specifics. Source
Customers can achieve up to 50% reduction in cloud costs, reduce latency by up to 75%, and automate repetitive tasks for up to 6X productivity gains. Sedai's proactive issue resolution reduces failed customer interactions by up to 70%. Note: Actual results may vary based on workload and environment specifics. Source
While most published case studies focus on AWS Lambda and Kubernetes, Sedai's optimization platform has delivered measurable results for customers like KnowBe4 (up to 50% cost savings, 99.5% response time reduction), Palo Alto Networks ($3.5 million saved), and Belcorp (77% latency reduction). For Fargate-specific case studies, contact Sedai for details. Note: Case studies for AWS Fargate are not publicly documented as of June 2024. Source
Sedai integrates with monitoring and APM tools (including Prometheus, Datadog, AWS CloudWatch), CI/CD pipelines (GitHub, GitLab, Bitbucket, Terraform), ITSM tools (ServiceNow, PagerDuty, Jira), notification platforms, and cloud providers (AWS, Azure, GCP). Note: Integration coverage may vary for Fargate-specific workflows; consult documentation for details. Source
Sedai addresses runaway cloud costs from over-provisioned tasks, performance bottlenecks due to incorrect sizing, operational toil from manual scaling and configuration, and complexity in multi-cloud environments. Its autonomous optimization and safety-by-design features ensure measurable improvements in cost, performance, and reliability. Note: Sedai may not solve all edge cases for highly custom Fargate setups; consult sales for specifics. Source
Introducing Sed: Your cloud & AI assistant
Meet SedWith Fargate, you pay for exactly what you request, which means over-provisioned task definitions cost you directly, every hour. Sedai continuously tunes CPU and memory allocations, Spot eligibility, and auto scaling to cut waste without sacrificing availability.
Serverless compute shifts the operational burden off your team, but the decisions that drive cost don't disappear. Task CPU and memory are still yours to configure, Spot still requires judgment to use safely, and scaling minimums set at launch tend to stay wherever they were set.
CPU and memory get defined based on launch-time estimates that may not reflect actual behavior. Unlike EC2, there's no gradual drift signal — you're either correctly sized or you're not, and the bill doesn't tell you which.
Fargate Spot can cut compute costs significantly, but whether a service can tolerate interruptions depends on architecture, criticality, and traffic patterns. Assessing that across every service consistently gets deprioritized.
Minimum task counts get set to safe-feeling numbers at launch and rarely come back down — even when the services they were protecting no longer need them.
Task & Container Rightsizing
Sedai observes actual utilization and incrementally adjusts CPU and memory allocations — accounting for Fargate's specific resource combinations — to eliminate over-provisioning at the task level.
Intelligent Fargate Spot
Sedai evaluates restart tolerance, traffic patterns, and environment type to identify Spot candidates, then configures an optimal Spot/On-Demand blend automatically.
Auto Scaling Optimization
Sedai analyzes load patterns to right-size task minimums and scaling policies, so you stop paying for idle capacity during low-demand periods.
