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AWS Spot Instances can help businesses cut EC2 costs by up to 90%, but their unpredictability makes them challenging to manage. With Sedai’s AI-powered automation, you can seamlessly optimize Spot Instance usage, prevent service disruptions, and maximize cloud savings—without manual effort. Sedai dynamically shifts workloads between Spot, On-Demand, and Reserved Instances, ensuring high availability and cost efficiency.

Google Cloud Dataflow and AWS Kinesis Data Analytics are two leading solutions for real-time data processing, each catering to different needs. Dataflow offers a unified model for batch and stream processing using Apache Beam, making it ideal for complex workflows. Kinesis Data Analytics, on the other hand, provides low-latency stream processing with deep AWS integration. This blog compares their architecture, scalability, cost, security, and use cases to help you choose the best fit for your data pipeline. Plus, discover how AI-driven optimization with Sedai can enhance performance and cost efficiency.

Discover key strategies to optimize Amazon EKS costs, from resource right-sizing and autoscaling to efficient instance selection. Understand how Sedai’s autonomous platform drives cost-effective management by continuously monitoring and adjusting workloads in real time, reducing the need for manual intervention. Learn how proactive optimization through tools like Sedai can help you control expenses across EKS components—like EC2 instances, storage, data transfer, and load balancing—ensuring a scalable, high-performing Kubernetes environment on AWS that aligns with your budget.

Sedai has been recognized as both a Leader and Outperformer in the 2025 GigaOm Radar for Kubernetes Resource Management. Discover how our AI-powered platform delivers 15-50% cloud cost reduction while improving application performance by up to 30% for enterprises like Palo Alto Networks, Experian, and HP.

This blog explores how to use Spot Instances in Azure Kubernetes Service (AKS) to scale workloads while maximizing cost savings. It covers adding Spot Node Pools, best practices for managing evictions, and optimizing Kubernetes costs using Spot VMs. You’ll learn how tools like Cluster Autoscaler and Sedai’s autonomous optimization can help maintain high availability even with potential interruptions. By blending Spot and On-Demand instances, you can strike the perfect balance between resilience and efficiency, achieving a scalable, cost-effective AKS environment with minimal manual intervention.

This article explores the critical roles of Service Level Agreements (SLAs), Service Level Objectives (SLOs), and Service Level Indicators (SLIs) in enhancing business performance and customer satisfaction. It emphasizes best practices for implementation and highlights how Sedai's innovative solutions optimize service level management through automation and real-time adjustments.

This article explores the importance of rightsizing Azure Virtual Machines (VMs) to optimize both cost-efficiency and performance. It covers various Azure VM instance types and provides best practices for determining the right size based on specific workloads. Key tools like Azure Advisor and Azure Monitor are highlighted for continuous monitoring and optimization, while Sedai's AI-driven platform is recommended for autonomous VM rightsizing. The blog also discusses common challenges in manual rightsizing, the benefits of vertical and horizontal scaling, and the need for continuous optimization to meet evolving business demands.

This article delves into effective strategies for optimizing AWS Lambda costs, focusing on memory allocation, request management, and leveraging ARM-based architectures. It outlines cost-saving techniques, including the benefits of provisioned concurrency and batching requests, to reduce invocation frequency. Additionally, the article highlights the role of Sedai’s autonomous optimization platform, which adjusts configurations in real-time to align Lambda functions with both performance targets and budgetary constraints, ensuring efficient cost management in serverless operations.

AWS Auto Scaling automatically adjusts cloud resources to optimize performance and costs. Integrated with Sedai, businesses benefit from AI-powered resource management, automated scaling, and real-time cost optimization. This combination helps reduce over-provisioning and ensures peak cloud infrastructure performance.

Optimizing cloud costs and performance is a challenge for many enterprises, with AWS services like Lambda, EKS, ECS, and EC2 offering different capabilities. This guide compares these services based on scalability, cost, ease of management, and performance to help you choose the right fit for your cloud applications. Whether you need a serverless solution, container orchestration, or full infrastructure control, understanding the trade-offs can streamline operations.

Optimize your Kubernetes clusters with Sedai! Discover how our autonomous platform detects orphaned resources, improves performance, and reduces costs. Sedai fine-tunes workload configurations, manages resources efficiently, and ensures scalability.

This post explores the importance of workload rightsizing in Amazon Elastic Kubernetes Service (EKS) for enhancing cost efficiency and application performance. It covers strategies for configuring CPU and memory requests, utilizing autoscalers like Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA), and leveraging observability tools for continuous monitoring. The role of Sedai's AI-driven platform is emphasized as a key solution for automating rightsizing processes, ultimately helping organizations optimize resource utilization and adapt to changing workload demands effectively.

This comprehensive guide explores top AWS Lambda optimization tools for 2025, addressing key challenges like memory tuning, cold start management, and concurrency issues. The article highlights five specialized optimization solutions including Sedai (AI-powered real-time optimization), Lambda Power Tuning (memory allocation optimization), AWS Compute Optimizer (machine learning recommendations), AWS Lambda Cold Start Annotations (latency analysis), and manual tuning approaches.

Efficient bin packing in Kubernetes optimizes resource usage, reducing AWS EC2 costs. This guide explores strategies like NodeResourcesFit and custom schedulers for better performance. Sedai's autonomous solution leverages application awareness to enhance node utilization, providing significant cost savings and efficiency.

This article explores the importance of optimizing AWS Lambda concurrency for handling high-demand scenarios. It highlights key concurrency controls, monitoring methods, and the benefits of using Sedai for autonomous optimization to reduce costs and ensure efficient, scalable performance.

This blog dives into effective strategies for optimizing Azure Kubernetes Service (AKS) costs, focusing on resource right-sizing, autoscaling, and leveraging cost-saving pricing models like Spot VMs and Reserved Instances. It highlights best practices such as regular resource audits, tagging for cost tracking, and training teams on cost-efficient Kubernetes practices. The blog also introduces Sedai’s autonomous optimization platform, which automates resource adjustments and scaling to minimize costs while maintaining performance. Practical insights and actionable tips empower teams to manage AKS clusters efficiently, balance cost with reliability, and thrive in a cloud-driven environment.

This blog explores the strategy of running Kubernetes clusters on spot instances, a cost-saving approach that taps into unused cloud capacity. It covers key benefits of using spot instances in Kubernetes, best practices for autoscaling, and methods for managing instance interruptions to maintain workload stability. Additionally, it introduces Sedai's autonomous optimization platform, which enhances spot instance management through real-time adjustments and predictive analytics, minimizing manual intervention. Practical steps for node group configuration, pod scheduling, and balancing reliability with cost-efficiency are also included to help teams optimize Kubernetes clusters for cost-effective, resilient operations.