Cloud Cost & Performance Optimization

Discover strategies to manage cloud computing costs across AWS, Azure, and Google Cloud in 2025. This article offers an in-depth comparison of pricing models, hidden fees, and key cost components such as computing, storage, and networking. Learn how to calculate total cloud expenses and make informed decisions based on your infrastructure needs and cloud provider. Additionally, uncover best practices for optimizing cloud resources through techniques like right-sizing, autoscaling, and leveraging discounts to achieve cost-efficiency across your cloud environments.

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.

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.

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 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 covers effective strategies for optimizing costs on Microsoft Azure, including using Azure’s native tools like Cost Management and Billing, Azure Advisor, and the Pricing Calculator for tracking and estimating expenses. It highlights the importance of right-sizing resources, utilizing Azure Reservations and Spot VMs, and setting up autoscaling to match demand. The post also emphasizes financial strategies like the Azure Hybrid Benefit and introduces Sedai’s AI-driven platform for continuous, autonomous cost optimization, ensuring businesses can maintain cost efficiency while maximizing performance.

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.

Optimizing costs in Google Kubernetes Engine (GKE) is crucial for businesses seeking to balance cloud spending with operational efficiency. This post explores essential strategies for achieving cost savings without compromising performance. Key practices include adjusting pod resource requests and limits, leveraging autoscaling for dynamic resource management, and utilizing Spot VMs for non-critical workloads to take advantage of significant cost reductions. Understanding GKE’s pricing models, such as committed use discounts (CUDs) and sustained use discounts (SUDs), is also essential for long-term cost management.

Managing Azure Kubernetes Service (AKS) efficiently requires automation to prevent unnecessary cloud costs. This article explores methods to automate AKS cluster shutdowns and startups using Azure Automation, PowerShell, Logic Apps, and Sedai. Learn how to optimize costs, reduce manual interventions, and ensure clusters run only when needed. Discover best practices, scripts, and scheduling techniques for smarter AKS management.

Best practices for cloud cost management and optimization include cost visibility, automating infrastructure rightsizing, and building cost-awareness culture within teams.

In a recent survey conducted by McKinsey with IT directors and IT organizations, one of the key takeaways was that FinOps is one of the most important and emerging practices that they have been adopting throughout their IT and technology organizations in the last couple of years.

A technology company achieved a 25% cost saving across 1,400 Kubernetes services with Sedai's autonomous optimization technology by rightsizing Dev/Test environments. AI-powered autonomous optimization optimized Kubernetes requests and limits in Kubernetes workloads and optimized instance count and type. An AI driven approach was more effective than manual optimization, especially given the small spend on each individual service. Optimization is one key capability of Sedai's autonomous cloud management platform.

KnowBe4's autonomous journey has led to 98% of their Amazon. ECS and Lambda services running autonomously, with a 27% cost reduction and over 1,100 autonomous actions in the past 3 months. KnowBe4 fhad aced an optimization challenge with their Amazon Elastic Container Service (ECS) services, leading them to adopt Sedai's autonomous optimization to reduce toil for engineers and improve efficiency. KnowBe4 implemented a three-part approach (Crawl, Walk, Run) to gradually adopt autonomous optimization, resulting in significant cost savings and performance gains.

In this post, we will cover how to master Amazon ECS optimization using autonomous techniques. The autonomous approach can help with both ECS cost optimization and performance optimization.

To optimize AWS ECS costs, focus on workload optimization by right-sizing resources, configuring efficient infrastructure with the appropriate instance types and mix, and making strategic purchasing decisions such as leveraging Savings Plans or Reserved Instances.

Explore the future of cloud cost optimization and learn how to leverage AI and machine learning to scale cloud operations. Gain insights on how modern autonomous cost optimization solutions compare to traditional automation.

Hello everyone! In this article, we will discuss seven effective strategies to optimize costs for AWS ECS. This discussion is based on the joint webinar between Sedai and AWS, which you can watch here.

Walkthrough of Sedai's autonomous optimization for ECS showing ECS service optimization, instance optimization, and purchasing levers. An autonomous cloud-management platform designed to optimize performance, cost, and availability for your ECS (Elastic Container Service) clusters.

Today, we're going to explore the concept of running a software company without the need for an operations team or an SRE team. We'll discuss the evolving landscape of operations and its implications for developers. This discussion will be presented from a developer's perspective.

We explore the key role of autoscaling in optimizing performance and cost within Kubernetes. Specifically, we delve into two critical autoscalers—Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA)—and shed light on their functionalities, benefits, and limitations.