This blog discusses optimizing autoscaling in Azure Kubernetes Service (AKS) to enhance cost-effectiveness and performance. It emphasizes the Cluster Autoscaler’s role in managing node numbers and outlines best practices, including implementing availability zones, assigning CPU/memory requests, and tailoring configurations for mixed workloads. The blog also covers creating performance-focused and cost-focused autoscaler profiles, addressing common challenges like scale-up and scale-down failures, and utilizing monitoring tools like resource logs and custom metrics. By adopting these strategies, organizations can significantly improve their cloud infrastructure's efficiency and scalability.

This blog explores the importance of selecting the correct instance types for rightsizing in Google Cloud Platform (GCP) Virtual Machines (VMs), emphasizing how businesses can optimize costs and performance. It covers various GCP instance categories, including general-purpose, compute-optimized, memory-optimized, and accelerator-optimized instances, and explains the key factors to consider when rightsizing. Additionally, the blog highlights the role of GCP’s built-in tools like GCE Rightsizing Reports and the benefits of using Sedai’s AI-driven platform to automate and optimize resource allocation in real-time, ensuring efficient and cost-effective cloud infrastructure management.

Managing costs effectively in Azure Kubernetes Service (AKS) is crucial for optimizing performance and ensuring scalability without overspending. This blog delves into the key components of AKS pricing, including control plane, node pools, data transfer, and storage costs. It also explores actionable strategies for cost optimization, such as leveraging auto-scaling, Spot VMs, rightsizing resources, and utilizing cost management tags. You’ll also learn about various approaches to resource optimization—manual, automated, and autonomous—to help businesses achieve efficiency while keeping costs under control.

This blog provides an in-depth look at the key components of AWS EKS pricing, including the control plane, worker nodes, data transfer, and storage costs. It breaks down how each element contributes to overall expenses and explores cost-effective strategies tailored for EKS environments. You’ll find insights on using various pricing models, including EC2, Fargate, and Outposts, and practical tips on leveraging Reserved and Spot Instances for savings. Additionally, the article discusses how businesses can adopt manual, automated, and autonomous approaches to cost management, with a special focus on optimizing resource allocation, auto-scaling, and workload management. Learn how intelligent, real-time solutions can help you streamline EKS operations and reduce costs without sacrificing performance.

Explore the Cloud Optimization Autonomy Spectrum and its six levels, from manual operations to full AI-driven autonomy. Learn how each stage of cloud optimization can reduce costs, improve performance, and minimize manual effort on the path to autonomous cloud management

At autocon we held a panel to generate in-depth insights on the business case of AI, tackling some big questions about autonomy and AI: How are big organizations using autonomy? To what extent is autonomy helpful? How willing are companies to adopt autonomy? How does the staff react to automation?How to go about the cost of using automations?

Explore the critical role of error budgets in Site Reliability Engineering (SRE), detailing their definition, key components, and stakeholder involvement. It discusses various management approaches, the importance of maintenance windows, and how Sedai enhances error budget management through AI automation. Emphasizing continuous review reinforces balancing reliability and innovation for business success.

In today's digital landscape, service reliability is essential for building customer trust and ensuring smooth operations. This blog explores the critical components of service reliability, including availability, durability, and dependability, while providing actionable strategies for businesses to enhance their systems. We discuss the importance of running reliability tests, conducting post-incident analyses, and effective incident response processes. With tools like Sedai, organizations can streamline their efforts, ensuring they meet and exceed customer expectations. Discover how investing in service reliability can drive long-term success and satisfaction for your business.

This article explores the significance of Service Level Objectives (SLOs) in enhancing service reliability and user satisfaction. It details methodologies for setting effective SLOs, the importance of error budgets, and best practices. It also highlights how Sedai’s AI-driven platform optimizes SLO management, empowering teams to focus on innovation while ensuring continuous service stability.

Uncover key strategies for optimizing Kubernetes costs across Amazon EKS, Azure AKS, and Google GKE. This article provides an in-depth comparison of pricing models, hidden expenses, and operational overheads. Learn how to calculate total Kubernetes costs and make informed decisions based on workload size and cloud provider. Additionally, discover best practices for optimizing multi-cloud and hybrid cloud strategies to achieve cost-efficiency in managing Kubernetes clusters.

Discover essential strategies for maximizing cloud efficiency while minimizing costs. This article delves into optimizing cloud resources, managing cloud infrastructure effectively, and avoiding common pitfalls like over-provisioning and underutilization. Explore the financial, performance, and environmental impacts of cloud inefficiency, and uncover best practices for rightsizing, automated resource scaling, and sustainable operations. Learn how to leverage advanced tools to drive efficiency across AWS, Azure, and Google Cloud platforms.

Understanding system availability is crucial for maintaining uptime in today's digital infrastructure. This article explores vital availability metrics, common causes of downtime, and how AI-driven platforms like Sedai can proactively enhance availability, reduce Failed Customer Interactions (FCIs), and optimize system performance for better efficiency.

This blog highlights the top tools for Amazon ECS cost optimization, focusing on AI-driven platforms like Sedai and CloudHealth. It covers features such as task rightsizing, auto-scaling, and instance optimization, helping businesses reduce cloud costs and enhance ECS performance.

Explore data-driven cloud infrastructure decisions with insights from Siddharth Ram. Learn about usage-based vs. time-based pricing models and optimize your cloud costs effectively.

Explore 18 essential FinOps platforms and tools for 2024, designed to optimize cloud costs, improve financial management, and drive efficiency in cloud operations. Find the best solutions to enhance your FinOps strategy and maximize cloud investments.

Learn how to optimize Kubernetes capacity with resource management, autoscaling, and cost control strategies. Explore tools like Sedai, Keda, and Karpenter for efficient Kubernetes environments.

Discover the top Kubernetes cost management tools for 2024 to optimize resources, performance, and cloud spending.

Discover the top 18 cloud cost management tools for 2024. Learn how to optimize your cloud spend, reduce waste, and align costs with business goals using AI-driven platforms across AWS, Azure, and Google Cloud.

Explore the top 13 cloud management platforms for 2024. Learn about key features, emerging trends, and how to choose the right solution for your organization's multi-cloud strategy and optimization needs.

Discover how cloud optimization goes beyond cost-cutting to boost revenue. Learn about FCI, latency, and availability losses, and how to minimize them for maximum profitability.

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

Discover how AI is transforming FinOps with autonomous optimization, predictive forecasting, and intelligent cost management. Learn 10 key ways AI revolutionizes cloud financial operations and prepares your organization for the future of FinOps.

Optimize Kubernetes performance and cost with Karpenter and Sedai. Learn how AI-driven insights enhance node provisioning, predictive scaling, and instance selection.

Learn how to seamlessly integrate Infrastructure as Code (IaC) with autonomous systems for cloud cost & resource optimization. This guide explores strategies for maintaining DevOps best practices while leveraging AI-driven optimization in CI/CD pipelines, ensuring your IaC remains accurate and your cloud costs are minimized. Ci/CD tools mentioned include Jenkins, Argo CD, Circle CI and GitLab CD. IaC tools mentioned include Terraform, Ansible, AWS Cloud Development Kit (CDK), Puppet and Helm. A case study showing how Gitlab IaC is integrated with Sedai's autonomous optimization capability at KnowBe4, a leading cybersecurity platform is included.

Explore the key value drivers of autonomous cloud management, guided by the framework in the diagram below which focuses on four main drivers: Cloud Cost Reduction, Performance Gains, Availability Gains and Operational Time Saved.

When in the course of technological evolution, it becomes necessary for engineers to break free from repetitive tasks and embrace innovation, a new approach is required. We hold these truths to be self-evident that all engineers deserve the right to meaningful, creative, and innovative work, free from the burdens of toil. Join the movement towards efficiency and freedom from toil.

Discover why many organizations choose Reservation Plans over Savings Plans in cloud financial optimization. Explore the benefits of cost predictability, higher discounts, enterprise agreements, and accounting advantages, even with minimal additional costs. Learn how Reservation Plans can impact your financial strategy and cloud usage effectively.

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.

While each organization’s approach is different let’s consider a journey through four stages of maturity from high-touch, manual processes to sophisticated, intelligent systems that enhance performance and cost-efficiency with minimal human intervention.

The development of autonomous vehicles as a solution to vehicle accidents provides parallels for the development of autonomous cloud management as a path to incident reduction. Explore key lessons on reducing human error, leveraging AI, incremental progress, phased rollouts, and building user trust to enhance system reliability and safety.

Traditional manual and rule-based automated systems struggle to keep up with the complexity of managing cloud environments leads to persistent issues with cost control and performance optimization. It's time to take the next step in cloud management—AI-driven autonomous cloud management platforms are solving the complexity challenge in modern cloud environments.

Amazon Simple Storage Service (Amazon S3) is a widely-used object storage service that offers industry-leading scalability, data availability, security, and performance. S3 has a variety of storage classes.

Key considerations when choosing Amazon S3 storage class include access frequency, performance (retrieval time), availability, durability, and cost. S3 Standard is ideal for frequently accessed data. S3 Standard-IA and One Zone-IA are cost-effective for infrequent access. Glacier, Glacier Flexible Retrieval, and Glacier Deep Archive offer substantial cost savings for long-term archival storage. Intelligent Tiering is beneficial for unpredictable access patterns, automating cost savings by transitioning data between tiers.

Discover AWS pricing models for ECS in this guide, covering On-Demand, Reserved Instances, Savings Plans, and Spot Instances. Learn how On-Demand instances offer a flexible pay-as-you-go model for fluctuating workloads, and explore the long-term discounts available with Reserved Instances and Savings Plans for predictable usage. Delve into Spot Instances, which provide steep discounts for fault-tolerant or stateless applications. This article also explains how to optimize costs and ensure availability by mixing On-Demand and Spot capacities, with a focus on ECS’s capabilities in automating Spot Instance lifecycle management and integrating with AWS Auto Scaling Groups. Perfect for optimizing your AWS spending strategy.

Explore four essential engineering optimizations for Amazon ECS - rightsizing, task placement, autoscaling, and scheduled shutdowns. Learn how to enhance ECS service performance and reduce costs with real-world examples of CPU optimization, memory efficiency, and task management. Discover the benefits of AWS Graviton instances for cost-efficiency and performance, strategic deployment through task placement, and cost savings with larger instance types. Understand how autoscaling adapts to traffic changes and how scheduled resource shutdowns during off-peak hours can further reduce expenses. Optimize your AWS environment for better performance and cost-effectiveness with these proven strategies.

Successful ECS optimization impacts financial performance including both cost savings through reduced overprovisioning and discount management, and revenue gains from application performance and latency gains. While ECS offers a range of controls and strategies, including rightsizing and utilizing spot instances, implementation is complex when managed with manual methods.

In this post we'll take a look at ECS vs other Amazon Compute models and some key concepts in Amazon ECS.

In environments where applications are not suitable for microservices architectures, rightsizing and in particular vertical scaling becomes a critical strategy to achieve cost-effective operations while meeting performance requirements.

In environments where applications are not suitable for microservices architectures, rightsizing and in particular vertical scaling becomes a critical strategy to achieve cost-effective operations while meeting performance requirements.

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.

Keeping Amazon ECS development & testing environments workloads running when they're not actively used can lead to considerable expenses for teams.Implementing scheduled shutdowns of Amazon ECS cloud resources when not in use can lead to cloud cost savings with relatively little implementation effort.Four ways to achieve shutdowns for Amazon ECS include (1) manual shutdowns, (2) Lambda functions, (3) Cloudwatch templates and (4) schedules inside a platform such as Sedai.

Learn about five key differences between autonomous and traditional automated systems. These include 1. Intelligent Decision-Making, 2. Dynamic Adaptability and Context-Awareness, 3. Self-Correcting Capability, 4. Complexity and 5. Scalability and Limited Human Oversight.

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.

Cloud cost and time savings drive the "above the surface" benefits of autonomous cloud management, but "below the surface" benefits including customer experience gains, release quality, employee retention and adaptability contribute to the overall value realized from autonomous cloud management.

Your autonomous business case should consider cloud cost savings, performance gains, availability improvements and time savings. Operations teams looking to adopt autonomous optimization need to show business value to get buy-in from leadership.

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.

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.

fabric is a modular and headless platform tailored for modern eCommerce applications, with a primary focus on Software-as-a-Service (SaaS) solutions. fabric's mission is to empower retailers to streamline their commerce operations across various channels by creating custom functions through interconnected components. Gone are the days of relying solely on prepackaged systems; they embrace a more flexible and adaptable approach to meet the evolving needs of their clients.

Manu Thapar, CTO of Mastercard discusses the application of AI/ML to credit card fraud detection systems and how moving from a rules based system to a system that can self-learn and operate autonomously was key to successfully keeping up with constantly changing credit card fraud risks. Manu spoke at autocon, Sedai's annual conference focused on autonomous cloud management.

In today's fast-paced and demanding IT world, professionals and individuals face increasing pressure to do more with less time for evaluation and beta testing. The focus on delivering value to customers is often overshadowed by flashy marketing gimmicks. However, customer satisfaction is crucial for business success. The realm of artificial intelligence (AI) and machine learning (ML) has shifted towards practical applications and collaborative autonomy, where autonomous systems work together with humans. This article delves into the practical aspects of autonomy, discussing the challenges and benefits of implementing autonomous cloud management in various industries. It highlights insights from industry experts on the transition to autonomy, practical approaches, building trust, and managing expectations. The article showcases real-world examples of organizations such as Palo Alto Networks, Paylocity, and Fabric that have successfully implemented autonomous systems to enhance productivity, efficiency, and customer satisfaction.

This article is my summary of an insightful panel discussion on the topic of Serverless versus Kubernetes. We will delve into the valuable perspectives shared by esteemed industry professionals during the panel. The discussion aims to shed light on the relationship between serverless architectures and Kubernetes, focusing on the logic behind utilizing Kubernetes for stateless applications. Through an exploration of different viewpoints, we aim to gain a better understanding of the merits and considerations surrounding these two approaches. You can watch the full video here.

In this article which serves as a summary of a collaborative panel discussion on monitoring and optimization strategies for cloud-based systems, we delve into the exciting advancements in this rapidly evolving landscape. We focus on crucial aspects such as cost management, performance optimization, and the emergence of autonomous solutions. Join us as we explore insights from the panelists and gain valuable perspectives on these topics. You can watch the full video here.

In this article, we delve into the reintroduction of autonomous infrastructure within the realm of large-scale systems, shedding light on the transformative changes that have taken place in infrastructure development over the past few decades. The insights shared here are the result of a collaborative effort involving multiple speakers who engaged in a thought-provoking discussion, comparing the distinctions between traditional monolithic systems and emerging paradigms such as Kubernetes and serverless environments. The focus lies on highlighting the advantages offered by enhanced flexibility and access to a diverse range of tools. As the conversation unfolds, we shift our attention towards the crucial role that autonomous systems play in this dynamic landscape and why relying solely on automation falls short of our aspirations. By embracing autonomy, we open up new possibilities to transcend the previously inevitable trade-offs that accompanied investment decisions, business choices, and market strategies. We reflect on the age-old dilemma where one must choose between something being good, fast, or cheap, realizing that achieving all three simultaneously has long been an elusive goal. However, our speakers firmly believe that with advancements in technology and the successful implementation of autonomy, these trade-offs can be overcome.

Is Tesla known for building the safest car on earth? They made an interesting discovery regarding task division. By examining all operations, they identified two distinct segments: One that can be effectively performed by machines and another that is better suited for humans. When the first segment is completely handled by machines without any human intervention, it is referred to as an autonomous system. This means that the system operates autonomously.

In this article we will explore the reasons behind our adoption of Kubernetes, delve into the concept of autoscaling within Kubernetes, and specifically focus on cluster autoscaling. Throughout this article, we will examine the various tools that enable cluster autoscaling configuration

In the past few years, we have seen a huge increase in adoption for Kubernetes. Kubernetes is an excellent platform, which provides you with all the facilities to fine-tune your application and run it the way you need. We at Sedai, use Kubernetes for deploying our applications along with surveillance and some other managed services from AWS. One question we get a lot is “Is there a need for another system or an autonomous system to manage Kubernetes?” Kubernetes is in fact a declarative system, an excellent one at which you can specify the needs of running your application, and Kubernetes will take care of the rest.

In this article, we will explore the world of optimization, with a special focus on serverless functions, particularly those leveraging Lambda. We will dive into how Sedai, a cutting-edge solution, helps in optimizing serverless functions. Sedai offers invaluable insights and tools that enable businesses to identify and address niche areas where optimization can make a significant impact.

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.

Datadog can be utilized to achieve autonomous cloud environments in just 15 minutes. We'll discuss the partnership between Sedai and DataDog, the integration they have developed, and how customers can leverage Datadog to automate manual processes. The goal is to guide customers towards a fully autonomous system. By following our guidance, you can transform your investment in Datadog into a self-driving engine. Stay tuned as we reveal the steps to unlock the full potential of Datadog and empower your cloud environments.

In this article, we'll be exploring an important topic: Lambda Extensions. These extensions are a powerful feature that was introduced last year. So, let's jump straight into it and understand the details of this valuable addition.

Failed Customer Interactions (FCIs) are a valuable metric measuring customer experience that accounts for the declining usefulness of time based measures such as uptime in microservice based architectures. It provides valuable insights into system availability from a customer-centric standpoint.

Learn strategies for enhanced system performance and reliability. See how observability metrics lay the foundation for autonomous systems, and how Palo Alto Networks approaches production metrics. Explore the potential problems associated with an overwhelming influx of data and metrics and how ML-driven correlation analysis can identify truly predictive metrics in an environment with multiple monitoring providers, multiple resource types, real-time data stress and predictive analytics. See how Palo Alto Network uses a multi-stage approach that includes a notice filter, aggregation, analysis, auto-remediation and notifications.

A comprehensive overview of how Sedai addresses the challenge of cold starts through the implementation of autonomous concurrency. We explore the history of cold starts, their underlying causes, and their relationship with concurrency before showing how an AI based approach can resolve cold starts.

This article, based on the kickoff session from the autocon/22 conference, sets the stage for the event by outlining the significance of autonomous systems in cloud management. It explores how autonomous systems have revolutionized various industries and highlights their transformative potential.

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.

Discover how Sedai brought AI-driven autonomous cloud management to SREcon23 Americas. Learn about our platform demos, magician entertainment, and special offers for conference attendees. Explore how Sedai can help SREs optimize resources, reduce costs, and prevent downtime

It's been a fantastic year for Sedai, and we're proud to share our year-end accomplishments with you in five areas.

Sedai CEO Suresh Mathew shares eight predictions for 2023 and explains what they could mean for the adoption of autonomous cloud management.

Our Datadog Dash 2022 survey found cost is the #1 challenge for Datadog users, that autonomous systems are on average expected to provide a 48% gain on these challenges, and that Datadog users are well down the path of running modern apps with 77% using containers and 36% using serverless.

Sedai is a launch partner for AWS for their new Telemetry API for AWS Lambda. Sedai uses the telemetry API to improve availability for Lambda users of Sedai’s autonomous cloud management platform by providing additional insights and signals on Lambda functions on a cost-effective basis.

Sedai has been accepted into the AWS Independent Software Vendor (ISV) Accelerate Program.

Microservices architectures are rapidly becoming the new norm architects rely on when it comes to cloud computing. There has been a lot of debate whether it's best to shift left, or shift right. With Microservices, organizations must shift up, and manage their systems autonomously.

Performance & latency matter for B2B SaaS Applications, impacting user experience and ultimately retention and revenue.

We are proud to sponsor the DevOps Institute 2022 State of SRE report.

We raised more than $15M in Series A funding led by Norwest Venture Partners. We are defining the future of SRE. Join us in the autonomous movement!