Blog

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.

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

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.

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.

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.

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.

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.

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

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.