Best Auto Scaling Software

Simplify application delivery with software-defined load balancers, web application firewall, and container ingress services for any application in any data center and cloud. Simplify administration with centralized policies and operational consistency across on-premises data centers, and hybrid and public clouds, including VMware Cloud (VMC on AWS, OCVS, AVS, GCVE), AWS, Azure, Google, and Oracle Cloud. Free infrastructure teams from manual tasks and enable DevOps teams with self-service. Application delivery automation toolkits include Python SDK, RESTful APIs, Ansible and Terraform integrations. Gain unprecedented insights, including network, end users and security, with real-time application performance monitoring, closed-loop analytics and deep machine learning.

StormForge Optimize Live continuously rightsizes Kubernetes workloads to ensure cloud-native applications are both cost effective and performant while removing developer toil. As a vertical rightsizing solution, Optimize Live is autonomous, tunable, and works seamlessly with the Kubernetes horizontal pod autoscaler (HPA) at enterprise scale. Optimize Live addresses both over- and under-provisioned workloads by analyzing usage data with advanced machine learning to recommend optimal resource requests and limits. Recommendations can be deployed automatically on a flexible schedule, accounting for changes in traffic patterns or application resource requirements, ensuring that workloads are always right-sized, and freeing developers from the toil and cognitive load of infrastructure sizing. Organizations see immediate benefits from the reduction of wasted resources — leading to cost savings of 40-60% along with performance and reliability improvements across the entire estate.

CAST AI is an automated Kubernetes cost monitoring, optimization and security platform for your EKS, AKS and GKE clusters. The company’s platform goes beyond monitoring clusters and making recommendations; it utilizes advanced machine learning algorithms to analyze and automatically optimize clusters, saving customers 50% or more on their cloud spend, and improving performance and reliability to boost DevOps and engineering productivity.

Pepperdata autonomous cost optimization for data-intensive workloads such as Apache Spark is the only solution that delivers 30-47% greater cost savings continuously and in real time with no application changes or manual tuning. Deployed on over 20,000+ clusters, Pepperdata Capacity Optimizer provides resource optimization and full-stack observability in some of the largest and most complex environments in the world, enabling customers to run Spark on 30% less infrastructure on average. In the last decade, Pepperdata has helped top enterprises such as Citibank, Autodesk, Royal Bank of Canada, members of the Fortune 10, and mid-sized companies save over $250 million.

Xosphere Instance Orchestrator automatically performs spot optimization by leveraging AWS Spot instances to optimize the cost of your infrastructure while maintaining the same level of reliability as on-demand instances. Spot instances are diversified amongst family, size, and availability zones to minimize any impact when Spot instances are reclaimed. Instances utilizing reservations will not be replaced by Spot instances. Automatically respond to Spot termination notifications and fast-track replacement on-demand instances. EBS volumes can be configured to be attached to new replacement instances enabling stateful applications to work seamlessly.

Auto Scaling is a service to automatically adjust computing resources based on your volume of user requests. When the demand for computing resources increase, Auto Scaling automatically adds ECS instances to serve additional user requests, or alternatively removes instances in the case of decreased user requests. Automatically adjusts computing resources according to various scaling policies. Supports manual scale-in and scale-out, which offer you flexibility to control resources manually. During peak periods, automatically adds additional computing resources to the pool. When user requests decrease, Auto Scaling automatically releases ECS resources to cut down your costs

Amazon EC2 Auto Scaling helps you maintain application availability and lets you automatically add or remove EC2 instances using scaling policies that you define. Dynamic or predictive scaling policies let you add or remove EC2 instance capacity to service established or real-time demand patterns. The fleet management features of Amazon EC2 Auto Scaling help maintain the health and availability of your fleet. Automation is vital to efficient DevOps, and getting your fleets of Amazon EC2 instances to launch, provision software, and self-heal automatically is a key challenge. Amazon EC2 Auto Scaling provides essential features for each of these instance lifecycle automation steps. Use machine learning to predict and schedule the right number of EC2 instances to anticipate approaching traffic changes.

UbiOps is an AI infrastructure platform that helps teams to quickly run their AI & ML workloads as reliable and secure microservices, without upending their existing workflows. Integrate UbiOps seamlessly into your data science workbench within minutes, and avoid the time-consuming burden of setting up and managing expensive cloud infrastructure. Whether you are a start-up looking to launch an AI product, or a data science team at a large organization. UbiOps will be there for you as a reliable backbone for any AI or ML service. Scale your AI workloads dynamically with usage without paying for idle time. Accelerate model training and inference with instant on-demand access to powerful GPUs enhanced with serverless, multi-cloud workload distribution.

NVIDIA DGX Cloud Serverless Inference is a high-performance, serverless AI inference solution that accelerates AI innovation with auto-scaling, cost-efficient GPU utilization, multi-cloud flexibility, and seamless scalability. With NVIDIA DGX Cloud Serverless Inference, you can scale down to zero instances during periods of inactivity to optimize resource utilization and reduce costs. There's no extra cost for cold-boot start times, and the system is optimized to minimize them. NVIDIA DGX Cloud Serverless Inference is powered by NVIDIA Cloud Functions (NVCF), which offers robust observability features. It allows you to integrate your preferred monitoring tools, such as Splunk, for comprehensive insights into your AI workloads. NVCF offers flexible deployment options for NIM microservices while allowing you to bring your own containers, models, and Helm charts.