Best Auto Scaling Software with a Free Trial

Google Compute Engine's auto scaling feature automatically adjusts the number of virtual machine instances in response to fluctuations in traffic or workload demands. This ensures that applications maintain optimal performance without manual intervention and helps to reduce unnecessary costs by scaling down when demand is low. Users can configure scaling policies based on specific criteria, such as CPU utilization or request rate, to further customize how resources are allocated. New customers receive $300 in free credits, enabling them to test and fine-tune auto scaling for their unique workloads.

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.

StarTree, powered by Apache Pinot™, is a fully managed real-time analytics platform built for customer-facing applications that demand instant insights on the freshest data. Unlike traditional data warehouses or OLTP databases—optimized for back-office reporting or transactions—StarTree is engineered for real-time OLAP at true scale, meaning: - Data Volume: query performance sustained at petabyte scale - Ingest Rates: millions of events per second, continuously indexed for freshness - Concurrency: thousands to millions of simultaneous users served with sub-second latency With StarTree, businesses deliver always-fresh insights at interactive speed, enabling applications that personalize, monitor, and act in real time.

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.

Zerops.io is a cloud platform designed for developers building modern applications, offering automatic vertical and horizontal autoscaling, granular control over resources, and no vendor lock-in. It simplifies infrastructure management with features like automated backups and failover, CI/CD integration, and full observability. Zerops.io scales seamlessly with your project’s needs, ensuring optimal performance and cost-efficiency from development to production, all while supporting microservices and complex architectures. Ideal for developers who want flexibility, scalability, and powerful automation without the complexity.

Zipher is an autonomous optimization platform specifically designed to improve the performance and cost efficiency of Databricks workloads by eliminating manual tuning and resource management and continuously adjusting clusters in real time. It uses proprietary machine learning models and the only Spark-aware scaler that actively learns and profiles workloads to adjust cluster resources, select optimal configurations for every job run, and dynamically tune settings like hardware, Spark configs, and availability zones to maximize efficiency and cut waste. Zipher continuously monitors evolving workloads to adapt configurations, optimize scheduling, and allocate shared compute resources to meet SLAs, while providing detailed cost visibility that breaks down Databricks and cloud provider costs so teams can identify key cost drivers. It integrates seamlessly with major cloud service providers including AWS, Azure, and Google Cloud and works with common orchestration and IaC tools.