Best Auto Scaling Software in Europe

The auto scaling capability of Google Compute Engine dynamically modifies the number of virtual machine instances based on varying traffic or workload requirements. This functionality guarantees that applications operate efficiently without the need for manual adjustments and minimizes costs by reducing resources when demand decreases. Users have the flexibility to set scaling guidelines according to particular metrics, like CPU usage or request frequency, allowing for tailored resource distribution. New users are also offered $300 in free credits, giving them the opportunity to experiment with and optimize auto scaling for their specific needs.

RunPod provides a cloud infrastructure that enables seamless deployment and scaling of AI workloads with GPU-powered pods. By offering access to a wide array of NVIDIA GPUs, such as the A100 and H100, RunPod supports training and deploying machine learning models with minimal latency and high performance. The platform emphasizes ease of use, allowing users to spin up pods in seconds and scale them dynamically to meet demand. With features like autoscaling, real-time analytics, and serverless scaling, RunPod is an ideal solution for startups, academic institutions, and enterprises seeking a flexible, powerful, and affordable platform for AI development and inference.

AWS Auto Scaling continuously observes your applications and automatically modifies capacity to ensure consistent and reliable performance while minimizing costs. This service simplifies the process of configuring application scaling for various resources across multiple services in just a few minutes. It features an intuitive and robust user interface that enables the creation of scaling plans for a range of resources, including Amazon EC2 instances, Spot Fleets, Amazon ECS tasks, Amazon DynamoDB tables and indexes, as well as Amazon Aurora Replicas. By providing actionable recommendations, AWS Auto Scaling helps you enhance performance, reduce expenses, or strike a balance between the two. If you are utilizing Amazon EC2 Auto Scaling for dynamic scaling of your EC2 instances, you can now seamlessly integrate it with AWS Auto Scaling to extend your scaling capabilities to additional AWS services. This ensures that your applications are consistently equipped with the appropriate resources precisely when they are needed, leading to improved overall efficiency. Ultimately, AWS Auto Scaling empowers businesses to optimize their resource management in a highly efficient manner.

Streamline the process of application delivery by utilizing software-defined load balancers, web application firewalls, and container ingress services that can be deployed across any application in various data centers and cloud environments. Enhance management efficiency through unified policies and consistent operations across on-premises data centers as well as hybrid and public cloud platforms, which include VMware Cloud (such as VMC on AWS, OCVS, AVS, and GCVE), AWS, Azure, Google Cloud, and Oracle Cloud. Empower infrastructure teams by alleviating them from manual tasks and provide DevOps teams with self-service capabilities. The automation toolkits for application delivery encompass a variety of resources, including Python SDK, RESTful APIs, and integrations with Ansible and Terraform. Additionally, achieve unparalleled insights into network performance, user experience, and security through real-time application performance monitoring, closed-loop analytics, and advanced machine learning techniques that continuously enhance system efficiency. This holistic approach not only improves performance but also fosters a culture of agility and responsiveness within the organization.

StarTree Cloud is a fully-managed real-time analytics platform designed for OLAP at massive speed and scale for user-facing applications. Powered by Apache Pinot, StarTree Cloud provides enterprise-grade reliability and advanced capabilities such as tiered storage, scalable upserts, plus additional indexes and connectors. It integrates seamlessly with transactional databases and event streaming platforms, ingesting data at millions of events per second and indexing it for lightning-fast query responses. StarTree Cloud is available on your favorite public cloud or for private SaaS deployment. StarTree Cloud includes StarTree Data Manager, which allows you to ingest data from both real-time sources such as Amazon Kinesis, Apache Kafka, Apache Pulsar, or Redpanda, as well as batch data sources such as data warehouses like Snowflake, Delta Lake or Google BigQuery, or object stores like Amazon S3, Apache Flink, Apache Hadoop, or Apache Spark. StarTree ThirdEye is an add-on anomaly detection system running on top of StarTree Cloud that observes your business-critical metrics, alerting you and allowing you to perform root-cause analysis — all in real-time.

StormForge drives immediate benefits for organization through its continuous Kubernetes workload rightsizing capabilities — leading to cost savings of 40-60% along with performance and reliability improvements across the entire estate. As a vertical rightsizing solution, Optimize Live is autonomous, tunable, and works seamlessly with the HPA at enterprise scale. Optimize Live addresses both over- and under-provisioned workloads by analyzing usage data with advanced ML algorithms 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.

CAST AI significantly reduces your compute costs with automated cost management and optimization. Within minutes, you can quickly optimize your GKE clusters thanks to real-time autoscaling up and down, rightsizing, spot instance automation, selection of most cost-efficient instances, and more. What you see is what you get – you can find out what your savings will look like with the Savings Report available in the free plan with K8s cost monitoring. Enabling the automation will deliver reported savings to you within minutes and keep the cluster optimized. The platform understands what your application needs at any given time and uses that to implement real-time changes for best cost and performance. It isn’t just a recommendation engine. CAST AI uses automation to reduce the operational costs of cloud services and enables you to focus on building great products instead of worrying about the cloud infrastructure. Companies that use CAST AI benefit from higher profit margins without any additional work thanks to the efficient use of engineering resources and greater control of cloud environments. As a direct result of optimization, CAST AI clients save an average of 63% on their Kubernetes cloud bills.

Pepperdata autonomous, application-level cost optimization delivers 30-47% greater cost savings for data-intensive workloads such as Apache Spark on Amazon EMR and Amazon EKS with no application changes. Using patented algorithms, Pepperdata Capacity Optimizer autonomously optimizes CPU and memory in real time with no application code changes. Pepperdata automatically analyzes resource usage in real time, identifying where more work can be done, enabling the scheduler to add tasks to nodes with available resources and spin up new nodes only when existing nodes are fully utilized. The result: CPU and memory are autonomously and continuously optimized, without delay and without the need for recommendations to be applied, and the need for ongoing manual tuning is safely eliminated. Pepperdata pays for itself, immediately decreasing instance hours/waste, increasing Spark utilization, and freeing developers from manual tuning to focus on innovation.

The Xosphere Instance Orchestrator enhances cost efficiency through automated spot optimization by utilizing AWS Spot instances, ensuring that the infrastructure remains as reliable as on-demand instances. By diversifying Spot instances across different families, sizes, and availability zones, it minimizes potential disruptions caused by the reclamation of these instances. Instances that are backed by reservations will not be substituted with Spot instances, preserving their intended use. Additionally, the system is designed to automatically respond to Spot termination notifications, allowing for expedited replacement of on-demand instances. Furthermore, EBS volumes can be configured to attach seamlessly to newly provisioned replacement instances, facilitating uninterrupted operation of stateful applications. This orchestration ensures a robust infrastructure while optimizing costs effectively.

Zerops.io serves as a cloud solution tailored for developers focused on creating contemporary applications, providing features such as automatic vertical and horizontal autoscaling, precise resource management, and freedom from vendor lock-in. The platform enhances infrastructure management through capabilities like automated backups, failover options, CI/CD integration, and comprehensive observability. Zerops.io adapts effortlessly to the evolving requirements of your project, guaranteeing maximum performance and cost-effectiveness throughout the development lifecycle, while also accommodating microservices and intricate architectures. It is particularly beneficial for developers seeking a combination of flexibility, scalability, and robust automation without the hassle of complex setups. This ensures a streamlined experience that empowers developers to focus on innovation rather than infrastructure.

Zipher is an innovative optimization platform that autonomously enhances the performance and cost-effectiveness of workloads on Databricks by removing the need for manual tuning and resource management, all while making real-time adjustments to clusters. Utilizing advanced proprietary machine learning algorithms, Zipher features a unique Spark-aware scaler that actively learns from and profiles workloads to determine the best resource allocations, optimize configurations for each job execution, and fine-tune various settings such as hardware, Spark configurations, and availability zones, thereby maximizing operational efficiency and minimizing waste. The platform continuously tracks changing workloads to modify configurations, refine scheduling, and distribute shared compute resources effectively to adhere to service level agreements (SLAs), while also offering comprehensive cost insights that dissect expenses related to Databricks and cloud services, enabling teams to pinpoint significant cost influencers. Furthermore, Zipher ensures smooth integration with major cloud providers like AWS, Azure, and Google Cloud, and is compatible with popular orchestration and infrastructure-as-code (IaC) tools, making it a versatile solution for various cloud environments. Its ability to adaptively respond to workload changes sets Zipher apart as a crucial tool for organizations striving to optimize their cloud operations.

Convox is an advanced platform-as-a-service (PaaS) that streamlines the deployment, scaling, and management of cloud applications by minimizing infrastructure complexity, allowing teams to concentrate on coding. It operates directly in your cloud account and connects with leading cloud service providers like AWS, Google Cloud, Azure, and DigitalOcean, ensuring you maintain full control and cost-effectiveness while eliminating unnecessary hosting charges. With features such as continuous integration and delivery pipelines, automatic scaling policies, and zero-downtime deployments, Convox provides tools for configuring environments, implementing role-based access controls, and establishing secure workflows. Its user-friendly command-line interface, adaptable deployment settings, and compatibility with popular tools like GitHub, GitLab, Slack, and various monitoring services enhance workflow efficiency and increase productivity. Additionally, Convox includes real-time monitoring capabilities, comprehensive logging, and one-click rollback options, ensuring reliable performance and facilitating easier debugging. Overall, the platform empowers development teams to innovate more rapidly while maintaining operational stability.

Auto Scaling is a service designed to dynamically adjust computing resources in response to fluctuations in user demand. When there is an uptick in requests, it seamlessly adds ECS instances to accommodate the increased load, while conversely, it reduces the number of instances during quieter times to optimize resource allocation. This service not only adjusts resources automatically based on predefined scaling policies but also allows for manual intervention through scale-in and scale-out options, giving you the flexibility to manage resources as needed. During high-demand periods, it efficiently expands the available computing resources, ensuring optimal performance, and when demand wanes, Auto Scaling efficiently retracts ECS resources, helping to minimize operational costs. Additionally, this adaptability ensures that your system remains responsive and cost-effective throughout varying usage patterns.

Amazon EC2 Auto Scaling ensures that your applications remain available by allowing for the automatic addition or removal of EC2 instances based on scaling policies that you set. By utilizing dynamic or predictive scaling policies, you can adjust the capacity of EC2 instances to meet both historical and real-time demand fluctuations. The fleet management capabilities within Amazon EC2 Auto Scaling are designed to sustain the health and availability of your instance fleet effectively. In the realm of efficient DevOps, automation plays a crucial role, and one of the primary challenges lies in ensuring that your fleets of Amazon EC2 instances can automatically launch, provision software, and recover from failures. Amazon EC2 Auto Scaling offers vital functionalities for each phase of instance lifecycle automation. Furthermore, employing machine learning algorithms can aid in forecasting and optimizing the number of EC2 instances needed to proactively manage anticipated changes in traffic patterns. By leveraging these advanced features, organizations can enhance their operational efficiency and responsiveness to varying workload demands.

UbiOps serves as a robust AI infrastructure platform designed to enable teams to efficiently execute their AI and ML workloads as dependable and secure microservices, all while maintaining their current workflows. In just a few minutes, you can integrate UbiOps effortlessly into your data science environment, thereby eliminating the tedious task of establishing and overseeing costly cloud infrastructure. Whether you're a start-up aiming to develop an AI product or part of a larger organization's data science unit, UbiOps provides a solid foundation for any AI or ML service you wish to implement. The platform allows you to scale your AI workloads in response to usage patterns, ensuring you only pay for what you use without incurring costs for time spent idle. Additionally, it accelerates both model training and inference by offering immediate access to powerful GPUs, complemented by serverless, multi-cloud workload distribution that enhances operational efficiency. By choosing UbiOps, teams can focus on innovation rather than infrastructure management, paving the way for groundbreaking AI solutions.

No expertise required! Our Kubernetes Management platform allows you to create clusters in minutes. Every feature of our platform has been designed to automate DevOps. We ensure that every component is tightly interconnected by building everything from scratch. This allows us to achieve the best performance and reduce complexity. Syself Autopilot supports declarative configurations. This is an approach where configuration files are used to define the desired states of your infrastructure and application. Instead of issuing commands that change the current state, the system will automatically make the necessary adjustments in order to achieve the desired state.

Lucidity serves as a versatile multi-cloud storage management solution, adept at dynamically adjusting block storage across major platforms like AWS, Azure, and Google Cloud while ensuring zero downtime, which can lead to savings of up to 70% on storage expenses. This innovative platform automates the process of resizing storage volumes in response to real-time data demands, maintaining optimal disk usage levels between 75-80%. Additionally, Lucidity is designed to function independently of specific applications, integrating effortlessly into existing systems without necessitating code alterations or manual provisioning. The AutoScaler feature of Lucidity, accessible via the AWS Marketplace, provides businesses with an automated method to manage live EBS volumes, allowing for expansion or reduction based on workload requirements, all without any interruptions. By enhancing operational efficiency, Lucidity empowers IT and DevOps teams to recover countless hours of work, which can then be redirected towards more impactful projects that foster innovation and improve overall effectiveness. This capability ultimately positions enterprises to better adapt to changing storage needs and optimize resource utilization.

NVIDIA DGX Cloud Serverless Inference provides a cutting-edge, serverless AI inference framework designed to expedite AI advancements through automatic scaling, efficient GPU resource management, multi-cloud adaptability, and effortless scalability. This solution enables users to reduce instances to zero during idle times, thereby optimizing resource use and lowering expenses. Importantly, there are no additional charges incurred for cold-boot startup durations, as the system is engineered to keep these times to a minimum. The service is driven by NVIDIA Cloud Functions (NVCF), which includes extensive observability capabilities, allowing users to integrate their choice of monitoring tools, such as Splunk, for detailed visibility into their AI operations. Furthermore, NVCF supports versatile deployment methods for NIM microservices, granting the ability to utilize custom containers, models, and Helm charts, thus catering to diverse deployment preferences and enhancing user flexibility. This combination of features positions NVIDIA DGX Cloud Serverless Inference as a powerful tool for organizations seeking to optimize their AI inference processes.