Best Auto Scaling Software in Brazil

Compute Engine (IaaS), a platform from Google that allows organizations to create and manage cloud-based virtual machines, is an infrastructure as a services (IaaS). Computing infrastructure in predefined sizes or custom machine shapes to accelerate cloud transformation. General purpose machines (E2, N1,N2,N2D) offer a good compromise between price and performance. Compute optimized machines (C2) offer high-end performance vCPUs for compute-intensive workloads. Memory optimized (M2) systems offer the highest amount of memory and are ideal for in-memory database applications. Accelerator optimized machines (A2) are based on A100 GPUs, and are designed for high-demanding applications. Integrate Compute services with other Google Cloud Services, such as AI/ML or data analytics. Reservations can help you ensure that your applications will have the capacity needed as they scale. You can save money by running Compute using the sustained-use discount, and you can even save more when you use the committed-use discount.

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.

Software-defined load balancers and container ingress services simplify application delivery for any application, in any datacenter and cloud. Simplify administration by implementing centralized policies that ensure operational consistency in hybrid clouds and on-premises datacenters, including VMware Cloud, AWS, Azure and Google Cloud. Self-service enables DevOps to free infrastructure teams from manual tasks. The toolkits for application delivery automation include Python SDKs, RESTful APIs and Terraform and Ansible integrations. With real-time monitoring of application performance, closed-loop analysis and deep machine-learning, you can gain unprecedented insights into network, end-users and security.

AWS Auto Scaling monitors and adjusts your applications to ensure predictable, consistent performance at the lowest cost. AWS Auto Scaling makes it easy to set up application scaling for multiple resources and multiple services in minutes. It offers a simple and powerful user interface that allows you to create scaling plans for resources such as Amazon EC2 instances, Spot Fleets and Amazon ECS tasks, Amazon DynamoDB indexes and tables, and Amazon Aurora Replicas. AWS Auto Scaling simplifies scaling with recommendations that optimize performance, cost, or balance between them. You can combine Amazon EC2 Auto Scaling with AWS Auto Scaling if you already use it to dynamically scale Amazon EC2 instances. AWS Auto Scaling ensures that your applications have the right resources at all times.

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.

Xosphere Instance Orchestrator performs automatic spot optimization using AWS Spot instances. This optimizes the cost of your infrastructure, while maintaining the same reliability as on-demand instance. Spot instances have been diversified by family, size and availability zone in order to minimize the impact of reclaiming Spot instances. Spot instances will not replace instances that use reservations. Respond automatically to Spot termination notifications. EBS volumes can configured to be attached new replacement instances, enabling stateful apps to work seamlessly.

Automated Optimization of Big Data Workloads. Control costs on any cloud. Pepperdata offers a 3X price performance improvement. We optimize your system resources and provide a correlated and detailed understanding of your infrastructure and applications, unlike traditional infrastructure monitoring vendors and APM vendors. Machine learning powers us to deliver the application SLAs required for business and provide complete visibility into your big data stack. Automated reduction of dynamic instances and packing more workloads into existing instances. Use application-specific recommendations to identify areas for manual tuning. Be alert for abnormal behavior in your application. Optimize the performance of your existing big data infrastructure without sacrificing observability. Our customers can run more applications and manage their costs with real-time optimization.

Amazon EC2 Auto Scaling allows you to maintain application availability by adding or removing EC2 instances automatically using scaling policies you define. Dynamic or predictive policies allow you to add or remove EC2 instances capacity in response to real-time or established demand patterns. The fleet management features in Amazon EC2 Auto Scaling maintain the health and availability your fleet. Automating DevOps is essential, and getting your fleets to automatically launch, provision software and self-heal is a major challenge. Amazon EC2 Auto Scaling offers essential features to automate each of these steps in the instance lifecycle. Use machine learning to predict the number of EC2 instance to be used to anticipate traffic changes.

Auto Scaling allows you to automatically adjust computing resources according to your user requests. Auto Scaling automatically adds new ECS instances when there is a greater demand or removes existing instances when there are fewer user requests.

UbiOps provides an AI infrastructure platform to help teams run AI & ML workloads quickly as reliable and secure Microservices without disrupting their existing workflows. UbiOps can be integrated seamlessly into your data-science workbench in minutes. This will save you time and money by avoiding the hassle of setting up expensive cloud infrastructure. You can use UbiOps as a data science team in a large company or a start-up to launch an AI product. UbiOps is a reliable backbone to any AI or ML services. Scale AI workloads dynamically based on usage, without paying for idle times. Instantly access powerful GPUs for model training and inference, enhanced by serverless, multicloud workload distribution.