Compute Engine enables you to create and run large-scale workloads on virtual machines hosted on Google Cloud. Get running quickly with pre-built and ready-to-go configurations or create machines of your own with the optimal amount of vCPU and memory required for your workload.
Multiple clouds. One cluster. All the benefits combined. Cast AI allows you to combine the benefits from multiple cloud providers in any way you want. Start with one or multiple clouds and Cast AI will create one cluster spanning them all. With auto-scaling, key metrics dashboard and management in vanilla Kubernetes out of the box.
Reduce K8s costs by up to 80%. Ensure workloads SLA. Free engineers from repeated manual configurations. ScaleOps is the industry-first Kubernetes Optimization Platform that automatically adjusts Compute Resources to changes in real-time, streamlining a new Kubernetes experience for engineering teams.
Xosphere's super power is reducing AWS EC2 expense by up to 80%. Xosphere is the world's only intelligent cloud orchestration company empowering enterprises to seamlessly move applications to the right place at the right time to reduce cloud expense and increase reliability. Xosphere's intelligent cloud software transforms unreliable Spot instances into robust resources that have the same reliability as On-Demand but at a fraction of the cost, yielding unparalleled savings. For enterprises that want to reduce cloud costs, Xosphere's optimization engine maximizes savings with the fastest speed of implementation in the industry. Xosphere Instance Orchestrator is a cloud-native, self-hosted subscription software application. It installs into your Amazon Web Services (AWS) account using either a CloudFormation stack or a Terraform module and runs using Lambda functions. Instance Orchestrator uses an opt-in design; it only executes on Auto-Scaling groups or individual instances that have explicitly been enabled via an AWS tag. Tags can be applied using any method or tool that is used within the organization to manage tags (for example, AWS Console, AWS CLI, AWS APIs, infrastructure-as-code platforms such as CloudFormation or Terraform, cloud management platforms, etc.). Once this enabling tag has been applied, Instance Orchestrator will automatically perform its management duties on an ongoing basis.
UbiOps by Dutch Analytics is an all-in-one software platform that enables you to very quickly turn your algorithms into scalable, robust and secure end-to-end applications. This without requiring knowledge to set up Cloud infrastructure, micro-services, automated scaling, or DevOps practices. Save months of work as UbiOps takes care of a smooth transition from where data science ends to where IT starts. Easily deployed across public/private Cloud or On-Premise. Centrally managed and fully secured with data and code encryption.
Avi Networks enables public-cloud-like simplicity and flexibility for application services such as load balancing, application analytics, and security in any data center or cloud.
Sedai incorporates key autonomous system characteristics in a cloud context. By leveraging a massive influx of data streams, Sedai builds a layer of intelligence via its core decision engine, which derives concepts from probability theory and applied machine learning techniques. Its self-learning and self-correcting model seamlessly manages cloud platforms with a focus on explainable decisions. Our products S.Watch Sedai connects with various monitoring tools, including Prometheus, Datadog, Cloudwatch, etc., and tracks four golden signals: Latency, Traffic, Errors, and Saturation. S-Watch distills noise to provide insights and recommendations to bring key KPIs such as MTTD, MTTF, MTBF, and MTTR to acceptable levels. S.Run Sedai distills data into an explainable and tunable knowledge base that powers its machine learning models. These models fuel Sedai’s core decision engine, which determines efficient and corrective workflows for all identified drifts to infer optimal strategies for detection and safe remediations. Its true closed-loop learning model enables self-configuration at optimal levels, ensuring the highest levels of availability. Armed with vast data, deep insights, and a rich knowledge base, platforms that are managed by Sedai are able to achieve a self-optimized state.
Meet your infrastructure Al toolkit: Thoras leverages bleeding-edge Al to predict GPU and CPU resource needs, prevent downtime, and optimize Kubernetes workloads-saving you money and keeping your systems running smoothly.
Auto Scaling is a service to automatically adjust computing resources based on your volume of user requests. When 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.
According to G2 data, both AWS Auto Scaling and Google Compute Engine hold an equal average rating of 4.5 out of 5, with AWS Auto Scaling having 257 reviews and Google Compute Engine 952 reviews. AWS Auto Scaling scores higher in Ease of Admin (8.9 vs 8.6) by 0.3 points and Better at Support (8.8 vs 8.4) by 0.4 points, while Google Compute Engine scores slightly lower in Easier to Admin and Support. Both products tie in Easier to Set Up and More Usable dimensions at 8.6 and 8.7 respectively. AWS Auto Scaling is praised for its automatic capacity adjustment based on workload demand, cost-effectiveness, and seamless integration with AWS services, though it has a noted complexity in configuration and slower scaling response times. Google Compute Engine is recognized for its ease of use, flexible VM configurations, strong scalability, and integration within the Google Cloud ecosystem, but users report a steeper learning curve and complex pricing structure. Both platforms offer robust scalability and performance, but AWS Auto Scaling emphasizes automated scaling policies and cost optimization, whereas Google Compute Engine provides more granular VM customization and live migration capabilities. Overall, AWS Auto Scaling excels in administration and support, while Google Compute Engine is favored for flexibility and integration with Google Cloud services.
The best alternatives to AWS Auto Scaling include Google Compute Engine (4.5/5 stars, 952 reviews), Cast AI (4.6/5 stars, 192 reviews), and ScaleOps (4.6/5 stars, 96 reviews). These platforms offer strong scalability, cost optimization, and user-friendly interfaces with extensive automation capabilities.
AWS Auto Scaling lacks some advanced automation features such as real-time workload right-sizing, intelligent spot instance management with automatic fallback, and seamless multi-cloud cluster management that competitors provide.
Reviewers highly recommend Cast AI for its automated Kubernetes cost optimization, real-time workload right-sizing, and intelligent spot instance management that reduces cloud spend by over 50%. ScaleOps is praised for its ease of setup, dynamic resource optimization, and excellent customer support, delivering significant cost savings and operational efficiency. Google Compute Engine is favored for its flexibility, customizable machine types, and seamless integration with Google Cloud services, providing reliable performance and scalability.
Users choose Google Compute Engine over AWS Auto Scaling primarily for its ease of use and flexibility. With 65 mentions highlighting ease of use and 60 mentions praising scalability, Google Compute Engine offers straightforward VM setup and management, which appeals especially to users seeking customizable machine types and seamless integration with other Google Cloud services like BigQuery and Kubernetes. Its live migration feature ensures minimal downtime during maintenance, enhancing reliability. Additionally, Google Compute Engine’s per-second billing and sustained-use discounts contribute to cost-effectiveness for users who understand its pricing model. Users also appreciate the clean and intuitive user interface and the ability to tailor VM configurations precisely to workload needs. Despite a steeper learning curve and complex pricing, the platform’s integration within the Google ecosystem and its flexibility make it a preferred choice for organizations prioritizing control and scalability. These factors, combined with 42 mentions of cost-effectiveness and 47 mentions of Google Cloud Platform integration, explain why users opt for Google Compute Engine over AWS Auto Scaling.