Alternatives to Amazon EC2 Auto Scaling
Compare Amazon EC2 Auto Scaling alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Amazon EC2 Auto Scaling in 2026. Compare features, ratings, user reviews, pricing, and more from Amazon EC2 Auto Scaling competitors and alternatives in order to make an informed decision for your business.
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Amazon Elastic Container Service (Amazon ECS) is a fully managed container orchestration service. Customers such as Duolingo, Samsung, GE, and Cook Pad use ECS to run their most sensitive and mission-critical applications because of its security, reliability, and scalability. ECS is a great choice to run containers for several reasons. First, you can choose to run your ECS clusters using AWS Fargate, which is serverless compute for containers. Fargate removes the need to provision and manage servers, lets you specify and pay for resources per application, and improves security through application isolation by design. Second, ECS is used extensively within Amazon to power services such as Amazon SageMaker, AWS Batch, Amazon Lex, and Amazon.com’s recommendation engine, ensuring ECS is tested extensively for security, reliability, and availability.
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AWS Auto Scaling
Amazon
AWS Auto Scaling monitors your applications and automatically adjusts capacity to maintain steady, predictable performance at the lowest possible cost. Using AWS Auto Scaling, it’s easy to setup application scaling for multiple resources across multiple services in minutes. The service provides a simple, powerful user interface that lets you build scaling plans for resources including Amazon EC2 instances and Spot Fleets, Amazon ECS tasks, Amazon DynamoDB tables and indexes, and Amazon Aurora Replicas. AWS Auto Scaling makes scaling simple with recommendations that allow you to optimize performance, costs, or balance between them. If you’re already using Amazon EC2 Auto Scaling to dynamically scale your Amazon EC2 instances, you can now combine it with AWS Auto Scaling to scale additional resources for other AWS services. With AWS Auto Scaling, your applications always have the right resources at the right time. -
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Amazon EKS
Amazon
Amazon Elastic Kubernetes Service (Amazon EKS) is a fully managed Kubernetes service. Customers such as Intel, Snap, Intuit, GoDaddy, and Autodesk trust EKS to run their most sensitive and mission-critical applications because of its security, reliability, and scalability. EKS is the best place to run Kubernetes for several reasons. First, you can choose to run your EKS clusters using AWS Fargate, which is serverless compute for containers. Fargate removes the need to provision and manage servers, lets you specify and pay for resources per application, and improves security through application isolation by design. Second, EKS is deeply integrated with services such as Amazon CloudWatch, Auto Scaling Groups, AWS Identity and Access Management (IAM), and Amazon Virtual Private Cloud (VPC), providing you a seamless experience to monitor, scale, and load-balance your applications. -
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Amazon RDS
Amazon
Amazon Relational Database Service (Amazon RDS) makes it easy to set up, operate, and scale a relational database in the cloud. It provides cost-efficient and resizable capacity while automating time-consuming administration tasks such as hardware provisioning, database setup, patching and backups. It frees you to focus on your applications so you can give them the fast performance, high availability, security and compatibility they need. Amazon RDS is available on several database instance types - optimized for memory, performance or I/O - and provides you with six familiar database engines to choose from, including Amazon Aurora, PostgreSQL, MySQL, MariaDB, Oracle Database, and SQL Server. You can use the AWS Database Migration Service to easily migrate or replicate your existing databases to Amazon RDS.Starting Price: $0.01 per month -
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AWS Fargate
Amazon
AWS Fargate is a serverless compute engine for containers that works with both Amazon Elastic Container Service (ECS) and Amazon Elastic Kubernetes Service (EKS). Fargate makes it easy for you to focus on building your applications. Fargate removes the need to provision and manage servers, lets you specify and pay for resources per application, and improves security through application isolation by design. Fargate allocates the right amount of compute, eliminating the need to choose instances and scale cluster capacity. You only pay for the resources required to run your containers, so there is no over-provisioning and paying for additional servers. Fargate runs each task or pod in its own kernel providing the tasks and pods their own isolated compute environment. This enables your application to have workload isolation and improved security by design. -
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Amazon GameLift
Amazon
Amazon GameLift is a dedicated game server hosting solution that deploys, operates, and scales cloud servers for multiplayer games. Whether you’re looking for a fully managed solution, or just the feature you need, GameLift leverages the power of AWS to deliver the best latency possible, low player wait times, and maximum cost savings. Amazon GameLift leverages the AWS global infrastructure for managing game servers. Match players into game sessions and autoscale that start one, hundreds, or even thousands of instances simultaneously, without thinking about scaling with fluctuating player demand. Game services provide basic multiplayer game support, like matchmaking, session directory, player data, and player analytics. With AWS, you can use services like AWS Lambda that provide serverless, scalable, and flexible computing, or features in services like Amazon GameLift FlexMatch, for matchmaking. -
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Xosphere
Xosphere
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. -
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Alibaba Auto Scaling
Alibaba Cloud
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 -
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Syself
Syself
Managing Kubernetes shouldn't be a headache. With Syself Autopilot, both beginners and experts can deploy and maintain enterprise-grade clusters with ease. Say goodbye to downtime and complexity—our platform ensures automated upgrades, self-healing capabilities, and GitOps compatibility. Whether you're running on bare metal or cloud infrastructure, Syself Autopilot is designed to handle your needs, all while maintaining GDPR-compliant data protection. Syself Autopilot integrates with leading DevOps and infrastructure solutions, allowing you to build and scale applications effortlessly. Our platform supports: - Argo CD, Flux (GitOps & CI/CD) - MariaDB, PostgreSQL, MySQL, MongoDB, ClickHouse (Databases) - Grafana, Istio, Redis, NATS (Monitoring & Service Mesh) Need additional solutions? Our team helps you deploy, configure, and optimize your infrastructure for peak performance.Starting Price: €299/month -
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Amazon Aurora
Amazon
Amazon Aurora is a MySQL and PostgreSQL-compatible relational database built for the cloud, that combines the performance and availability of traditional enterprise databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora is up to five times faster than standard MySQL databases and three times faster than standard PostgreSQL databases. It provides the security, availability, and reliability of commercial databases at 1/10th the cost. Amazon Aurora is fully managed by Amazon Relational Database Service (RDS), which automates time-consuming administration tasks like hardware provisioning, database setup, patching, and backups. Amazon Aurora features a distributed, fault-tolerant, self-healing storage system that auto-scales up to 64TB per database instance. It delivers high performance and availability with up to 15 low-latency read replicas, point-in-time recovery, continuous backup to Amazon S3, and replication across three Availability Zones.Starting Price: $0.02 per month -
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EC2 Spot
Amazon
Amazon EC2 Spot Instances let you take advantage of unused EC2 capacity in the AWS cloud. Spot Instances are available at up to a 90% discount compared to On-Demand prices. You can use Spot Instances for various stateless, fault-tolerant, or flexible applications such as big data, containerized workloads, CI/CD, web servers, high-performance computing (HPC), and test & development workloads. Because Spot Instances are tightly integrated with AWS services such as Auto Scaling, EMR, ECS, CloudFormation, Data Pipeline and AWS Batch, you can choose how to launch and maintain your applications running on Spot Instances. Moreover, you can easily combine Spot Instances with On-Demand, RIs and Savings Plans Instances to further optimize workload cost with performance. Due to the operating scale of AWS, Spot Instances can offer the scale and cost savings to run hyper-scale workloads.Starting Price: $0.01 per user, one-time payment, -
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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.
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Enterpristore
Logistica Solutions
Enterpristore for Infor ERP is fully integrated with Amazon Web Services offering an ecommerce cloud computing solution to small and large businesses that want a flexible, secured, highly scalable, and low-cost solution for online sales and retailing. Cloud computing is the on-demand delivery of compute power, database storage, applications, and other IT resources through a cloud services platform via the internet with pay-as-you-go pricing. Experience the power and reliability of AWS. Deploy in seconds and manage from the intuitive Lightsail setup for smaller requirements. Amazon EC2 Auto Scaling ensures that your application always has the right amount of compute capacity. Amazon EC2 Auto Scaling adds new instances only when necessary and terminates them when no longer needed. -
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Amazon EC2 Capacity Blocks for ML enable you to reserve accelerated compute instances in Amazon EC2 UltraClusters for your machine learning workloads. This service supports Amazon EC2 P5en, P5e, P5, and P4d instances, powered by NVIDIA H200, H100, and A100 Tensor Core GPUs, respectively, as well as Trn2 and Trn1 instances powered by AWS Trainium. You can reserve these instances for up to six months in cluster sizes ranging from one to 64 instances (512 GPUs or 1,024 Trainium chips), providing flexibility for various ML workloads. Reservations can be made up to eight weeks in advance. By colocating in Amazon EC2 UltraClusters, Capacity Blocks offer low-latency, high-throughput network connectivity, facilitating efficient distributed training. This setup ensures predictable access to high-performance computing resources, allowing you to plan ML development confidently, run experiments, build prototypes, and accommodate future surges in demand for ML applications.
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Tencent Cloud Load Balancer
Tencent
One CLB cluster consists of 4 physical servers, offering an availability of up to 99.95%. In the extreme case where only one CLB instance is available, it can still support over 30 million concurrent connections. The cluster system will quickly remove faulty instances and keep healthy instances to ensure that the backend server continues to operate properly. The CLB cluster scales the service capabilities of the application system elastically according to the business load, and automatically creates and releases CVM instances through the dynamic scaling group of Auto Scaling. These features, in conjunction with a dynamic monitoring system and a billing system that is accurate to the second, eliminate the need to manually intervene or estimate resource requirements, helping you efficiently allocate computing resources and prevent resource waste. -
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UbiOps
UbiOps
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. -
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Google Cloud Build
Google
Fully serverless platform. Cloud Build scales up and scales down in response to load with no need to pre-provision servers or pay in advance for additional capacity. Pay only for what you use. With custom build steps and pre-created extensions to third party apps, enterprises can easily tie their legacy or home-grown tools as a part of their build process. Guard against security threats in your software supply chain with vulnerability scanning. Automatically block the deployment of vulnerable images based on policies set by DevSecOps. Cloud Build scales up and down with no infrastructure to set up, upgrade, or scale. Run builds in a fully managed environment across Google Cloud, on-premises, other public clouds, or your own private network. Create portable images directly from the source without a Dockerfile using buildpacks. Support for Tekton pipelines running on Kubernetes gives you scale and self-healing benefits of Kubernetes, without lock-in. -
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Amazon EC2 P5 Instances
Amazon
Amazon Elastic Compute Cloud (Amazon EC2) P5 instances, powered by NVIDIA H100 Tensor Core GPUs, and P5e and P5en instances powered by NVIDIA H200 Tensor Core GPUs deliver the highest performance in Amazon EC2 for deep learning and high-performance computing applications. They help you accelerate your time to solution by up to 4x compared to previous-generation GPU-based EC2 instances, and reduce the cost to train ML models by up to 40%. These instances help you iterate on your solutions at a faster pace and get to market more quickly. You can use P5, P5e, and P5en instances for training and deploying increasingly complex large language models and diffusion models powering the most demanding generative artificial intelligence applications. These applications include question-answering, code generation, video and image generation, and speech recognition. You can also use these instances to deploy demanding HPC applications at scale for pharmaceutical discovery. -
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Amazon EC2 G4 Instances
Amazon
Amazon EC2 G4 instances are optimized for machine learning inference and graphics-intensive applications. It offers a choice between NVIDIA T4 GPUs (G4dn) and AMD Radeon Pro V520 GPUs (G4ad). G4dn instances combine NVIDIA T4 GPUs with custom Intel Cascade Lake CPUs, providing a balance of compute, memory, and networking resources. These instances are ideal for deploying machine learning models, video transcoding, game streaming, and graphics rendering. G4ad instances, featuring AMD Radeon Pro V520 GPUs and 2nd-generation AMD EPYC processors, deliver cost-effective solutions for graphics workloads. Both G4dn and G4ad instances support Amazon Elastic Inference, allowing users to attach low-cost GPU-powered inference acceleration to Amazon EC2 and reduce deep learning inference costs. They are available in various sizes to accommodate different performance needs and are integrated with AWS services such as Amazon SageMaker, Amazon ECS, and Amazon EKS. -
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Amazon EC2 Inf1 Instances
Amazon
Amazon EC2 Inf1 instances are purpose-built to deliver high-performance and cost-effective machine learning inference. They provide up to 2.3 times higher throughput and up to 70% lower cost per inference compared to other Amazon EC2 instances. Powered by up to 16 AWS Inferentia chips, ML inference accelerators designed by AWS, Inf1 instances also feature 2nd generation Intel Xeon Scalable processors and offer up to 100 Gbps networking bandwidth to support large-scale ML applications. These instances are ideal for deploying applications such as search engines, recommendation systems, computer vision, speech recognition, natural language processing, personalization, and fraud detection. Developers can deploy their ML models on Inf1 instances using the AWS Neuron SDK, which integrates with popular ML frameworks like TensorFlow, PyTorch, and Apache MXNet, allowing for seamless migration with minimal code changes.Starting Price: $0.228 per hour -
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Elastigroup
Spot by NetApp
Provision, manage and scale compute infrastructure on any cloud. Save up to 80% on your costs while ensuring SLA and high-availability. Elastigroup is a cluster software, designed to optimize performance and costs. It enables companies of all sizes and verticals to reliably leverage Cloud Excess Capacity to optimize and accelerate workloads and save up to 90% on infrastructure compute costs. Elastigroup makes use of proprietary price prediction technology to deploy reliably onto Spot Instances. By predicting interruptions and fluctuations Elastigroup is able to offensively rebalance clusters to prevent interruption. Elastigroup reliably leverages excess capacity across all major cloud providers such as EC2 Spot Instances (AWS), Low-priority VMs (Microsoft Azure) and Preemptible VMs (Google Cloud), while removing risk and complexity, providing simple orchestration and management at scale. -
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Amazon SageMaker Model Training reduces the time and cost to train and tune machine learning (ML) models at scale without the need to manage infrastructure. You can take advantage of the highest-performing ML compute infrastructure currently available, and SageMaker can automatically scale infrastructure up or down, from one to thousands of GPUs. Since you pay only for what you use, you can manage your training costs more effectively. To train deep learning models faster, SageMaker distributed training libraries can automatically split large models and training datasets across AWS GPU instances, or you can use third-party libraries, such as DeepSpeed, Horovod, or Megatron. Efficiently manage system resources with a wide choice of GPUs and CPUs including P4d.24xl instances, which are the fastest training instances currently available in the cloud. Specify the location of data, indicate the type of SageMaker instances, and get started with a single click.
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Amazon EC2 Trn2 Instances
Amazon
Amazon EC2 Trn2 instances, powered by AWS Trainium2 chips, are purpose-built for high-performance deep learning training of generative AI models, including large language models and diffusion models. They offer up to 50% cost-to-train savings over comparable Amazon EC2 instances. Trn2 instances support up to 16 Trainium2 accelerators, providing up to 3 petaflops of FP16/BF16 compute power and 512 GB of high-bandwidth memory. To facilitate efficient data and model parallelism, Trn2 instances feature NeuronLink, a high-speed, nonblocking interconnect, and support up to 1600 Gbps of second-generation Elastic Fabric Adapter (EFAv2) network bandwidth. They are deployed in EC2 UltraClusters, enabling scaling up to 30,000 Trainium2 chips interconnected with a nonblocking petabit-scale network, delivering 6 exaflops of compute performance. The AWS Neuron SDK integrates natively with popular machine learning frameworks like PyTorch and TensorFlow. -
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Amazon EC2 P4 Instances
Amazon
Amazon EC2 P4d instances deliver high performance for machine learning training and high-performance computing applications in the cloud. Powered by NVIDIA A100 Tensor Core GPUs, they offer industry-leading throughput and low-latency networking, supporting 400 Gbps instance networking. P4d instances provide up to 60% lower cost to train ML models, with an average of 2.5x better performance for deep learning models compared to previous-generation P3 and P3dn instances. Deployed in hyperscale clusters called Amazon EC2 UltraClusters, P4d instances combine high-performance computing, networking, and storage, enabling users to scale from a few to thousands of NVIDIA A100 GPUs based on project needs. Researchers, data scientists, and developers can utilize P4d instances to train ML models for use cases such as natural language processing, object detection and classification, and recommendation engines, as well as to run HPC applications like pharmaceutical discovery and more.Starting Price: $11.57 per hour -
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Amazon Elastic Inference
Amazon
Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Sagemaker instances or Amazon ECS tasks, to reduce the cost of running deep learning inference by up to 75%. Amazon Elastic Inference supports TensorFlow, Apache MXNet, PyTorch and ONNX models. Inference is the process of making predictions using a trained model. In deep learning applications, inference accounts for up to 90% of total operational costs for two reasons. Firstly, standalone GPU instances are typically designed for model training - not for inference. While training jobs batch process hundreds of data samples in parallel, inference jobs usually process a single input in real time, and thus consume a small amount of GPU compute. This makes standalone GPU inference cost-inefficient. On the other hand, standalone CPU instances are not specialized for matrix operations, and thus are often too slow for deep learning inference. -
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Amazon Inspector
Amazon
Amazon Inspector is an automated security assessment service that helps improve the security and compliance of applications deployed on AWS. Amazon Inspector automatically assesses applications for exposure, vulnerabilities, and deviations from best practices. After performing an assessment, Amazon Inspector produces a detailed list of security findings prioritized by level of severity. These findings can be reviewed directly or as part of detailed assessment reports which are available via the Amazon Inspector console or API. Amazon Inspector security assessments help you check for unintended network accessibility of your Amazon EC2 instances and for vulnerabilities on those EC2 instances. Amazon Inspector assessments are offered to you as pre-defined rules packages mapped to common security best practices and vulnerability definitions. Accelerate MTTR by using over 50 sources for vulnerability intelligence to help identify zero-day vulnerabilities quickly. -
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AWS Elastic Fabric Adapter (EFA)
United States
Elastic Fabric Adapter (EFA) is a network interface for Amazon EC2 instances that enables customers to run applications requiring high levels of inter-node communications at scale on AWS. Its custom-built operating system (OS) bypass hardware interface enhances the performance of inter-instance communications, which is critical to scaling these applications. With EFA, High-Performance Computing (HPC) applications using the Message Passing Interface (MPI) and Machine Learning (ML) applications using NVIDIA Collective Communications Library (NCCL) can scale to thousands of CPUs or GPUs. As a result, you get the application performance of on-premises HPC clusters with the on-demand elasticity and flexibility of the AWS cloud. EFA is available as an optional EC2 networking feature that you can enable on any supported EC2 instance at no additional cost. Plus, it works with the most commonly used interfaces, APIs, and libraries for inter-node communications. -
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AWS Elastic Load Balancing
Amazon
Elastic Load Balancing automatically routes incoming application traffic across multiple destinations, such as Amazon EC2 instances, containers, IP addresses, Lambda functions, and virtual appliances. You can control the variable load of your application traffic in a single zone or in multiple Availability Zones. Elastic Load Balancing offers four types of load balancers that have the necessary level of high availability, automatic scalability, and security to make your applications fault tolerant. Elastic Load Balancing is part of the AWS network, with native knowledge of fault limits like AZ to keep your applications available in one region, without requiring Global Server Load Balancing (GSLB). ELB is also a fully managed service, which means you can focus on delivering applications and not installing fleets of load balancers. Capacity is automatically added and removed based on the utilization of the underlying application servers.Starting Price: $0.027 USD per Load Balancer per hour -
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AWS Backup
Amazon
AWS Backup is a fully managed backup service that makes it easy to centralize and automate the backup of data across AWS services. Using AWS Backup, you can centrally configure backup policies and monitor backup activity for AWS resources, such as Amazon EBS volumes, Amazon EC2 instances, Amazon RDS databases, Amazon DynamoDB tables, Amazon EFS file systems, and AWS Storage Gateway volumes. AWS Backup automates and consolidates backup tasks previously performed service-by-service, removing the need to create custom scripts and manual processes. With just a few clicks in the AWS Backup console, you can create backup policies that automate backup schedules and retention management. AWS Backup provides a fully managed, policy-based backup solution, simplifying your backup management, enabling you to meet your business and regulatory backup compliance requirements. -
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Akamai Cloud
Akamai
Akamai Cloud (formerly Linode) is the world’s most distributed cloud computing platform, designed to help businesses deploy low-latency, high-performance applications anywhere. It delivers GPU acceleration, managed Kubernetes, object storage, and compute instances optimized for AI, media, and SaaS workloads. With flat, predictable pricing and low egress fees, Akamai Cloud offers a transparent and cost-effective alternative to traditional hyperscalers. Its global infrastructure ensures faster response times, improved reliability, and data sovereignty across key regions. Developers can scale securely using Akamai’s firewall, database, and networking solutions, all managed through an intuitive interface or API. Backed by enterprise-grade support and compliance, Akamai Cloud empowers organizations to innovate confidently at the edge. -
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AWS Neuron
Amazon Web Services
It supports high-performance training on AWS Trainium-based Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances. For model deployment, it supports high-performance and low-latency inference on AWS Inferentia-based Amazon EC2 Inf1 instances and AWS Inferentia2-based Amazon EC2 Inf2 instances. With Neuron, you can use popular frameworks, such as TensorFlow and PyTorch, and optimally train and deploy machine learning (ML) models on Amazon EC2 Trn1, Inf1, and Inf2 instances with minimal code changes and without tie-in to vendor-specific solutions. AWS Neuron SDK, which supports Inferentia and Trainium accelerators, is natively integrated with PyTorch and TensorFlow. This integration ensures that you can continue using your existing workflows in these popular frameworks and get started with only a few lines of code changes. For distributed model training, the Neuron SDK supports libraries, such as Megatron-LM and PyTorch Fully Sharded Data Parallel (FSDP). -
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Amazon EC2 G5 Instances
Amazon
Amazon EC2 G5 instances are the latest generation of NVIDIA GPU-based instances that can be used for a wide range of graphics-intensive and machine-learning use cases. They deliver up to 3x better performance for graphics-intensive applications and machine learning inference and up to 3.3x higher performance for machine learning training compared to Amazon EC2 G4dn instances. Customers can use G5 instances for graphics-intensive applications such as remote workstations, video rendering, and gaming to produce high-fidelity graphics in real time. With G5 instances, machine learning customers get high-performance and cost-efficient infrastructure to train and deploy larger and more sophisticated models for natural language processing, computer vision, and recommender engine use cases. G5 instances deliver up to 3x higher graphics performance and up to 40% better price performance than G4dn instances. They have more ray tracing cores than any other GPU-based EC2 instance.Starting Price: $1.006 per hour -
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AWS Thinkbox Deadline
Amazon
Automatically sync on-premises asset files to Amazon Simple Storage Service (S3), ensuring availability in the cloud. Synchronize with local servers, manage data transfers before rendering begins, and tag accounts and instances for bill allocation. Purchase usage-based software licenses, bring your own licenses, or use a combination of both to create third-party digital content. Leverage Amazon Elastic Compute Cloud (EC2) Spot Instances to save up to 90% compared to on-demand pricing. Set up a render farm in minutes, run more projects in parallel, and improve cost control. Generate a hybrid or cloud-based render farm and scale to thousands of cores in minutes with the AWS Portal. Build, tailor, and deploy render farms with the Render Farm Deployment Kit (RFDK) using familiar programming languages, such as Python. Use the Jigsaw tool to render very high-resolution images faster by distributing them across multiple machines. -
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AWS Inferentia
Amazon
AWS Inferentia accelerators are designed by AWS to deliver high performance at the lowest cost for your deep learning (DL) inference applications. The first-generation AWS Inferentia accelerator powers Amazon Elastic Compute Cloud (Amazon EC2) Inf1 instances, which deliver up to 2.3x higher throughput and up to 70% lower cost per inference than comparable GPU-based Amazon EC2 instances. Many customers, including Airbnb, Snap, Sprinklr, Money Forward, and Amazon Alexa, have adopted Inf1 instances and realized its performance and cost benefits. The first-generation Inferentia has 8 GB of DDR4 memory per accelerator and also features a large amount of on-chip memory. Inferentia2 offers 32 GB of HBM2e per accelerator, increasing the total memory by 4x and memory bandwidth by 10x over Inferentia. -
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Azure Service Fabric
Microsoft
Focus on building applications and business logic, and let Azure solve the hard distributed systems problems such as reliability, scalability, management, and latency. Service Fabric is an open source project and it powers core Azure infrastructure as well as other Microsoft services such as Skype for Business, Intune, Azure Event Hubs, Azure Data Factory, Azure Cosmos DB, Azure SQL Database, Dynamics 365, and Cortana. Designed to deliver highly available and durable services at cloud-scale, Azure Service Fabric intrinsically understands the available infrastructure and resource needs of applications, enabling automatic scale, rolling upgrades, and self-healing from faults when they occur. Focus on building features that add business value to your application, without the overhead of designing and writing additional code to deal with issues of reliability, scalability, management, or latency in the underlying infrastructure.Starting Price: $0.17 per month -
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StormForge
StormForge
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.Starting Price: Free -
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Amazon EC2 Trn1 Instances
Amazon
Amazon Elastic Compute Cloud (EC2) Trn1 instances, powered by AWS Trainium chips, are purpose-built for high-performance deep learning training of generative AI models, including large language models and latent diffusion models. Trn1 instances offer up to 50% cost-to-train savings over other comparable Amazon EC2 instances. You can use Trn1 instances to train 100B+ parameter DL and generative AI models across a broad set of applications, such as text summarization, code generation, question answering, image and video generation, recommendation, and fraud detection. The AWS Neuron SDK helps developers train models on AWS Trainium (and deploy models on the AWS Inferentia chips). It integrates natively with frameworks such as PyTorch and TensorFlow so that you can continue using your existing code and workflows to train models on Trn1 instances.Starting Price: $1.34 per hour -
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Convox
Convox
Convox is a powerful platform-as-a-service (PaaS) that simplifies deploying, scaling, and managing cloud applications by abstracting infrastructure complexity and letting teams focus on shipping code. It runs directly within your cloud account and integrates with major cloud providers such as AWS, Google Cloud, Azure, and DigitalOcean, giving you full control and cost efficiency while avoiding extra hosting fees. Convox supports seamless continuous integration and delivery pipelines, auto-scaling policies, and zero-downtime deployments, with tools for environment configuration, role-based access controls, and secure workflows. It includes a developer-friendly CLI, flexible deployment configuration, and integration with common tools like GitHub, GitLab, Slack, and monitoring services, streamlining workflows and boosting productivity. Convox also offers real-time monitoring, detailed logs, and one-click rollbacks for reliable performance and easier troubleshooting.Starting Price: Free -
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Lucidity
Lucidity
Lucidity is a multi-cloud storage management platform that dynamically resizes block storage across AWS, Azure, and Google Cloud without downtime, enabling enterprises to save up to 70% on storage costs. Lucidity automates the expansion and contraction of storage volumes based on real-time data demands, ensuring optimal disk utilization between 75-80%. This autonomous, application-agnostic solution integrates seamlessly with existing applications and environments, requiring no code changes or manual provisioning efforts. Lucidity's AutoScaler is available on the AWS Marketplace, offering enterprises an automated solution to expand and shrink live EBS volumes based on workload without downtime. By streamlining operations, Lucidity enables IT and DevOps teams to reclaim hundreds of hours, allowing them to focus on higher-impact initiatives that drive innovation and efficiency. -
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AWS Deep Learning AMIs
Amazon
AWS Deep Learning AMIs (DLAMI) provides ML practitioners and researchers with a curated and secure set of frameworks, dependencies, and tools to accelerate deep learning in the cloud. Built for Amazon Linux and Ubuntu, Amazon Machine Images (AMIs) come preconfigured with TensorFlow, PyTorch, Apache MXNet, Chainer, Microsoft Cognitive Toolkit (CNTK), Gluon, Horovod, and Keras, allowing you to quickly deploy and run these frameworks and tools at scale. Develop advanced ML models at scale to develop autonomous vehicle (AV) technology safely by validating models with millions of supported virtual tests. Accelerate the installation and configuration of AWS instances, and speed up experimentation and evaluation with up-to-date frameworks and libraries, including Hugging Face Transformers. Use advanced analytics, ML, and deep learning capabilities to identify trends and make predictions from raw, disparate health data. -
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IBM Cloud® Kubernetes Service is a certified, managed Kubernetes solution, built for creating a cluster of compute hosts to deploy and manage containerized apps on IBM Cloud®. It provides intelligent scheduling, self-healing, horizontal scaling and securely manages the resources that you need to quickly deploy, update and scale applications. IBM Cloud Kubernetes Service manages the master, freeing you from having to manage the host OS, container runtime and Kubernetes version-update process.Starting Price: $0.11 per hour
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AWS Directory Service
Amazon
AWS Directory Service for Microsoft Active Directory, also known as AWS Managed Microsoft Active Directory (AD), enables your directory-aware workloads and AWS resources to use managed Active Directory (AD) in AWS. AWS Managed Microsoft AD is built on actual Microsoft AD and does not require you to synchronize or replicate data from your existing Active Directory to the cloud. You can use the standard AD administration tools and take advantage of the built-in AD features, such as Group Policy and single sign-on. With AWS Managed Microsoft AD, you can easily join Amazon EC2 and Amazon RDS for SQL Server instances to your domain, and use AWS End User Computing (EUC) services, such as Amazon WorkSpaces, with AD users and groups. AWS Managed Microsoft AD makes it easy to migrate AD-dependent applications and Windows workloads to AWS. With AWS Managed Microsoft AD, you can use Group Policies to manage EC2 instances and run AD-dependent applications in the AWS Cloud.Starting Price: $0.018 -
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Tencent Cloud Virtual Machine
Tencent
To meet your ever-changing business needs, you can quickly add or delete CVMs in minutes. By defining relevant policies, you can ensure that your CVM instances will be seamlessly scaled up during periods of higher demand to ensure application availability and scaled down during periods of lower demand to save costs. CVM offers a wide variety of instances, operating systems and software packages. You can flexibly adjust each instance’s CPU, memory, disk and bandwidth configuration to match your applications. CVM supports multiple Linux distribution versions and Windows Server versions. You can access Tencent Cloud CVM as an administrator with full control. Using various tools such as the Tencent Cloud console and APIs, you can connect to your CVM instances and perform operations like restarting and modifying your network configurations. -
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AWS Trainium
Amazon Web Services
AWS Trainium is the second-generation Machine Learning (ML) accelerator that AWS purpose built for deep learning training of 100B+ parameter models. Each Amazon Elastic Compute Cloud (EC2) Trn1 instance deploys up to 16 AWS Trainium accelerators to deliver a high-performance, low-cost solution for deep learning (DL) training in the cloud. Although the use of deep learning is accelerating, many development teams are limited by fixed budgets, which puts a cap on the scope and frequency of training needed to improve their models and applications. Trainium-based EC2 Trn1 instances solve this challenge by delivering faster time to train while offering up to 50% cost-to-train savings over comparable Amazon EC2 instances. -
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BidElastic
BidElastic
It isn’t always straightforward to benefit from the rich features of cloud services. To make it easier for businesses to use the cloud, we developed BidElastic as a resource provisioning tool with two components: BidElastic BidServer cuts computational costs; BidElastic Intelligent Auto Scaler (IAS) streamlines management and monitoring of your cloud provider. The BidServer uses simulation and advanced optimization routines to anticipate market movements and to design a robust infrastructure for cloud providers’ spot instances. To match demand in volatile workloads, you need to scale your cloud infrastructure dynamically. But that’s easier said than done. There’s a traffic spike and only 10 minutes later are new servers online. In the meantime you’ve lost customers who may never come back. To scale your resources properly you need to be able to predict computational workloads. CloudPredict does exactly that; it uses machine learning to predict computational workloads. -
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VictoriaMetrics Cloud
VictoriaMetrics
VictoriaMetrics Cloud allows users to run the Enterprise version of VictoriaMetrics, hosted on AWS, without the need to perform typical DevOps tasks such as proper configuration, monitoring, log collection, access protection, software updates, and backups. We run VictoriaMetrics Cloud instances in our environment on AWS and provide easy-to-use endpoints for data ingestion and querying. The VictoriaMetrics team takes care of optimal configuration and software maintenance. It comes with the following features: It can be used as a Managed Prometheus - configure Prometheus or Vmagent to write data to Managed VictoriaMetrics and then use the provided endpoint as a Prometheus data source in Grafana; Every VictoriaMetrics Cloud instance runs in an isolated environment, so instances cannot interfere with each other; VictoriaMetrics Cloud instance can be scaled up or scaled down in a few clicks; Automated backups;Starting Price: $190 per month -
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Veeam Backup for AWS
Veeam
Veeam Backup for AWS is a comprehensive solution designed to protect and manage data within Amazon Web Services (AWS) environments. It offers native, policy-based backups for AWS workloads, including Amazon Elastic Compute Cloud (EC2), Amazon Relational Database Service (RDS), and Amazon Virtual Private Cloud (VPC) configurations. The platform ensures data integrity through immutable and encrypted backups, providing secure protection against threats like ransomware. With wizard-driven, automated, and self-service workflows, users can perform full instance or granular recoveries, either in-place or as new entities. Additionally, Veeam Backup for AWS supports cross-account, region, and platform restores, offering flexibility and efficiency in data recovery processes. -
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Amazon Linux 2
Amazon
Run all your cloud and enterprise applications in a security-focused and high-performance Linux environment. Amazon Linux 2 is a Linux operating system from Amazon Web Services (AWS). It provides a security-focused, stable, and high-performance execution environment to develop and run cloud applications. Amazon Linux 2 is provided at no additional charge. AWS provides ongoing security and maintenance updates for Amazon Linux 2. Amazon Linux 2 includes support for the latest Amazon EC2 instance capabilities and is tuned for enhanced performance. It includes packages that help ease integration with other AWS Services. Amazon Linux 2 offers long-term support. Developers, IT administrators, and ISVs get the predictability and stability of a Long Term Support (LTS) release, but without compromising access to the latest versions of popular software packages. -
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AWS CloudFormation
Amazon
AWS CloudFormation is a infrastructure provisioning and management tool that provides you the ability to create resource templates that specifies a set of AWS resources to provision. The templates allow you to version control your infrastructure, and also easily replicate your infrastructure stack quickly and with repeatability. Define an Amazon Virtual Private Cloud (VPC) subnet or provisioning services like AWS OpsWorks or Amazon Elastic Container Service (ECS) with ease. Run anything from a single Amazon Elastic Compute Cloud (EC2) instance to a complex multi-region application. Automate, test, and deploy infrastructure templates with continuous integration and delivery (CI/CD) automation. AWS CloudFormation lets you model, provision, and manage AWS and third-party resources by treating infrastructure as code. Speed up cloud provisioning with infrastructure as code.Starting Price: $0.0009 per handler operation -
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AWS Nitro System
Amazon
The AWS Nitro System is the foundation for the latest generation of Amazon EC2 instances, enabling AWS to innovate faster, reduce costs for customers, and deliver enhanced security and new instance types. By reimagining virtualization infrastructure, AWS has offloaded functions such as CPU, storage, and networking virtualization to dedicated hardware and software, allowing nearly all server resources to be allocated to instances. This architecture comprises several key components: Nitro Cards, which offload and accelerate I/O for functions like VPC, EBS, and instance storage; the Nitro Security Chip, providing a minimized attack surface and prohibiting administrative access to eliminate human error and tampering; and the Nitro Hypervisor, a lightweight hypervisor that manages memory and CPU allocation, delivering performance nearly indistinguishable from bare metal. The Nitro System's modular design allows for rapid delivery of EC2 instance types.
