We performed a comparison between Amazon Elastic Inference and AWS Fargate based on real PeerSpot user reviews.
Find out in this report how the two Compute Service solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
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Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon 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, and ONNX models, with more frameworks coming soon.
In most deep learning applications, making predictions using a trained model—a process called inference—can drive as much as 90% of the compute costs of the application due to two factors. First, standalone GPU instances are designed for model training and are typically oversized for inference. While training jobs batch process hundreds of data samples in parallel, most inference happens on a single input in real time that consumes only a small amount of GPU compute. Even at peak load, a GPU's compute capacity may not be fully utilized, which is wasteful and costly. Second, different models need different amounts of GPU, CPU, and memory resources. Selecting a GPU instance type that is big enough to satisfy the requirements of the most demanding resource often results in under-utilization of the other resources and high costs.
Amazon Elastic Inference solves these problems by allowing you to attach just the right amount of GPU-powered inference acceleration to any EC2 or SageMaker instance type or ECS task with no code changes. With Amazon Elastic Inference, you can now choose the instance type that is best suited to the overall CPU and memory needs of your application, and then separately configure the amount of inference acceleration that you need to use resources efficiently and to reduce the cost of running inference.
A new compute engine that enables you to use containers as a fundamental compute primitive without having to manage the underlying instances. With Fargate, you don’t need to provision, configure, or scale virtual machines in your clusters to run containers. Fargate can be used with Amazon ECS today, with plans to support Amazon Elastic Container Service for Kubernetes (Amazon EKS) in the future.
Fargate has flexible configuration options so you can closely match your application needs and granular, per-second billing.
Amazon Elastic Inference is ranked 11th in Compute Service while AWS Fargate is ranked 8th in Compute Service with 3 reviews. Amazon Elastic Inference is rated 0.0, while AWS Fargate is rated 8.6. On the other hand, the top reviewer of AWS Fargate writes "A serverless, pay-as-you-go compute engine that you can deploy quickly". Amazon Elastic Inference is most compared with AWS Lambda, Amazon EC2 Auto Scaling and AWS Batch, whereas AWS Fargate is most compared with Amazon EC2 Auto Scaling, Amazon EC2, AWS Batch, AWS Lambda and Oracle Compute Cloud Service.
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