Compare AWS Batch vs. Amazon Elastic Inference

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Amazon Elastic Inference Logo
210 views|193 comparisons
AWS Batch Logo
2,695 views|2,616 comparisons
Ranking
11th
out of 13 in Compute Service
Views
210
Comparisons
193
Reviews
0
Average Words per Review
0
Rating
N/A
6th
out of 13 in Compute Service
Views
2,695
Comparisons
2,616
Reviews
0
Average Words per Review
0
Rating
N/A
Find out what your peers are saying about Apache, Amazon, StackStorm and others in Compute Service. Updated: February 2021.
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Also Known As
Amazon Batch
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Overview

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.

AWS Batch enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. AWS Batch dynamically provisions the optimal quantity and type of compute resources (e.g., CPU or memory optimized instances) based on the volume and specific resource requirements of the batch jobs submitted. With AWS Batch, there is no need to install and manage batch computing software or server clusters that you use to run your jobs, allowing you to focus on analyzing results and solving problems. AWS Batch plans, schedules, and executes your batch computing workloads across the full range of AWS compute services and features, such as Amazon EC2 and Spot Instances.

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Sample Customers
Expedia, Intuit, Royal Dutch Shell, Brooks Brothers
Hess, Expedia, Kelloggs, Philips, HyperTrack
Top Industries
No Data Available
VISITORS READING REVIEWS
Media Company30%
Computer Software Company19%
Comms Service Provider15%
Energy/Utilities Company7%
Find out what your peers are saying about Apache, Amazon, StackStorm and others in Compute Service. Updated: February 2021.
463,678 professionals have used our research since 2012.

Amazon Elastic Inference is ranked 11th in Compute Service while AWS Batch is ranked 6th in Compute Service. Amazon Elastic Inference is rated 0.0, while AWS Batch is rated 0.0. On the other hand, Amazon Elastic Inference is most compared with AWS Fargate, Amazon EC2 Auto Scaling and AWS Lambda, whereas AWS Batch is most compared with AWS Lambda, AWS Fargate, Apache Spark and Apache NiFi.

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