Compare Amazon EC2 vs. Amazon Elastic Inference

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Pricing and Cost Advice
"For our usage, the cost is approximately $20,000 to $23,000 per month."

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Questions from the Community
Top Answer: The scalability of the solution is fantastic. It's one of our favorite features.
Top Answer: We're charged depending on the run time, but there are other costs as well, including costs for transactions and storage.
Top Answer: The customization and configuration could be simplified. Updates could be automated and simplified.
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Ranking
4th
out of 13 in Compute Service
Views
305
Comparisons
141
Reviews
2
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631
Avg. Rating
8.0
11th
out of 13 in Compute Service
Views
65
Comparisons
60
Reviews
0
Average Words per Review
0
Avg. Rating
N/A
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Also Known As
Amazon Elastic Compute Cloud, EC2
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Overview

Amazon Elastic Compute Cloud (Amazon EC2) is a web service that provides secure, resizable compute capacity in the cloud. It is designed to make web-scale cloud computing easier for developers.

Amazon EC2’s simple web service interface allows you to obtain and configure capacity with minimal friction. It provides you with complete control of your computing resources and lets you run on Amazon’s proven computing environment. Amazon EC2 reduces the time required to obtain and boot new server instances to minutes, allowing you to quickly scale capacity, both up and down, as your computing requirements change. Amazon EC2 changes the economics of computing by allowing you to pay only for capacity that you actually use. Amazon EC2 provides developers the tools to build failure resilient applications and isolate them from common failure scenarios.

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.

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Sample Customers
Netflix, Expedia, TimeInc., Novaris, airbnb, LamborghiniExpedia, Intuit, Royal Dutch Shell, Brooks Brothers
Find out what your peers are saying about Apache, Amazon, StackStorm and others in Compute Service. Updated: October 2020.
442,517 professionals have used our research since 2012.
Amazon EC2 is ranked 4th in Compute Service with 3 reviews while Amazon Elastic Inference is ranked 11th in Compute Service. Amazon EC2 is rated 8.4, while Amazon Elastic Inference is rated 0.0. The top reviewer of Amazon EC2 writes "Good user interface with great built-in monitoring and very good documentation". On the other hand, Amazon EC2 is most compared with Apache NiFi, whereas Amazon Elastic Inference is most compared with AWS Fargate.

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