Compare Amazon Elastic Inference vs. Apache Storm

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Amazon Elastic Inference Logo
255 views|232 comparisons
Apache Storm Logo
1,753 views|1,503 comparisons
Ranking
11th
out of 13 in Compute Service
Views
255
Comparisons
232
Reviews
0
Average Words per Review
0
Rating
N/A
7th
out of 13 in Compute Service
Views
1,753
Comparisons
1,503
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.
465,836 professionals have used our research since 2012.
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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.

Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm is simple and can be used with any programming language.
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Sample Customers
Expedia, Intuit, Royal Dutch Shell, Brooks Brothers
Groupon, Spotify, The Weather Channel, Twitter, FullContact
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Computer Software Company29%
Comms Service Provider20%
Financial Services Firm10%
Educational Organization8%
Find out what your peers are saying about Apache, Amazon, StackStorm and others in Compute Service. Updated: February 2021.
465,836 professionals have used our research since 2012.

Amazon Elastic Inference is ranked 11th in Compute Service while Apache Storm is ranked 7th in Compute Service. Amazon Elastic Inference is rated 0.0, while Apache Storm is rated 0.0. On the other hand, Amazon Elastic Inference is most compared with AWS Fargate, Amazon EC2 Auto Scaling, AWS Lambda and AWS Batch, whereas Apache Storm is most compared with Apache NiFi, AWS Lambda, Google Cloud Dataflow, Azure Stream Analytics and IBM Streams.

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