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AWS Lambda vs Amazon Elastic Inference comparison

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July 2022
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Pricing and Cost Advice
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  • "Its pricing is on the higher side."
  • "The price of the solution is reasonable and it is a pay-per-use model. It is very good for cost optimization."
  • "The cost is based on runtime."
  • "The fees are volume-based."
  • "AWS Lambda is inexpensive."
  • "Lambda is a good and cheap solution and I would recommend it to those without a huge payload."
  • "For licensing, we pay a yearly subscription."
  • "We don't need to pay for licensing to use Lambda."
  • More AWS Lambda Pricing and Cost Advice →

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    Top Answer:AWS Lambda is a serverless solution. It doesn’t require any infrastructure, which allows for cost savings. There is no setup process to deal with, as the entire solution is in the cloud. If you use… more »
    Ranking
    11th
    out of 14 in Compute Service
    Views
    603
    Comparisons
    543
    Reviews
    0
    Average Words per Review
    0
    Rating
    N/A
    1st
    out of 14 in Compute Service
    Views
    9,380
    Comparisons
    8,480
    Reviews
    27
    Average Words per Review
    333
    Rating
    8.4
    Comparisons
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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 Lambda is a compute service that lets you run code without provisioning or managing servers. AWS Lambda executes your code only when needed and scales automatically, from a few requests per day to thousands per second. You pay only for the compute time you consume - there is no charge when your code is not running. With AWS Lambda, you can run code for virtually any type of application or backend service - all with zero administration. AWS Lambda runs your code on a high-availability compute infrastructure and performs all of the administration of the compute resources, including server and operating system maintenance, capacity provisioning and automatic scaling, code monitoring and logging. All you need to do is supply your code in one of the languages that AWS Lambda supports (currently Node.js, Java, C# and Python).

    You can use AWS Lambda to run your code in response to events, such as changes to data in an Amazon S3 bucket or an Amazon DynamoDB table; to run your code in response to HTTP requests using Amazon API Gateway; or invoke your code using API calls made using AWS SDKs. With these capabilities, you can use Lambda to easily build data processing triggers for AWS services like Amazon S3 and Amazon DynamoDB process streaming data stored in Amazon Kinesis, or create your own back end that operates at AWS scale, performance, and security.

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    Sample Customers
    Expedia, Intuit, Royal Dutch Shell, Brooks Brothers
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    Media Company26%
    Comms Service Provider24%
    Computer Software Company16%
    Financial Services Firm7%
    REVIEWERS
    Computer Software Company35%
    Financial Services Firm15%
    Educational Organization8%
    Comms Service Provider8%
    VISITORS READING REVIEWS
    Computer Software Company19%
    Comms Service Provider16%
    Media Company14%
    Financial Services Firm10%
    Company Size
    VISITORS READING REVIEWS
    Small Business18%
    Midsize Enterprise8%
    Large Enterprise74%
    REVIEWERS
    Small Business40%
    Midsize Enterprise12%
    Large Enterprise49%
    VISITORS READING REVIEWS
    Small Business15%
    Midsize Enterprise12%
    Large Enterprise73%
    Buyer's Guide
    Compute Service
    July 2022
    Find out what your peers are saying about Amazon, Apache, Zadara and others in Compute Service. Updated: July 2022.
    622,358 professionals have used our research since 2012.

    Amazon Elastic Inference is ranked 11th in Compute Service while AWS Lambda is ranked 1st in Compute Service with 32 reviews. Amazon Elastic Inference is rated 0.0, while AWS Lambda is rated 8.2. On the other hand, the top reviewer of AWS Lambda writes "Programming is getting much easier and does not need a lot of configuration ". Amazon Elastic Inference is most compared with AWS Fargate, Amazon EC2 Auto Scaling and AWS Batch, whereas AWS Lambda is most compared with AWS Batch, Apache NiFi, Amazon EC2 Auto Scaling, Apache Spark and Apache Storm.

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