Compare AWS Lambda vs. Amazon Elastic Inference

Cancel
You must select at least 2 products to compare!
Amazon Elastic Inference Logo
310 views|281 comparisons
AWS Lambda Logo
5,553 views|4,949 comparisons
Most Helpful Review
Find out what your peers are saying about Apache, Amazon, StackStorm and others in Compute Service. Updated: March 2021.
474,319 professionals have used our research since 2012.
Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pricing and Cost Advice
Information Not Available
"AWS is slightly more expensive than Azure.""Its pricing is on the higher side."

More AWS Lambda Pricing and Cost Advice »

report
Use our free recommendation engine to learn which Compute Service solutions are best for your needs.
474,319 professionals have used our research since 2012.
Questions from the Community
Ask a question

Earn 20 points

Top Answer: The basic feature that I like is that there is no server installation. It also has good support for various languages, such as Java, .NET, C#, and Python.
Top Answer: Its price should be improved. Its pricing is on the higher side. I am not sure if it currently supports the Go language. If it doesn't support the Go language, they can introduce it.
Ranking
11th
out of 13 in Compute Service
Views
310
Comparisons
281
Reviews
0
Average Words per Review
0
Rating
N/A
2nd
out of 13 in Compute Service
Views
5,553
Comparisons
4,949
Reviews
8
Average Words per Review
619
Rating
8.4
Popular Comparisons
Learn More
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.

Offer
Learn more about Amazon Elastic Inference
Learn more about AWS Lambda
Sample Customers
Expedia, Intuit, Royal Dutch Shell, Brooks Brothers
Netflix
Top Industries
VISITORS READING REVIEWS
Media Company30%
Comms Service Provider25%
Computer Software Company22%
Financial Services Firm8%
VISITORS READING REVIEWS
Computer Software Company26%
Media Company19%
Comms Service Provider17%
Energy/Utilities Company6%
Company Size
No Data Available
REVIEWERS
Small Business33%
Midsize Enterprise8%
Large Enterprise58%
Find out what your peers are saying about Apache, Amazon, StackStorm and others in Compute Service. Updated: March 2021.
474,319 professionals have used our research since 2012.

Amazon Elastic Inference is ranked 11th in Compute Service while AWS Lambda is ranked 2nd in Compute Service with 9 reviews. Amazon Elastic Inference is rated 0.0, while AWS Lambda is rated 8.4. 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, Apache Spark, Apache Storm and IBM Streams.

See our list of best Compute Service vendors.

We monitor all Compute Service reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.