AWS offers an array of computing services that can be used to host large-scale applications. But it can be confusing for beginners as they begin to explore the service environment for their particular use case since Lambda, EC2, and Beanstalk are amongst the most overlapping application execution environments in the AWS ecosystem. Users are required to understand the strengths of these AWS services to tailor their cloud environment to the needs of a particular workload. That said, this blog post aims to compare key parameters of Lambda, EC2, and Beanstalk, namely use cases, shared responsibility, and limitations to help you get a clear grasp of when to choose these services for a particular use case.

AWS Lambda

Overview

AWS Lambda is a function as a service that enables users to run code when an event is triggered. Lambda functions scale automatically and seamlessly to handle the rate of incoming requests without requiring any configuration.

Parameter 1: Use Cases

Serverless API

Lambda function can be integrated with an API gateway to deploy a serverless API. In this case, the API Gateway invokes the Lambda function synchronously.

Thumbnail Generation

Real-time thumbnail generation is a complex task to do at scale. However, the S3 event notification feature can be used to invoke a Lambda function asynchronously whenever a new image gets uploaded to the S3 bucket by a user.

This use case is ideal for Lambda as the functions are lightweight and generating thumbnails saves S3 storage costs. However, users will be charged highly by AWS if the source bucket and the destination bucket are the same.

Parameter 2: Shared Responsibility

AWS Lambda is fully managed by AWS so you don’t have to worry about the infrastructure provisioning or patching. Lambda service provides built-in fault tolerance that empowers users to concentrate on code rather than data center failures.

Parameter 3: Limitations

The current function timeout is 900 seconds (15 minutes), at the time of drafting this blog, which cannot be increased. So, it’s better to avoid Lambda for operations that require an execution time of more than 15 minutes.

Most web servers today use connection pooling for relational databases to enhance query performance. And as mentioned above, the Lambda service invokes a new function instance whenever a new request appears. While this is good for scaling, Lambda will create a new connection to the RDS database for each invocation which can hit the RDS max_connections. So, in this case, it’s better to avoid Lambda or use an RDS proxy to manage connection pooling which can incur high costs.

Lambda has a cold start problem which is nothing but the time taken by the function to handle the first request. This occurs because AWS stops the container when there is no load but when the new request comes in, it tends to schedule the container on a computing resource which introduces latency. So, if you have a use case that demands very low latency, then you might need to look for alternatives or use provisioned concurrency.

Amazon EC2 (Elastic Compute Cloud)

Overview

Amazon Elastic Compute Cloud (EC2) is a virtual cloud infrastructure service offered by AWS that provides users complete control over their computing resources. Amazon EC2 offers on-demand computing resources through which users can create powerful servers in the cloud. Additionally, EC2 enables users to get a virtual machine up and running in just a few clicks.

Parameter 1: Use Cases

High-performance Computing/Machine Learning Applications

High-performance computing/Machine Learning applications require heavy computing resources and throughputs like GPUs and disk-based volumes (EBS). And EC2 is an excellent fit for those applications if they are OS-specific otherwise it’s better to deploy such applications on containers. AWS provides various kinds of virtual machine instances like Compute-optimized (C), Memory-optimized (R), General purpose (M), and Burstable (T).

Anything that Can Run on OS

Workloads that are interruptible in nature can be deployed on spot instances or spot blocks while saving up to 90% off the on-demand price.
If you have a use case that requires computing resources for a certain period of time, say daily or weekly, then it can be deployed on scheduled reserved instances.

Databases like MySQL, MongoDB can be deployed on memory-optimized instances as they require indexes to be stored in the memory. But it is recommended to use managed services like RDS for MySQL unless you require full control and flexibility on your database instance.

Parameter 2: Shared Responsibility

AWS manages the underlying physical infrastructure while the customer that deploys the EC2 instance needs to manage the security groups, firewalls, configurations, patching, security utilities, applications installed on the guest operating system.

Parameter 3: Limitations

There are no particular set of limitations on EC2 but it is recommended to use a managed service for the application that you are deploying on EC2. Because with EC2 you’d need to manage a lot of things but most of those things have already been taken care of by managed services. Use Amazon RDS for hosting relational databases like MySQL, PostgreSQL, MariaDB, and SQL Server. RDS takes care of managing and patching the underlying database instance. Use Elastic Beanstalk if you want to deploy a web application but don’t want to manage the underlying EC2 instance.

AWS Elastic Beanstalk

Overview

Elastic Beanstalk is a PAAS (Platform as a service) offered by AWS that allows the deployment and scale of web applications that are easily developed in Java, .NET, PHP, Node.js, Python, Ruby, Go, and Docker very easily. Elastics Beanstalk can help developers manage some aspects of the infrastructure such as creating, testing, and deploying applications on the platform.

Parameter 1: Use Cases

Web Applications

If you want to deploy a web application that is developed in supported runtimes then it’s better to deploy it on the Beanstalk. But if you have a static website then it’s better to deploy it on S3 and Cloudfront to save costs.

Environments and Application Versions

Beanstalk provides a feature called the environments that allows users to easily distribute their application into multiple environments such as production, non-production, and test environments. This feature can be leveraged by development teams to test their application without affecting the production version.

Parameter 2: Shared Responsibility

Elastic Beanstalk automatically handles the deployment, from capacity provisioning, load balancing, auto-scaling to application health monitoring. What’s more, you retain full control over the AWS resources powering your application and can access the underlying resources at any time.

Parameter 3: Limitations

Single container Docker vs multi-container Docker application: Though Beanstalk supports both the single container docker and multi-container docker deployments. It uses ECS (Elastic container service) under the hood to deploy a multi-container docker. So, if you have a microservices’ architecture, then it’s better to deploy it directly on ECS as it provides more control, flexibility, and scaling than Beanstalk multi-container docker.

Bottom Line

As you can see, there is no hard and fast rule to tell which service to use because it depends upon the specific use cases of a business. But as a rule of thumb, prefer to use more managed services like Lambda & Beanstalk wherever possible as a lot of things will be managed by AWS, giving you room to focus on the important growth aspects of your business. That said, when making the choice of AWS Lambda vs. Elastic Beanstalk vs. EC2, it is down to flexibility and the knowledge that you have about these three computing services.

The teams here at Quantiphi carefully assess your application requirements and craft designs that exploit the strengths of each of these AWS services as appropriate. We help you get the right balance of PaaS, FaaS, and IaaS and enable your applications to be more responsive and scalable, yet cost fairly less to operate and maintain.

Being an AWS Advanced Consulting Partner enables Quantiphi to build and deploy AWS solutions at scale, leveraging Machine Learning and Artificial Intelligence and developing critical solutions to address complex business challenges. Interested in further exploring how Quantiphi can help your business harness the power of cutting-edge AWS services and turn your cloud-stored data into actionable business opportunities? Reach out to our experts now.