AWS Elastic Beanstalk vs AWS Lambda: A comparison

As organizations increasingly turn to cloud services to enhance their infrastructure, Amazon Web Services (AWS) continues to be a dominant player in the cloud computing landscape. This post will explain the AWS Elastic Beanstalk vs AWS Lambda and describe the respective strengths, limitations and scenarios in which the two services should be used. Among the many AWS offerings, AWS Elastic Beanstalk and AWS Lambda are two key services that are often considered for application delivery. Although both aim to simplify infrastructure management, they work in different ways and are tailored to different use cases.

By the end of this article, you will know exactly when to use Elastic Beanstalk and when to use Lambda depending on the needs of your application.

Introduction to AWS Elastic Beanstalk and AWS Lambda
How Elastic Beanstalk works
How AWS Lambda works
Elastic Beanstalk vs Lambda

Architecture and management
Cost models
Supported use cases
Scalability
Monitoring and troubleshooting
**When should you choose AWS Elastic Beanstalk?
**When should you choose AWS Lambda?

Elastic Beanstalk vs Lambda for different workloads
Best practices for using Elastic Beanstalk and Lambda
Conclusion: Which solution should you choose?

Introduction to AWS Elastic Beanstalk and AWS Lambda

Before you dive into detailed comparisons, it’s important to understand what AWS Elastic Beanstalk and AWS Lambda are at their core.

AWS Elastic Beanstalk

AWS Elastic Beanstalk is a Platform-as-a-Service (PaaS) offering that simplifies the deployment and scaling of web applications and services. With Elastic Beanstalk, you can deploy your applications in environments that AWS automatically provisions and manages. Elastic Beanstalk abstracts away much of the complexity of managing servers, storage and networks so developers can focus on their code.

The key features of Elastic Beanstalk:

Automated provisioning of environments
Application scaling, monitoring and management
Supports multiple programming languages such as Java, .NET, Node.js, Python, PHP, Ruby and Go
Integration with other AWS services such as S3, RDS and CloudWatch

AWS Lambda

AWS Lambda, on the other hand, is a Function-as-a-Service (FaaS) offering that allows developers to run code in response to events without having to deploy or manage servers. Lambda works with a serverless architecture where your application’s code is executed in short-lived stateless containers that are triggered by events such as data changes or incoming HTTP requests. Developers write individual functions, and AWS takes care of the infrastructure needed to run them.

The most important functions of AWS Lambda:

Serverless, event-driven computing
Code execution in response to triggers such as HTTP requests, S3 events or database updates
Pay-as-you-go pricing based on the number of requests and execution time
Automatic scaling to meet demand

How does Elastic Beanstalk work?

Elastic Beanstalk provides a PaaS experience where the user focuses on the application code and AWS takes care of everything else. Here’s how it works:

Upload application code: You upload your application code and select the desired runtime (Node.js, Python, Java, etc.).
AWS provides the infrastructure: Elastic Beanstalk automatically provides resources such as EC2 instances, Elastic Load Balancing (ELB) and Auto Scaling groups.
Environment configuration: You can configure various settings such as instance types, database connections, scaling policies and more.
Development: When you deploy your application, Beanstalk takes care of versioning, rollback and updating the environment without downtime.
Monitoring: Elastic Beanstalk is integrated with CloudWatch so you can monitor performance metrics and log data.
Scaling: As traffic increases or decreases, Elastic Beanstalk automatically scales your environment up or down based on predefined thresholds.

Essentially, AWS Elastic Beanstalk gives developers more control over the underlying infrastructure while providing many automation features that simplify resource management.

How does AWS Lambda work?

AWS Lambda works according to a completely different paradigm. Instead of managing an environment or an application server, you focus solely on writing individual functions that are executed when triggered. This is how Lambda functions work:

Upload lambda function: You write a function (in Node.js, Python, Java, etc.) and upload it to AWS Lambda or deploy it via the AWS Management Console, AWS CLI, or frameworks like Serverless.
Trigger configuration: You configure triggers for the Lambda function. These triggers can be event sources such as S3 bucket uploads, API gateway HTTP requests or a DynamoDB table update.
Execution: When a trigger event occurs, AWS Lambda executes your code in a secure, isolated container.
Automatic Scaling: AWS Lambda automatically handles scaling, meaning it can call your function in response to multiple triggers at once.
Pay for Execution: Lambda charges for the number of requests and the time it takes to execute the function, with no cost for idle time.

Unlike Elastic Beanstalk, Lambda has no concept of a continuously running application. Instead, your code is executed in response to certain events and stops as soon as the function has been executed.

Elastic Beanstalk vs Lambda

Architecture and management

Elastic Beanstalk uses a traditional server-based architecture where your application runs continuously on EC2 instances managed by AWS. Although you don’t have to manage the servers manually, Elastic Beanstalk provides a PaaS-like environment where infrastructure components such as load balancers, databases and instance types need to be configured.

AWS Lambda, on the other hand, is completely serverless. There are no servers to deploy or manage, and your application code is only executed when triggered by events. Lambda is well suited for event-driven architectures where individual functions are executed in response to incoming HTTP requests, file changes or database updates.

Cost models

Cost is one of the key differences between Elastic Beanstalk and Lambda.

Elastic Beanstalk is charged based on the resources you use such as EC2 instances, ELBs and RDS databases. Even if your application is idle, you pay for the infrastructure provided.

Lambda follows a pay-per-use pricing model. You only pay when your function is triggered and for the duration it is running. This can lead to significant cost savings for applications with unpredictable or low traffic, as there are no charges for idle time.

Supported use cases

Elastic Beanstalk is well suited for applications that require a traditional long runtime environment. This includes web applications, REST APIs and enterprise-grade services that need to be constantly available and support sessions or persistent connections.

AWS Lambda is great for scenarios where short-lived functions are required. Typical use cases are:

Event-driven applications (e.g. in response to changes in a database)
Microservices and APIs where each endpoint can be treated as a separate function
Data processing tasks such as converting files uploaded to S3

Scalability

Elastic Beanstalk provides automatic scaling by managing EC2 instances behind a load balancer. It can scale your application as needed, but scaling is not instantaneous as provisioning new instances takes time.

Lambda scales automatically and instantly in response to incoming events. Each function call is processed independently, and AWS Lambda can handle thousands of requests per second without manual intervention. This makes Lambda ideal for applications with spiky or unpredictable traffic patterns.

Monitoring and troubleshooting

Elastic Beanstalk is integrated with CloudWatch for monitoring, logging and alerting. You have access to detailed EC2 instance metrics and logs to monitor your application’s performance and troubleshoot issues. As you are working with a traditional environment, you can also install your own monitoring tools if required.

AWS Lambda is also integrated with CloudWatch to provide execution metrics such as the number of invocations, errors, and execution time. However, debugging can be more difficult as Lambda functions are stateless and short-lived. You need to rely heavily on logging within your function and on the CloudWatch logs to troubleshoot issues.

When you should choose AWS Elastic Beanstalk

Elastic Beanstalk is an excellent choice in the following scenarios:

Complex web applications: If your application requires constant availability, sessions and persistent connections, Elastic Beanstalk is better suited than Lambda.
Legacy applications: For applications built with a monolithic architecture or where significant refactoring would be required to adopt a microservices model, Elastic Beanstalk can help you move to the cloud without a major transition.
Control over infrastructure: If you need greater control over infrastructure components (e.g. managing EC2 instances, using custom AMIs), Elastic Beanstalk provides that flexibility while abstracting away some of the complexities.
Long-running background tasks: If your application requires long-running background tasks, Elastic Beanstalk provides the ability to manage workspaces that are better suited for these tasks than the short execution times of AWS Lambda.

When you should choose AWS Lambda

AWS Lambda is the best choice when:

Event-driven architectures: If your application logic is triggered by events such as file uploads, database changes, or API requests, Lambda’s event-driven model is ideal.
Microservices: Lambda fits well with microservice architectures where each service is a standalone, stateless function. This is especially true when creating APIs with AWS API Gateway.
Cost efficiency: For applications with low or irregular traffic, AWS Lambda’s pay-per-execution model can be far more cost-effective than running EC2 instances 24/7. Since you only pay for the time your code is running, this can result in significant savings, especially for applications with sporadic or unpredictable workloads.
Automatic scalability: If your application needs to scale instantly to meet fluctuating loads, AWS Lambda’s ability to automatically handle thousands of concurrent calls is a key benefit. There is no need to manually configure or provision new servers.
Fast prototyping: AWS Lambda is perfect for rapid development and deployment of small, standalone functions. You can deploy new features without having to worry about the underlying infrastructure, making it a good choice for rapid iteration and experimentation.
Serverless applications: If you’re aiming for a completely serverless architecture, AWS Lambda combines well with other AWS services like API Gateway, DynamoDB, and S3 to build scalable, event-driven applications without having to manage servers.

Elastic Beanstalk vs Lambda for different workloads

To better understand when to use Elastic Beanstalk and when to use AWS Lambda, let’s look at specific use cases and determine which service is the better choice:

Web application with predictable traffic

For a web application with constant, predictable traffic, Elastic Beanstalk is probably the better choice. You can deploy your application on a set number of EC2 instances and have it scale automatically as demand increases or decreases. This type of application often benefits from persistent connections and a dedicated runtime environment, which Elastic Beanstalk easily provides.

Event-driven applications

For applications that need to respond to specific events — such as data changes in an S3 bucket, HTTP requests via the API Gateway or database updates — AWS Lambda is a clear winner. Lambda is characterized by the fact that it processes events in real time without having to worry about provisioning or maintaining servers. The event-driven nature of Lambda ensures that your application can process a large number of events without incurring high idle costs.

API backend for mobile and web applications

When building a backend for mobile or web applications, AWS Lambda in combination with AWS API Gateway is a powerful and cost-effective solution. With Lambda, each API endpoint can be mapped to a specific function, allowing for easy scalability and lower costs compared to managing a dedicated server environment with Elastic Beanstalk. However, if you prefer a traditional, long-running backend with a monolithic design, Elastic Beanstalk may be more familiar and easier to manage.

Long-running background tasks

For long-running background tasks such as batch processing, video transcoding or data transformations, Elastic Beanstalk may be more suitable. Lambda is limited by its maximum execution time of 15 minutes and is therefore not suitable for jobs that exceed this limit. Elastic Beanstalk can support worker environments that are better suited to managing these types of tasks.

Microservices architecture

For a microservices architecture, where each service is a standalone, independently deployable function, AWS Lambda is the ideal choice. You can deploy multiple Lambda functions that work together via API Gateway, SNS, SQS or other AWS services. With this serverless approach, each function can be scaled independently, reducing costs and increasing agility.

Continuous Integration/Continuous Deployment (CI/CD) pipelines

Both Elastic Beanstalk and AWS Lambda support CI/CD pipelines, but in different ways. Elastic Beanstalk integrates well with common CI/CD tools such as Jenkins or AWS CodePipeline and enables the automated deployment of entire applications. AWS Lambda, on the other hand, allows more granular CI/CD with serverless deployment frameworks such as the Serverless Framework or AWS SAM (Serverless Application Model). For projects that require rapid iteration and deployment at the functional level, Lambda can provide more flexibility.

Best practices for the use of Elastic Beanstalk and Lambda

Regardless of whether you choose Elastic Beanstalk or AWS Lambda, there are some best practices you should follow to ensure smooth operation and scalability.

Best practices for Elastic Beanstalk

Environment separation: Use separate environments for development, staging and production. This isolates changes and reduces the risk of errors impacting running systems.
Health monitoring: Use Elastic Beanstalk’s health monitoring tools to monitor the health of your application. Use alerts to detect performance degradation early.
Efficient instance management: Choose the right EC2 instance types for your application workload to optimize cost and performance. Use auto-scaling policies to ensure your application scales up or down depending on traffic.
Database management: If your application relies on a database, you should use Amazon RDS for database scaling, backups and failover instead of managing a database on EC2 instances.
Version control: Implement version control for your deployments. Elastic Beanstalk supports version control of your application and allows you to revert to previous versions if needed.

Best Practices for AWS Lambda

Functional granularity: Divide your application into small, independently deployable functions. Each function should perform a single task to promote reusability and maintainability.
Optimize cold starts: Cold starts (the time it takes to initialize a function) can degrade performance. To avoid this, keep the codebase of your function as small as possible and avoid extensive libraries unless absolutely necessary. You can also use AWS Provisioned Concurrency to reduce cold start times.
Security: Use AWS IAM roles to grant your Lambda functions the least privileges necessary for their operation. Review roles regularly to ensure they follow the principle of least privilege.
Efficient logging: AWS Lambda is integrated with CloudWatch for logging. Make sure you log important events and errors, but avoid excessive logging as this can incur additional costs and increase execution times.
Monitoring and alerting: Use CloudWatch’s metrics to monitor the performance and error rates of your Lambda functions. Set up alerts for critical issues to respond quickly to failures.

Conclusion: Which one should you choose?

When comparing AWS Elastic Beanstalk and AWS Lambda, the right choice depends heavily on your application architecture, your traffic patterns, and the level of control you need over the infrastructure.

Choose AWS Elastic Beanstalk if:
- You need a traditional environment for a long-running web application or service.
- Your application has constant, predictable traffic and requires persistent connections or sessions.
- You need more control over the infrastructure, e.g. when customizing EC2 instances or load balancing.
Choose AWS Lambda if:
- You want a completely serverless architecture with minimal infrastructure management.
- Your application is event-driven and can respond to triggers such as HTTP requests, database updates or file uploads.
- You want to build a microservices-based application with independent, scalable functions.
- You want to minimize costs by only paying for the actual execution of the code, especially if your workload is unpredictable or intermittent.

Ultimately, AWS Elastic Beanstalk and AWS Lambda are both powerful tools that serve different use cases. Elastic Beanstalk offers more control over infrastructure for traditional, long-running applications, while AWS Lambda provides a cost-effective, scalable and serverless solution for event-driven workloads.

In many cases, you will find that a hybrid approach where you combine both services delivers the best results. For example, you could use Elastic Beanstalk to host a monolithic web application and use AWS Lambda to handle certain background tasks or event-driven processes. This flexibility makes AWS a versatile platform for building modern applications that can scale with the needs of your business.

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