AWS Lambda vs Containers – What’s the Difference? (Pros and Cons). AWS developers have several methods for deploying applications to the cloud. Two of the methods – AWS Lambda and Containers (Docker) have become increasingly popular over the years. With the help of Lambda and Containers, developers can build applications with more flexibility and far less overhead than applications hosted on virtual machines or traditional servers.
In this article, we’ll go over the benefits of each deployment and why you might choose one over the other. Let’s dive in.
Serverless vs Container
These two -Serverless and Containers have some similarities like eliminating complexity and make it easier to deploy and scale applications.
Serverless (Lambda)
Firstly, serverless is best suited when you need to perform quite simple processing of events without maintaining underlying infrastructure. With serverless computing like Lambda you can run applications and services and not think about them.
Containers (Docker)
On the other hand with containers you get full control over the application hosting environment. They provide a standard way to package your application’s code, configurations and dependencies in one place.
What is AWS Lambda?
AWS Lambda is a serverless, event based computing service for AWS. With this computing service, events happen automatically in response to inputs referred to as triggers. As a result, developers can package code written in various programming languages, including C#, Java, Python, Go, Ruby, Node.js, and PowerShell, without worrying about administrative tasks.
With Lambda, you don’t have to worry about booking AWS server resources, maintaining servers, manually scaling CPU, RAM. Additionally you don’t need to bother about memory with demand, managing the operating system, or installing security patches. You can build and run any type of application or service and scale it with high availability handled for you.
So, is AWS Lambda serverless computing?
Not really. You see, the serverless concept refers to not needing to maintain your own servers to run functions. AWS Lambda is a fully managed service, so you don’t have to think about servers. Instead, you focus on writing application code in whatever programming language you prefer.
How Does AWS Lambda Work?
When you create a Lambda function (the code you run in AWS Lambda), AWS packages it in its own container and then executes the container on a multi tenant cluster of machines. AWS allocates the required CPU and RAM to each function.
After your function finishes running, the RAM allocated to it is multiplied by the time it took to run. The result forms the basis of your AWS bill. All this happens automatically, so you don’t have to worry about the underlying mechanics.
This Lambda tool integrates seamlessly with other AWS services so you can create large, flexible, secure, and cost effective applications in the cloud. These services include database management, app development, web hosting, backup and restore, ecommerce, big data analytics, IoT, streaming, and chatbots.
So, what are some of the key features of AWS Lambda? Let’s find out.
Key Features of AWS Lambda
These following features of AWS Lambda we have found:
- There is benefit with AWS Lambda to allow you to use custom logic to extend other AWS services.
- Being able to build custom backend services that are triggered on-demand using the AWS Lambda API or a custom API built with the Amazon API Gateway.
- There is no need to learn a new programming language with AWS Lambda. It supports PowerShell, Go, Java, Node.js, Python, C#, and Ruby. It also comes with a Runtime API to allow you to use any other programming language.
- The service in AWS Lambda is completely automated, so your developers don’t have to manage servers and are free to work on what matters most – improving customer experience.
- There is in built fault tolerance with AWS Lambda. This is to protect your code from server failures and guarantees high availability.
- Scale automatically with AWS lambda to match the resource needs of your code. You don’t have to fiddle with configurations or deal with deployment delays.
- You pay with Lambda only for what you use rather than a fixed server cost.
- Provisioned Concurrency gives you greater control over the performance of your serverless application. You can scale up, scale down, or shut down your application on demand.
Pros of AWS Lambda
- With AWS Lambda it simplifies cloud infrastructure by abstracting the provisioning and managing servers and load balancers.
- Teams experience improved productivity due to Lambda’s simplified development and deployment model.
- Automatic scalability of Lambda allows you to handle spikes in server activity without taking a hit on performance. In short, you don’t have to worry about the number of concurrent requests your application can handle before running out of resources.
- Resource wastage through overprovisioning is nipped in the bud leading to lower cloud bills.
- You don’t have to allocate a large budget to DevOps due to the simplified architecture of AWS Lambda. Lambda is a fully managed service, so there’s no need to buy hardware or hire specialists to maintain your application.
Cons of AWS Lambda
- Functions timeout after 15 minutes, so containers are not ideal for running large workloads.
- Your AWS Lambda bill can spike if you don’t know how to optimize costs.
- There’s a limit of 250MB on the size of your Lambda code. Zipped versions shouldn’t exceed 20MB.
- You can only run a maximum of 1000 concurrent requests. AWS will throttle anything above this limit.
Next in this article AWS Lambda vs Containers – What’s the Difference? we will look at what is a container.
What are Containers?
Containers provide a standard way to package applications whereby they’re separated from the underlying infrastructure. In essence, your application’s code, dependencies and configurations are packaged into a single object that can be consistently deployed on-premise and in the cloud server.
The Containerized applications give you an easy way to overcome the challenges of traditional virtualization. Since they’re portable, you can move them from one host to another as long as they support the container runtime.
Compared to traditional virtual machines, containers are much faster and more efficient because only the operating system is virtualized. As a result, containers are more lightweight than virtual machines.
How Do Containers Work?
At the heart of it, containers are a type of virtualization technology. You use the hardware of a single computer to create multiple virtual computers. The only difference between containers and virtual machines (VMs) is their architecture.
A VM provides hardware virtualization while a container provides virtualization at the operating system level. In a nutshell, each container gets its own isolated user space on a single host machine. The containers share a single operating system kernel. In short, containers don’t carry a guest operating system with them the way a VM does.
A good example of containers are Docker and Kubernetes. Containers are ope -source platform that is designed to be lightweight and simple. A Docker container is a standardized unit of software used to deploy applications. apps are containerized and run on a single cloud server.
Key Features of Containers on AWS
- Security – AWS provides security isolation between containers so you can grant granular access permissions for each of your containers. The service also comes with 210 compliance, security, and governance services.
- Reliability and availability – AWS containers come with 69 availability zones in over 22 regions. You get SLAs for all AWS container services.
- You can run containers on AWS using several services. AWS Fargate is ideal when you want to run your containers on a serverless computer. Amazon EC2 is perfect when you want to take charge of installation, management and configuration of your compute environment. Consider Elastic Container Services (ECS) and Elastic Kubernetes Service (EKS) container orchestrator services when you have a large scale deployment.
- AWS containers come deeply integrated with AWS Services. You can take advantage of the security, networking, monitoring, and elasticity available on AWS when deploying containerized applications.
Pros of Containers
- Lower infrastructure costs because you can run several containers on one virtual machine.
- Containerization allows for more consistent operation since applications run the same way wherever you deploy them.
- Using containers supports DevOps teams and agile efforts to fast-track development, testing and production cycles.
- Containers improve efficiency by allowing you to deploy, patch, and scale applications rapidly.
- In addition the containers allow for full portability of applications between cloud and on-premise environments.
Cons of Containers
- Running applications in containers comes with security related limitations due to the additional abstraction layer.
- Many security admins don’t fully understand the technology.
- Using containers can complicate the development process.
- Not all applications are suited for running in containers.
- Containerization can introduce all kinds of new challenges to cloud cost management. Inexperienced admins can run up an unnecessary number of containers leading to cloud sprawl.
- Containers share the OS kernel, so any vulnerability exposes all containers running on it.
AWS Lambda vs Containers – What are the Similarities?
The similarities of AWS Lambda and containers are there, even though they are distinct technologies. They share some functionalities. Let’s go over them:
- They both enable you to deploy application code.
- Both eliminate complexity and make it easier to scale applications.
- They’re both an improvement over virtual machines.
- They are both in need orchestration to use effectively at scale.
AWS Lambda vs Containers – What are the Differences?
So, what are the differences between AWS Lambda and containers?
General features differences in Lambda and Containers
AWS Lambda
- Serverless so no management.
- Scale it up with usage increase.
- Pay as you go when it comes to usage.
- It is highly available.
Containers (Docker)
- There is a software unit that packages the code and its dependencies.
- Applications run quickly and reliably from one computing environment to another.
Cost in Serverless vs container
Serverless AWS Lambda
With serverless computing, you only pay for what you use so serverless can be great choice for cost savings. But on the other hand AWS Lambda is usually more expensive than containers because you’re offloading the management and maintenance of services to the cloud provider (AWS).
Containers (Docker)
With containers, you pay for them as long as they’re on (Pay-as-you-go). Containers are constantly running in most of the cases. For workloads with consistent demand containers are better choice. But with containers you must consider the cost of the services that power your containers. You have to be extra careful, especially with dynamic workloads, since you may end up paying for idle resources.
Ease of Deployment of Lambda and Containers
AWS Lambda
AWS Lambda is easier to deploy since there’s way less work involved. But on the contrary serverless is more difficult to run outside a cloud environment.
Containers (Docker)
On the plus side to containers, they run the same no matter where they are deployed and anywhere.
They are so flexible as containers can run in a cloud, local data center or on a developer’s workstation.
Host Environments in Lambda and Containers
Serverless AWS Lambda
The limitation with serverless is that you can only run Lambda on AWS servers. The infrastructure is then managed by Cloud Provider. There is no flexibility to choose your own.
Containers (Docker)
Big benefit is that you can run containerized applications on any Linux server or selected Windows servers. You get full control of your environment (example Virtual machine size).
The minus side of containers is the need for management (e.g. patching, updates) and orchestration. But on the other side of the coin you can install almost any software.
There is a benefit of prepackaged container images with different software available.
Scalability of Lambda vs container
AWS Lambda scalability
Firstly it scales automatically and there is no need for scaling configuration. The server resources scale automatically to meet demand. More often that not, each request connection invokes a new Lambda instance.
Secondly you control the process using concurrency limit.
Containers (Docker) scalability
Interestingly containers require that you plan for scaling by purchasing the necessary server capacity for containers to run smoothly as demand increases. There is Horizontal Pod Autoscaling for configuring scaling.
Maintenance of AWS Lambda and Containers
AWS Lambda
For Lambda, you offload server and infrastructure maintenance to AWS.
Containers (Docker)
But for containers, you have to manage the backend yourself.
Monitoring of AWS Lambda vs Containers
AWS Lambda Monitoring
Lambda comes with integration of CloudWatch for monitoring.
Containers (Docker) Monitoring
For monitoring with containers you can install an agent to work with Prometheus or Grafana..
Use cases of AWS Lambda vs Containers
AWS Lambda use cases
- When you unsure of scaling pattern so you can automatically adjust scaling due to demand.
- You want to avoid infrastructure management and resources management.
- Pay as you go model – so you want to pay as there is more traffic and you grow.
- Develop a cloud native applications.
- To process data from multiple sources by using simple functions.
Containers (Docker) use cases
- Predicable traffic pattern is predictable and fast scaling.
- Ideal tools to apply for machine learning.
- Use your own choice of operating system and have full control over your infrastructure.
- Easily deployed on prem or on cloud infrastructure like Hybrid Cloud.
- DevOps and CI/CD process: with containers DevOps can eliminate environment differences between development, staging or production deployments.
- Specific version requirements (Web Server, App Server).
- Option of faster migration to the cloud.
- Containers and microservices: containers are lightweight, isolated and portable so ideal for microservices creation.
- With containers you can deploy anywhere. You can run on-prem or cloud applications like Hybrid Cloud.
That’s it! Thank you for reading AWS Lambda vs Containers – What’s the Difference? Let’s summarize.
AWS Lambda vs Containers – What's the Difference? Conclusion
To conclude when you want complete control over your application environment then containers are the best option for you. They are beneficial when moving the applications to the cloud due to replication the on prem. For large scale microservice workloads running Docker containers on our own EC2 cluster using ECS is ideal choice.
Good application with Lambda will come for smaller scale workloads. It is an excellent choice if you want to code and deploy applications quickly without the hassle of maintaining servers and other infrastructure. It’s also the best option for unpredictable workloads that are difficult to scale up and down in a containerized environment.
To summarize we would recommend implementing your code in Docker containers wherever possible, even when Lambda is your preferred deployment option. Docker containers move well across cloud providers but can also be used with numerous AWS services. Finally, it makes it easy to change your deployment and hosting needs as the company’s changing the size and needs.
