Choosing a modern application strategy
Taking the first step
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Purpose
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Help determine which modern application development approach is the
best fit for your organization.
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Last updated
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November 23, 2023
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Covered services
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Introduction
Amazon Web Services (AWS) provides you with the flexibility to choose different compute
options to build and run modern applications that map to your business needs. We provide
you with access to the right operational model for your compute choice.
Developers and data engineers prefer a level of autonomy when choosing which compute
models match which workloads. When initially developing modern applications, development
teams need to manage and operate their applications directly.
As more workloads are developed, you might decide to create central platform or
operations teams. The function of these central teams varies - some provide standard
architectures and templated patterns for development teams to use, and others operate
and manage workloads on behalf of multiple development teams.
These central teams strive to create standards for controlling costs, achieving the
right performance and security, simplifying operations, and providing common
architecture patterns. Achieving the right balance between autonomy and standardization
is a challenge that enterprises and other large organizations often deal with.
It is common to choose one of two operational models to meet this challenge: serverless compute or
Kubernetes.
Understand
Developers and data engineers may have different compute requirements. For example, a
developer might choose AWS Lambda because it is optimized for event-driven patterns and gives
access to hundreds of managed integrations.
Alternatively, a data engineer might choose an open-source framework like KubeFlow or Ray on
Amazon Elastic Kubernetes Service (Amazon EKS) because it simplifies deployment of machine-learning models but
allows access to the right high-powered instances.
Each role tends to develop skills in technology stacks over time, and has preferences about
the tools they use, both look at their compute choice on a workload-by-workload basis, which
means operational roles need to support a variety of workloads since they often work across
teams. These roles include platform engineers, cloud administrators, or site reliability
engineers (SREs).
Those in operational roles are challenged to provide autonomy for developers and data
engineers while making sure they can deploy, operate, and monitor all workloads consistently to
meet security, performance, resiliency, and cost requirements. Over time, as you develop more
modern applications, these roles need to standardize on the tools to automate the deployment and
monitoring of their workloads.
The choice between serverless compute and Kubernetes as an operational model is often driven
by the need to have the right balance between autonomy and standardization with the number of
resources one dedicates to running and operating workloads. Many workloads can be built
successfully using either of these options. But, for some workloads, there are inherent
advantages of one over the other.
A good team can make the compute choice entirely transparent to developers. Poor choices can
limit developers options, and lead to sub-standard outcomes. Operators are always affected by
the selected operating model and it will determine needs such as common libraries and networking
configuration - as well as how the organization will interact with underlying services it needs
to configure.
The operational model will often be determined by your organizational structure and skill
around automation and operations. These roles might be distributed across development teams,
different parts of your organization, or be centralized. The structure and skills of these teams
are often among the most important considerations when choosing between a serverless or
Kubernetes operational model.
The serverless operational model prioritizes shifting much of the work involved in
provisioning and managing compute resources to AWS. This model includes services such as
AWS Lambda and
the Amazon Elastic Container Service (Amazon ECS).
The second model, Kubernetes on AWS, meets an important need for organizations that
prioritize the Cloud Native Computing Foundation (CNCF)
ecosystem with a managed experience using Amazon EKS.
Consider
For developers and data engineers, we recommend that you evaluate the most appropriate
compute option on a workload-by-workload basis within your operational strategy. Here are some
of the key criteria to consider when determining your strategy.
- Organizational structure and skill
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Enterprises tend to organize their development teams around one of the following
models:
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Distributed: Each team owns their development,
deployment, and operational functions. This model provides a great level of autonomy
and allow teams to innovate at their own pace.
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Centralized: A centralized team maintains
standards, creates automations, and facilitates the sharing of knowledge and best
practices.
These are separate ends of a spectrum, and organizations may start with a distributed
model and later migrate towards a centralized model as the number of workloads increases
and the need for consistency across cost, performance and security of workloads becomes an
important factor. However, in both models, serverless can reduce the infrastructure
management overhead.
Another model organizations adopt, especially in the Kubernetes space, are the
formation of central teams that specialize in platform engineering. These skilled
engineering teams have developer skills and build and maintain platforms made up of common
automation, deployment, and observability tools. Amazon EKS is often the choice for these
organizations.
Identifying the structure and skill of your development teams can be critical to
choosing the appropriate platform.
- Operational model
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Organizations standardize on automation technologies to take full benefit of the
cloud. The strategy and tools used by infrastructure, platform, and DevOps teams often
drives decisions.
For example, organizations have choices for tooling that automates the creation,
configuration, and maintenance of infrastructure, resources, and workloads. These tools
include infrastructure-as-code (IaC) tools such as Terraform, CDK, AWS CloudFormation, or other
community tools.
Organizations that use Kubernetes might use Kubernetes-based tools for automation
using GitOps tools such as ArgoCD and Flux and Kubernetes API-based cloud provisioning
tools like AWS Controllers for Kubernetes (ACK) or the CNCF Crossplane project.
These tooling choices extend to tools that extend to security, testing, networking,
observability, performance, and more. For each of these categories, there is a stack of
available tools.
These automation tools often have built in integrations and accelerators for compute
choices. For example, AWS Serverless Application Model (AWS SAM) is optimized for serverless developers to build
and quickly deploy Lambda functions. Other tools, like ArgoCD, automate the deployment and
configuration of Kubernetes workloads.
As customers increase workloads, it can be a burden to support a large number of
tooling choices. We recommend standardizing a set that support the most workload
patterns.
- Workload characteristics
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It is important to evaluate the most appropriate compute option on a
workload-by-workload basis within the default strategy. You should always strive to
achieve the desired performance, security, and cost benefit for each workload.
A good standardization strategy will allow for different use cases such as
microservices, modernized monoliths, event driven architectures, tools built by operation
teams, and data processing workloads, such as machine learning, batch processing, and
stream processing.
These workloads have different architectural characteristics. The strategy adopted
should allow flexibility to support all the stages of a developer or data scientist
workflow.
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Application developer: Needs to run multiple
environments such as development, prototyping, test, staging, and production.
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Data engineer: These workflows involve streaming
and acting on large data models, cleaning, training, running inference with models,
and building applications and data pipelines that experiment with the data, including
Jupyter Notebooks.
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AI/ML scientist: Generative Artificial
Intelligence (AI) Large Language Models (LLM) may require specialized compute
instances such as AWS Trainium or other GPU-based architecture.
- Integrations
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Applications do not exist in isolation. They are supported by technologies such as
databases, messaging, streaming, orchestration, and other services. An effective modern
app development strategy requires integration with these services. Managed integrations
simplify operational overhead as much as management of the underlying
infrastructure.
AWS serverless compute options, such as AWS Lambda, are integrated into the AWS
ecosystem. Lambda can subscribe to events from more than 250 other services.
AWS-managed offerings for Kubernetes also provide integrations with many AWS
managed offerings. For example, you can use AWS Controllers for Kubernetes to provision
native AWS resources using a Kubernetes API. In addition, Kubernetes has a rich ecosystem,
offering integration with numerous open-source projects. Amazon EKS works with many
other AWS services to provide additional solutions for business
challenges.
- Prototyping
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Many organizations need to create experiments to validate ideas. The ability to
provide an environment where you can quickly write, deploy, and validate ideas is
essential for a healthy environment.
This environment is often overlooked when developing a modern app development
strategy, but the ability to innovate may depend on it. Enabling teams to use services
that allow builders to rapidly build, test, and iterate help in discovering new business
opportunities and receive feedback faster.
Serverless compute options like AWS Lambda and AWS App Runner are optimized to enable
organizations write code quickly, deploy it, and change it. These capabilities provide
useful options for doing fast prototyping work that doesn’t require making a lot of
choices upfront. Developers can quickly turn an idea into a modern, working application.
The AWS
serverless compute option provides choices to prototype with minimal cost or
operational overhead.
Other options can be used for prototyping: for Amazon ECS, you can create dedicated
clusters and use AWS Fargate or dedicated nodes for prototyping, for Kubernetes,
platform teams can create dedicated clusters or have namespaces within a cluster dedicated
to prototyping teams. Open source projects such as CNCF BuildPacks or Knative can
be used to simplify the configuration experience.
Choose
AWS offers different container options, such as Amazon ECS, serverless containers with
AWS Fargate, and AWS App Runner, and different Kubernetes options, such as Amazon EKS, ROSA, and
self-managed Kubernetes on Amazon EC2.
The following comparison table can help you determine your approach based on your workload
requirements. You might choose pieces of both approaches, or have different teams that use
different approaches. It is not uncommon to see a very large organizations have departments with
different strategies.
| Modern application approach |
When would you use it? |
What workload is it optimized for? |
Serverless services |
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Serverless
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Use when AWS managed services and tools are your first choice, such as
AWS Lambda, AWS App Runner, and Amazon ECS.
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Optimized for enabling developers to focus solely on writing code without the need
to manage or provision servers, minimizing operational overhead.
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Amazon ECS
AWS App Runner
AWS Fargate
AWS Lambda
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Kubernetes
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Use when Kubernetes is your primary compute platform interface.
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Optimized for teams with central platforms teams that invest in platform
engineering skills. Platform engineers are skilled at keeping clusters up to date with
the fast-moving CNCF Kubernetes versioning strategy.
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Amazon EKS
Red Hat OpenShift Service on AWS (ROSA)
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Use
Now that you have determined which approach best fits your workload for your environment, we
recommend that you review the following service-specific resources to help you begin
implementing your approach. This include links to in-depth documentation, hands-on tutorials,
and other key assets to help get you started.
- Amazon ECS
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Getting started with Amazon ECS
We provide an introduction to the tools available to access Amazon ECS and introductory step-by-step procedures to run containers.
Explore the guide
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Tutorials for Amazon ECS
Explore more than a dozen tutorials on how to perform common tasks—including the creation of clusters and VPCs.
Get started with the tutorials
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What's new and what's next with Amazon ECS
Learn what’s new since the launch of Amazon ECS Anywhere, new features of AWS Fargate, and a look ahead at the exciting enhancements to Amazon ECS
Watch the video
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Amazon ECS deployment
This guide offers an overview of Amazon ECS deployment options on AWS and shows how it can be used to manage a simple containerized application.
Explore the guide
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Amazon ECS workshop
This workshop is designed to educate those that might not be familiar with AWS Fargate, Amazon ECS, and possibly even Docker container workflow.
Explore the workshop
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Deploy Docker containers on Amazon ECS
Learn how to run a Docker-enabled sample application on an Amazon ECS cluster behind a load balancer, test the application, and delete your resources to avoid charges.
Get started with the tutorial
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- AWS App Runner
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Getting started with AWS App Runner
Learn how to configure the source code and deployment, service build, and service runtime to deploy your application to App Runner.
Get started with the tutorial
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AWS App Runner: From code to a scalable, secure web application in minutes
Explore how AWS App Runner was designed to make it easier for you to deploy web apps and APIs to the cloud, regardless of the language they are written in, even for teams that lack prior experience deploying and managing containers or infrastructure.
Read the blog
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Deploy a web app using AWS App Runner
Learn how to deploy a containerized web app using AWS App Runner. Start with your source code or a container image. App Runner automatically builds and deploys the web application and load balances traffic with encryption.
Get started with the tutorial
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- AWS Fargate
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Getting started with AWS Fargate
Understand the basics of AWS Fargate, a technology that you can use with Amazon ECS to run containers without having to manage servers or clusters of EC2 instances.
Explore the guide
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Getting started with the console using Linux containers on AWS Fargate
Get started with Amazon ECS on AWS Fargate by using the Fargate launch type for your tasks in the Regions where Amazon ECS supports AWS Fargate.
Explore the guide
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Creating a cluster with a Fargate Linux task using the AWS CLI
Learn how to set up a cluster, register a task definition, run a Linux task, and perform other common scenarios in Amazon ECS with the AWS CLI.
Get started with the tutorial
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- AWS Lambda
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What is AWS Lambda
Learn more about AWS Lambda, a compute service that lets you run code without provisioning or managing servers.
Explore the guide
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Guide to AWS Lambda Pricing
Explore and understand AWS Lambda pricing. You are charged based on the number of requests for your functions and the duration it takes for your code to start.
Explore the guide
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Using AWS Lambda with other services
Explore common use cases, learn how invocation works and includes a table that covers the services that work with Lambda and how it can be invoked from that service.
Explore the guide
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- Amazon EKS
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Getting started with Amazon EKS
Learn more about Amazon EKS, a managed service that you can use to run Kubernetes on AWS without needing to install, operate, and maintain your own Kubernetes control plane or nodes.
Explore the guide
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Amazon EKS deployment
Explore Amazon EKS deployment options on AWS and learn how it can be used to manage a general containerized application.
Explore the guide
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Amazon EKS Quick Start Reference Deployment
Using a Quick Start reference deployment guide, we provide step-by-step instructions for deploying Amazon Elastic Kubernetes Service (Amazon EKS) clusters.
Explore the guide
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Deploy a Kubernetes application
Learn how to deploy a containerized application onto a Kubernetes cluster managed by Amazon EKS.
Get started with the tutorial
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Amazon EKS workshop
Explore practical exercises to learn about Amazon EKS.
Visit the workshop
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- ROSA
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Getting started with Red Hat OpenShift Service on AWS
Learn how to get started using Red Hat OpenShift Service on AWS
Explore the guide
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Why would you use ROSA?
Learn when you might use Red Hat OpenShift over standard Kubernetes and explores ROSA on AWS in depth.
Watch the video
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Explore
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Architecture diagrams
Explore reference architecture diagrams to help you implement your modern app
development approach.
Explore architecture diagrams
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Whitepapers
Explore whitepapers to learn best practices in implementing your modern app
development approach.
Explore whitepapers
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AWS Solutions
Explore vetted solutions and architectural guidance for common modern app
development use cases.
Explore solutions
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