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 13, 2026
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Covered services
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AWS App Runner availability
AWS App Runner closed to new customers on April 30, 2026. Existing customers can continue to use
the service, including creating new resources. If you are a new customer and want a similar
experience that runs containers without managing infrastructure, use Amazon ECS Express Mode. For
more information, see AWS App Runner availability
change and Amazon ECS Express
Mode.
Introduction
Amazon Web Services (AWS) gives you the flexibility to choose different compute
options to build and run modern applications that map to your business needs. It provides
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. 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 face.
It is common to choose one of two operational models to meet this challenge: serverless compute or Kubernetes.
When you modernize existing applications, you can also use AI-assisted services to
accelerate the work. AWS Transform uses agentic AI to migrate and modernize .NET, VMware,
and mainframe workloads at scale. For building AI agents into your applications, AWS
provides Amazon Bedrock AgentCore, the open source Strands Agents SDK, and the AWS
Well-Architected Agentic AI Lens for design guidance.
Understand
Developers and data engineers might 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 developers and data engineers 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 high level of autonomy
and allows 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. Organizations can start with a distributed
model and later migrate toward a centralized model. This shift often happens as the
number of workloads increases and consistency across cost, performance, and security
becomes more important. However, in both models, serverless can reduce the infrastructure
management overhead.
Organizations adopt another model, especially in the Kubernetes space: 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 advantage of the
cloud. The strategy and tools that infrastructure, platform, and DevOps teams use often
drive these 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, AWS CDK, CloudFormation, and other
community tools.
Organizations that use Kubernetes might use Kubernetes-based tools for automation
using GitOps tools such as Argo CD, and Kubernetes API-based cloud provisioning tools like
AWS Controllers for
Kubernetes (ACK), Kube Resource
Orchestrator (kro), or the CNCF Crossplane project. Amazon EKS Capabilities provides managed Argo CD, AWS
Controllers for Kubernetes, and kro, so platform teams can use these tools without
installing and operating them in their clusters.
These tooling choices also cover 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, such as Argo CD, 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 supports 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 allows for different use cases such as
microservices, modernized monoliths, event-driven architectures, tools built by operations
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) might require accelerated compute such
as AWS Trainium or GPU instances.
- 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 can 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 such as AWS Lambda and Amazon ECS Express Mode are optimized to
help organizations write code quickly, deploy it, and change it. These capabilities provide
useful options for fast prototyping work that does not require many 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.
You can also use other options for prototyping. For Amazon ECS, you can create dedicated
clusters and use AWS Fargate or dedicated nodes. For Kubernetes, platform teams can
create dedicated clusters or dedicate namespaces within a cluster to prototyping teams.
You can use open source projects such as CNCF BuildPacks on the buildpacks.io website or Knative on the Knative website to
simplify the configuration experience.
- AI-assisted modernization
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If you are modernizing existing applications, consider services that use AI to
accelerate the work. Choose AWS Transform when you need AI-assisted .NET, VMware, or
mainframe modernization at scale. AWS Transform uses agentic AI to automate discovery,
planning, and code transformation, with human review at key steps.
AWS Transform includes the following options:
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AWS Transform custom: Transforms code
patterns such as language version upgrades, framework migrations, and code
refactoring. For more information, see AWS Transform custom.
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AWS Transform continuous modernization: A
managed option that analyzes and remediates technical debt across connected
repositories on an ongoing basis. For more information, see AWS Transform continuous modernization.
- Agentic architecture patterns
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Modern applications increasingly include AI agents. If you are building agents into
your applications, consider the following AWS options:
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Amazon Bedrock AgentCore: Choose Amazon Bedrock
AgentCore when you need managed infrastructure to deploy and operate AI agents,
including runtime, memory, identity, and tool access. AgentCore works with open
source agent frameworks and models in or outside Amazon Bedrock. For more information,
see Amazon Bedrock
AgentCore.
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Amazon ECS and Amazon EKS: Choose Amazon ECS or Amazon EKS when you
want to run agents alongside your existing containerized applications, with the same
deployment, networking, security, and scaling controls you already use.
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Strands Agents SDK: Choose the Strands Agents SDK
when you want an open source, model-driven SDK to build agents in code. For more
information, see Introducing
Strands Agents, an open source AI agents SDK.
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AWS Well-Architected Agentic AI Lens: Use the
Agentic AI Lens for design guidance that covers agent infrastructure, orchestration
patterns, and operational practices. For more information, see AWS Well-Architected Agentic AI Lens.
- AI-augmented development
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AI coding tools can help development teams write, review, and refactor code. Choose
Kiro when you need an agentic development tool with spec-driven workflows, chat, and
Model Context Protocol (MCP) support. For more information, see Kiro on the kiro.dev website.
If your teams use the Amazon Q Developer IDE plugins, note that AWS will
discontinue support for them on April 30, 2027, and stopped accepting new sign-ups on
May 15, 2026. Kiro is the recommended successor for similar IDE and agentic coding
capabilities. For more information, see Amazon Q
Developer IDE plugins end of support.
Choose
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. Large organizations commonly 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 and Amazon ECS with AWS Fargate, including Amazon ECS Express Mode.
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Optimized to let developers focus on writing code without provisioning or
managing servers, which minimizes operational overhead.
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Amazon ECS
Amazon ECS Express Mode
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 that want Kubernetes and the CNCF ecosystem as their standard
platform interface. Teams with platform engineering skills can customize and operate
clusters directly. Teams without them can use Amazon EKS Auto Mode, which manages compute,
storage, and networking and keeps nodes patched and up to date.
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Amazon EKS
Amazon EKS Auto
Mode
Red Hat OpenShift Service on AWS (ROSA)
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If you are modernizing existing applications rather than starting new ones, choose AWS
Transform when you need AI-assisted .NET, VMware, or mainframe modernization at scale. AWS
Transform uses agentic AI to automate discovery, planning, and code transformation, with
human review at key steps. For more information, see AWS Transform.
App2Container, Microservice Extractor for .NET, and AWS Migration Hub Refactor Spaces
closed to new customers on November 7, 2025. AWS Transform is the recommended successor for
these modernization tasks. For more information, see AWS Transform custom.
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 includes 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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Containers on AWS
Learn about containers on AWS through blog posts, code patterns, and
visuals.
Explore the site
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Amazon ECS deployment
This guide offers an overview of Amazon ECS deployment options on AWS and shows how
to use it to manage a simple containerized application.
Explore the guide
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Amazon ECS Immersion Day
This workshop expands on your foundational understanding of containers and
provides practical experience scaling, monitoring, and managing container workflows
using Amazon ECS and AWS Fargate.
Explore the
workshop
- AWS App Runner
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AWS App Runner closed to new customers on April 30, 2026. If you are a new customer, use
Amazon ECS Express Mode instead. Existing App Runner customers can continue to use the resources
that follow.
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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
- Amazon ECS Express Mode
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Create your first Amazon ECS Express Mode service
Follow a step-by-step tutorial to deploy your first web application or API with
Amazon ECS Express Mode.
Get
started with the tutorial
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Amazon ECS Express Mode overview
Learn how Amazon ECS Express Mode provisions and configures the infrastructure to run a
web application or API, including a Fargate-based service, a load balancer with
SSL/TLS, auto scaling, monitoring, and networking.
Explore the
guide
- AWS Transform
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AWS Transform custom
Learn how AWS Transform custom uses agentic AI to transform code patterns such
as language version upgrades, framework migrations, and code refactoring.
Explore
the guide
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AWS Transform continuous modernization
Learn how AWS Transform analyzes and remediates technical debt across connected
repositories as a managed, ongoing process.
Explore the
guide
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AWS Transform
Explore how AWS Transform accelerates .NET, VMware, and mainframe
modernization.
Explore the service
- 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
- 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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Using AWS Lambda with other services
Explore common use cases and learn how invocation works. This guide includes a
table of the services that work with Lambda and how each service invokes it.
Explore
the guide
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Serverless Land
Get the latest information, blogs, videos, code, and resources for AWS
serverless.
Explore the site
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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 your code runs.
Explore the guide
- 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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Deploy a Kubernetes application on Linux
Learn how to deploy a containerized application to a Kubernetes cluster managed by
Amazon EKS on Linux nodes.
Get
started with the tutorial
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Deploy a Kubernetes application on Windows
Learn how to deploy a containerized application onto a Kubernetes cluster managed
by Amazon EKS on Windows nodes.
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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Streamline Kubernetes cluster management with Amazon EKS Auto
Mode
Learn how you can automate cluster management without deep Kubernetes expertise
using Amazon EKS Auto Mode.
Read the blog
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Use your on-premises infrastructure in Amazon EKS clusters with
Amazon EKS Hybrid Nodes
With Amazon EKS Hybrid Nodes, you can unify Kubernetes management across your cloud and
on-premises environments, and take advantage of the scale and availability of Amazon EKS in
all the places your applications need to run.
Read the blog
- 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 instead of standard Kubernetes, and
explore ROSA on AWS in depth.
Watch the
video
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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AWS Well-Architected Agentic AI Lens
Explore design guidance and best practices for building agentic AI into your
applications, including agent infrastructure, orchestration, and operations.
Explore the
lens
