Becoming cloud-native is not simply moving servers to a cloud provider. It is a sustained change to how applications are designed, delivered and operated: workloads should be manageable through repeatable automation, and systems should be secure, resilient and observable. The practical route is to set measurable goals, choose a migration path for each workload, build the platform and operating practices teams need, then expand in stages.
What does cloud-native mean?
The Cloud Native Computing Foundation (CNCF) defines cloud-native practices as a way to develop, build and deploy workloads across public, private and hybrid computing environments “to meet their organizational needs at scale in a programmatic and repeatable manner.” Its Cloud Native Definition v1.1, approved February 26, 2024, describes systems that interoperate while being secure, resilient, manageable, sustainable and observable.
That definition makes cloud-native an approach to building and operating systems, not a location or a single technology. Containers, microservices, service meshes, multi-tenancy, immutable infrastructure, serverless and declarative APIs are common elements, but the CNCF’s list is non-exhaustive. An organization does not need to adopt every item to become cloud-native.
A container can make an application easier to package and deploy, but containers alone do not provide repeatable delivery, secure operations or resilience. Those depend on automation and the practices around the workload as well.
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How do I migrate to cloud-native?
Treat migration as a sequence of planning, execution and validation—not as business-as-usual infrastructure work. The goal is to choose changes that serve an organizational need, then verify that they produced the intended result.
1. Set outcomes and constraints
Choose the results that matter before selecting technologies or counting servers. Goals might include more frequent releases, improved reliability, faster recovery, cost control or better developer productivity. Record constraints such as compliance, data location, application dependencies and available skills; these shape which paths are practical.
2. Inventory and classify workloads
For each application, map its dependencies, data stores, operational owner and compliance needs. Identify which systems must move together and where teams rely on manual work or tightly coupled components. This inventory helps prevent a fast infrastructure move from overlooking the application and operating changes needed afterward.
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3. Select a path for each workload
There is rarely one suitable migration pattern for an entire estate. Rehosting may fit a workload when migration speed and minimal change matter most. Refactoring or re-architecting may make sense when the expected benefit justifies changes to the application or its data. Other workloads may need an intermediate platform change or a more substantial rewrite. Compare the options against the workload’s needs rather than treating any one path as a universal destination.
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Establish the platform capabilities and support model before onboarding large numbers of teams. Start with a small pilot, document supported paths and runbooks, and expand when the platform is ready to support the next cohort. Keep legacy and newer systems operating together when the transition requires it.
5. Validate and improve
Check results against the goals set at the outset. Use what the pilot reveals about reliability, delivery, security, cost and team experience to adjust the platform and decide what to modernize next. A migration is not successful merely because workloads have changed location.
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Should we lift and shift or refactor?
Neither approach is inherently right for every application. Rehosting makes fewer application changes, while refactoring or re-architecting changes more of the workload to pursue benefits that a simple move may not deliver. The table is a planning guide, not a guarantee: outcomes depend on the workload, constraints and team’s ability to operate the result.
| Approach | Change depth | Time to value | Operational burden and resilience | Portability and economics | Readiness to consider |
|---|---|---|---|---|---|
| Rehost (“lift and shift”) | Low application change; move the workload with limited redesign. | Can prioritize migration speed. | May preserve existing coupling and operational toil rather than improve them. | Run cost and provider dependence need validation after the move; migration alone does not establish savings or portability. | Useful when speed and low change dominate, provided the team accepts and validates the resulting operating model. |
| Replatform | Intermediate change: adapt the workload to a different platform without a full redesign. | Between a minimal move and deeper modernization; actual timing depends on the workload. | May change how the workload is operated, but does not by itself establish resilience or reduce support needs. | Assess platform-specific services, licensing and engineering effort for the chosen design. | Consider when a platform change has a clear benefit and the team can support it. |
| Refactor or re-architect | Change application or data design to address identified constraints or pursue a specific benefit. | Requires more engineering change than rehosting; value depends on the outcome sought. | Can enable different scaling, deployment or failure-isolation choices, but adds complexity that must be operated. | Compare provider-specific dependencies and total engineering and operating effort against the expected benefit. | Suitable when the expected value justifies the work and product and operations teams can own the changes. |
| Rewrite | Substantial replacement of application implementation. | Not a shortcut to migration; delivery timing depends on scope and replacement readiness. | Introduces the burden of building, validating and operating a replacement while managing the existing workload. | Requires an explicit assessment of engineering effort, licensing and any provider-specific design choices. | Consider only where the case for replacing the workload is clear and the organization can manage the transition. |
For every option, assess organizational readiness alongside technical fit: team skills, product ownership, governance and ability to adopt DevOps practices can determine whether the intended benefits are attainable. A full lift-and-shift can preserve legacy constraints, so validate the post-move outcome before treating the workload as modernized.
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A platform should make secure, repeatable delivery practical for application teams while giving operators the information and controls to run workloads. Its exact design depends on the organization, but foundational capabilities commonly include:
- Container and image management, with controls for how workloads are built and deployed.
- Identity, secrets management and network policy.
- Continuous integration and delivery (CI/CD), plus infrastructure as code for repeatable changes.
- Observability that helps teams understand system behavior and investigate failures.
- Backup and recovery, with cost controls that make spending visible and manageable.
These capabilities need owners, documented ways of working and support—not just installed tools. A “golden path” can give teams a supported route for common tasks, while runbooks clarify how to respond when systems need attention. The platform team should pilot those paths with users and improve them before broad onboarding.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Is Kubernetes the same as cloud-native?
No. Kubernetes is an open-source container orchestrator: it schedules containers across nodes and can coordinate infrastructure resources such as load balancers and persistent storage. Its declarative approach supports reproducible deployment and lifecycle automation, making it one possible component of a cloud-native platform.
Kubernetes is not a complete cloud-native strategy. It does not by itself provide a well-designed application, secure operating practices, team ownership or the wider automation and observability a workload needs. It also brings platform and skills requirements. Managed Kubernetes services can reduce some undifferentiated operational work, but may increase dependence on the provider.
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CNCF’s 2025 survey reported that 82% of container users ran Kubernetes in production. That figure describes surveyed container users; it is not a measure of cloud-native maturity across all organizations.
How do we measure cloud transformation success?
Measure outcomes that reflect the goals chosen for the workloads and teams—not infrastructure activity alone. A useful scorecard can combine delivery, operations, security, cost and user impact:
- Delivery: lead time and deployment frequency show how work moves from change to release.
- Reliability: recovery time and service reliability help show how teams respond to failures.
- Security: security findings help track issues that require attention.
- Economics: unit cost helps relate spending to the workload or service delivered.
- User outcomes: user-focused measures test whether technical improvements support the intended business result.
Use these measures to decide what to improve next. A workload move that raises deployment counts but leaves reliability, recovery or user outcomes unchanged has not demonstrated the full value of transformation.
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