What is Cloud Native, really?
This is one of those topics where everyone seems to have a slightly different view and most of them are valid. That’s the beauty of computer science: there’s rarely a single right answer. You’ve probably come across some of the many answers out there, and I won’t dive into all of them here. It’s simply too much to cover in a single article. I want to share my perspective here. For other viewpoints, you can explore online resources and read different takes on the subject.
Before diving into cloud-native development and design, we need to first understand what software design and software architecture are.
Software Design
Software design is the process of planning and defining how individual components or modules of a software system will work. It focuses on the detailed implementation of features, algorithms, data structures, and user interfaces, ensuring each module is functional, reusable, and integrates seamlessly with others. It operates at a lower level, producing outputs like class diagrams, flowcharts, and pseudocode that directly guide coding efforts.
Software Architecture
Software architecture is the high-level blueprint of a software system, defining its structure, components, and their interactions. It focuses on overarching decisions like scalability, performance, reliability, and technology choices, ensuring the system aligns with business goals and is adaptable to future needs. Outputs include system architecture diagrams, deployment strategies, and communication flows, serving as a foundation for software design and development.
Relation Between Software Architecture and Design
The relationship between software architecture and software design is collaborative and hierarchical. Architecture provides the high-level structure and guiding principles, while design focuses on the detailed implementation of those principles within individual components. Software architecture defines the overall system blueprint, including how components interact, scalability, and technology choices, creating constraints and opportunities for the design process. Software design, in turn, implements the architectural vision by detailing how each module will function, including algorithms, data structures, and workflows. Together, they ensure the system is both cohesive at a macro level (architecture) and functional at a micro level (design).
Bridging Architecture and Design in Cloud-Native Development
Understanding both software architecture and design is a foundation for cloud-native development because they address different but complementary aspects of building cloud-native systems. Cloud-native architecture focuses on the overall structure of the system, such as adopting microservices, using containers, and leveraging orchestration tools like Kubernetes. Meanwhile, cloud-native design deals with the specific implementation details of each module or service, including API design, resilience patterns, and error handling. Together, they ensure that the system is both scalable and robust at a high level and efficient and reliable in its individual components.
What is Cloud-Native Development?
Eventually we need to ask this question right. What is cloud native? I will cheat for this answer. There is a really good explanation of this question from it’s source. Cloud Native Foundation in 23 different languages. Source
Cloud Native Computing Foundation
The Cloud Native Computing Foundation (CNCF) is a Linux Foundation project that was started in 2015 to help advance container technology and align the tech industry around its evolution.
Cloud native practices empower organizations to develop, build, and deploy workloads in computing environments (public, private, hybrid cloud) to meet their organizational needs at scale in a programmatic and repeatable manner. It is characterized by loosely coupled systems that interoperate in a manner that is secure, resilient, manageable, sustainable, and observable.
It focuses on creating scalable, resilient, and flexible systems using modern practices such as microservices, containers, DevOps, and continuous delivery. Applications are designed to run in distributed environments, ensuring they can scale dynamically, recover from failures quickly, and adapt to changing demands. By embracing cloud-native development, organizations can deliver software faster, optimize resource usage, and respond more effectively to business needs.
If we go to the first description from their GitHub page from May 18, 2018, there was a slightly different answer for this.
Cloud native technologies empower organizations to build and run scalable applications in modern, dynamic environments such as public, private, and hybrid clouds. Containers, service meshes, microservices, immutable infrastructure, and declarative APIs exemplify this approach. These techniques enable loosely coupled systems that are resilient, manageable, and observable. Combined with robust automation, they allow engineers to make high-impact changes frequently and predictably with minimal toil.
Let’s focus on the latest definition from the Cloud Native Computing Foundation and unpack what it really means. Later in this article, we’ll also explore why that definition has evolved over time.
The answer describes the key attributes of a well-designed cloud-native system (or modern software architecture). Let’s break it down:
Loosely Coupled Systems
The components of the system are independent and interact with each other through well-defined interfaces (like APIs). Changes or failures in one component don’t directly affect the others, allowing for easier updates and better fault tolerance.
Example
An e-commerce platform running on Kubernetes (Amazon EKS, Azure AKS, Google GKE) has the following microservices:
- Order Service (Node.js) running in an AWS Fargate container
- Payment Service (Java) deployed as an AWS Lambda function
- Inventory Service (Python) using Amazon DynamoDB
Services communicate via Amazon SQS for asynchronous messaging and fault isolation. If the Inventory Service is unavailable, the order flow continues, and stock updates are retried later.
Interoperate
These systems can work together seamlessly using standardized communication protocols (like HTTP or gRPC) and managed messaging services provided by cloud platforms.
Example
Let’s imagine a ride-sharing platform built entirely on Google Cloud using:
- Driver Matching Service (Go) on Cloud Run
- Passenger Request Service (Python) on Cloud Functions
- Pricing Engine (Java) on Google Kubernetes Engine (GKE)
All services communicate using gRPC over Google Cloud Pub/Sub, while Google Cloud Load Balancing manages traffic flow securely and efficiently between services.
Secure
The system ensures data confidentiality, integrity, and access control using managed identity, encryption, and secure networking services.
Example
A banking app on Azure uses:
- Azure Key Vault to securely store secrets and certificates
- Azure Active Directory for OAuth 2.0-based authentication and RBAC
- Azure API Management for secure, rate-limited API exposure
- Azure Disk Encryption to encrypt data at rest
Each microservice is deployed using Azure Container Apps and leverages Managed Identities to ensure secure, least-privilege access without managing Kubernetes.
Resilient
The system recovers gracefully from failures using built-in redundancy, automated scaling, and chaos testing tools provided by cloud providers.
Example
A streaming service on AWS ensures resilience by:
- Using Amazon EC2 Auto Scaling Groups for elasticity
- Running fault injection with AWS Fault Injection Simulator
- Hosting across regions with Amazon Route 53 global DNS failover
- Backing its data layer with Amazon Aurora Multi-AZ
If one region goes offline, Route 53 reroutes traffic to a healthy region with minimal user impact.
Manageable
The system is designed for operational simplicity with cloud-native deployment tools, monitoring dashboards, and managed CI/CD pipelines.
Example
A SaaS company builds and operates its platform on Azure, using:
- Azure DevOps Pipelines for CI/CD automation
- Bicep templates for infrastructure as code
- Azure Monitor and Application Insights for real-time observability
- Azure Log Analytics for central log management and performance tracking
This setup enables reliable deployments and continuous feedback across the platform.
Sustainable
The system is built with scalability and resource efficiency in mind, leveraging serverless, autoscaling, and rightsizing tools from cloud platforms.
Example
A growing retail startup on Google Cloud runs:
- Stateless services on Cloud Run, which automatically scales to zero when idle
- Event-driven tasks via Cloud Functions
- A serverless, scalable database using Cloud Firestore
- Budget alerts and Google Cloud Recommender for identifying unused resources
This architecture minimizes operational cost and carbon footprint while scaling with demand.
Observable
Visibility into the system is achieved through managed logging, tracing, and monitoring tools that work across services and regions.
Example
A logistics company running on AWS ensures observability with:
- Amazon CloudWatch for metrics, logs, and alarms
- AWS X-Ray for distributed tracing and service maps
- AWS CloudTrail for auditing infrastructure events
- CloudWatch Synthetics to simulate and monitor user journeys
When issues arise, engineers can trace them end-to-end across microservices and regions.
Why the Definition Evolved
The original definition of cloud-native, published in 2018, focused heavily on specific technologies, containers, service meshes, microservices, immutable infrastructure, and declarative APIs. At the time, these tools were revolutionary. They enabled faster deployments, better scaling, and a shift away from traditional monolithic applications. The industry needed guidance, and a technology-centric definition gave teams something tangible to work toward.
But over time, as teams gained experience and cloud adoption matured, a new realization emerged: cloud-native isn’t just a set of tools. It’s a philosophy of how software should be designed, deployed, and maintained.
The newer definition from the CNCF reflects this shift. It broadens the perspective to include operational and cultural dimensions of building software. It introduces terms like resilient, secure, manageable, sustainable, and observable, all qualities that speak to how systems behave in the real world, not just how they are built.
In essence, the focus has moved from what you use to how you think:
- Resilience means building for failure from day one, not as an afterthought.
- Observability implies that every system must be understandable at scale.
- Sustainability points to long-term adaptability and efficient resource use.
- Security is now integral, not optional.
This evolution also reflects how organizations and engineering cultures have changed. It recognizes the need for repeatability, automation, and scalable practices across multi-cloud and hybrid environments. It’s not about locking into one tool. It’s about adopting the mindset that your system must evolve continuously, just like your product and your team.
At the same time, cloud providers have stepped up to support this evolution. They’ve built integrated, cloud-native services that embody these principles out of the box. Whether it’s AWS X-Ray for tracing, Azure Managed Identities for secure access, or Google Cloud Run for auto-scaling stateless apps, cloud vendors now provide purpose-built tools for observability, security, resilience, and scale.
Instead of assembling and configuring a patchwork of open-source components, teams can now build highly capable systems using first-party services that align with cloud-native ideals and no Kubernetes expertise required.
In short, the definition matured because our challenges matured and because the tools we have today are much more powerful and opinionated than they were just a few years ago.
From Lift-and-Shift to Cloud-Native by Design
In the early stages of cloud adoption, lift-and-shift was the dominant strategy. Companies took their existing applications and moved them to cloud infrastructure with minimal code changes. This helped reduce operational overhead, improve availability, and start the transition to cloud,but it didn’t fundamentally change how software was built or delivered. In most cases, teams continued using the same tools, patterns, and workflows, just hosted on different infrastructure.
To truly adopt cloud-native practices, organizations must go beyond infrastructure migration and re-educate their teams. That means adapting new architectural patterns, adapting to managed services, and learning technologies like serverless platforms, event-driven design, managed identity, distributed tracing, and infrastructure-as-code. The shift is not just technical. It’s cultural. Teams need to evolve their skill sets, development workflows, and mindset around automation, scale, and resilience.
Today, that phase is largely behind us. Most major enterprises have completed their initial cloud migration. The real challenge now is designing systems that take full advantage of the cloud not just running in it, but thriving in it.
This is why modern cloud-native thinking focuses less on “which technologies are you using?” and more on “how are you building? Cloud-native isn’t defined by using Kubernetes, or any single stack. It’s defined by principles like automation, resilience, observability, and rapid iteration.
Cloud providers recognize this shift, too. They’ve evolved from simply offering infrastructure to offering complete platforms that support these principles out of the box. Whether it’s event-driven services like AWS Lambda, zero-maintenance containers with Google Cloud Run, or secure identity access via Azure Managed Identities, the cloud is no longer just a place to host code, it’s a toolkit for building better systems.
In this new era, success is not just about migrating to the cloud, it’s about rethinking how we build for it.
Final Thoughts: Cloud-Native Is a Mindset, Not a Stack
Cloud-native development isn’t just about adopting Kubernetes or moving to containers. It’s about embracing a mindset that prioritizes resilience, agility, observability, and scale from day one.
Yes, tools matter but they’re only a means to an end. Real cloud-native systems are designed to evolve, to recover, and to scale, regardless of the tech stack underneath. That’s why the conversation has shifted from “What are you using?” to “How are you building?”
Cloud providers now offer integrated solutions that support this philosophy, services that simplify identity, observability, messaging, and deployment. But for these tools to make a real impact, teams need to adapt their practices, learn new skills, and rethink their systems with cloud-native principles in mind.
So, if you’re planning your next architecture or building a new feature, don’t just ask:
“Should we use Kubernetes?”
Ask:
“Are we building something that can survive failure, scale effortlessly, and evolve with our needs?”
That’s the heart of cloud-native. And it’s much bigger than any one technology.