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 shared the answer in 23 different languages. Source
The three major cloud providers have also published their own articles on the topic.
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
In a loosely coupled system, each component is designed to function independently. Services interact through well-defined interfaces, typically via APIs or messaging systems, so that a failure or update in one component doesn’t directly impact the others. This architecture allows for greater flexibility, scalability, and fault tolerance across the system.
Example
Imagine an e-commerce platform built using AWS services, where each part of the system is deployed independently:
- Order Service (Node.js) running in an AWS Fargate container
- Payment Service (Java) deployed as an AWS Lambda function
- Inventory Service (Python) hosted on Elasticbeanstalk using an Amazon DynamoDB
The services communicate asynchronously using Amazon SQS. If the Inventory Service becomes temporarily unavailable, the system continues processing orders, and stock updates are queued and handled later, ensuring a smooth user experience despite the partial outage.
Interoperate
In a cloud-native system, services are often developed in different languages, deployed independently, and maintained by separate teams. Despite these differences, they must work together seamlessly. This is made possible by standardized communication protocols (like HTTP/REST or gRPC) and cloud-managed messaging and routing services that enable consistent and reliable interactions across components.
Example
Imagine a ride-sharing platform built entirely on Google Cloud, with each service using the best-fit compute option while maintaining loose coupling and seamless communication:
- Driver Matching Service (Go) deployed on Cloud Run
- Passenger Request Service (Python) running on Cloud Run
- Pricing Engine (Java) running in a container on Google Kubernetes Engine (GKE)
All services expose HTTP/gRPC interfaces, and communication is handled through a combination of Google Cloud Load Balancing for synchronous calls and Google Cloud Pub/Sub for event-driven, asynchronous messaging. This hybrid approach enables high performance and loose coupling, ensuring a smooth and scalable experience for both drivers and passengers.
Secure
Security is a core pillar of cloud-native systems. It goes beyond just protecting data. It’s about enforcing least-privilege access, managing secrets securely, encrypting everything by default, and ensuring secure communication between services. Cloud providers offer native tools that make implementing these best practices easier and more consistent.
Example
A banking application deployed on Azure follows modern security practices by using fully managed services:
- Azure Key Vault stores secrets, certificates, and encryption keys securely
- Azure Active Directory handles OAuth 2.0 authentication and fine-grained role-based access control (RBAC)
- Azure API Management acts as a secure, throttled gateway for exposing internal APIs
- Azure Disk Encryption ensures that all stored data is encrypted at rest
Each microservice is deployed as an isolated unit using Azure Container Apps and is granted Managed Identities. This enables secure communication with other services and access to resources, without the need to manage credentials or Kubernetes infrastructure. The result is a tightly secured, cloud-native environment that meets enterprise-grade compliance requirements by design.
Resilient
Resilience in a cloud-native system means anticipating failure and designing the system to recover automatically, without manual intervention. Cloud providers offer powerful tools to build self-healing architectures that detect, respond to, and recover from disruptions in real time.
Example
A video streaming platform built on AWS achieves resilience through a combination of automated scaling, distributed architecture, and fault injection testing:
- Amazon EC2 Auto Scaling Groups dynamically adjust capacity based on traffic demand
- AWS Fault Injection Simulator is used to proactively test failure scenarios and improve system robustness
- Amazon Route 53 enables global DNS-based failover, distributing user traffic across multiple regions
- Amazon Aurora Multi-AZ ensures high availability for databases with automatic failover to standby replicas
If an entire AWS region becomes unavailable, traffic is seamlessly rerouted to a healthy region, ensuring continuous playback for users with little to no disruption.
Manageable
Cloud-native systems are built with operations in mind. They embrace automation, observability, and streamlined deployment workflows. Enabling teams to deliver, monitor, and troubleshoot applications efficiently at scale. The goal is to minimize manual effort and maximize system insight.
Example
A SaaS company runs its platform on Azure, leveraging native tools to simplify operations and accelerate delivery:
- Azure DevOps Pipelines handle continuous integration and delivery with gated releases and rollback support
- Bicep templates manage infrastructure as code, ensuring repeatable and version-controlled provisioning
- Azure Monitor and Application Insights provide real-time visibility into system health and user behavior
- Azure Log Analytics consolidates logs from across services for centralized analysis and alerting
This cloud-native setup empowers developers to ship features confidently, detect issues early, and maintain high system reliability. All without managing complex infrastructure manually.
Sustainable
Sustainability in cloud-native systems goes beyond just uptime. It’s about building architectures that scale efficiently, avoid waste, and reduce operational and environmental costs. Cloud providers offer serverless and autoscaling options that help teams meet demand dynamically while optimizing resource usage.
Example
A fast-growing retail startup on Google Cloud embraces sustainability by designing for scale and efficiency from the start:
- Stateless backend services run on Cloud Run, scaling to zero when not in use
- Background processing is handled via Cloud Functions, triggered by events like new orders or inventory updates
- Data is stored in Cloud Firestore, a serverless NoSQL database that scales automatically
- Google Cloud Recommender and budget alerts help the team identify idle resources and control spending proactively
This architecture allows the team to grow without overprovisioning, lowering both infrastructure costs and their carbon footprint.
Observable
Observability is essential in cloud-native systems. It allows teams to understand what’s happening inside complex, distributed environments. By leveraging managed logging, tracing, and monitoring tools, engineers can detect issues early, trace failures, and ensure performance across services and regions.
Example
A logistics company operating on AWS ensures deep observability across its platform using a suite of native tools:
- Amazon CloudWatch captures metrics, logs, and custom alarms to monitor system behavior in real time
- AWS X-Ray provides distributed tracing to visualize service interactions and pinpoint latency bottlenecks
- AWS CloudTrail tracks API calls and infrastructure changes for security and compliance auditing
- CloudWatch Synthetics runs automated canary tests to simulate and monitor real user journeys
With this setup, the team can investigate issues end-to-end from API requests to backend processing. Reducing downtime and improving response times across their microservice architecture.
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.