How to build cloud-native applications: a strategic guide to microservices, serverless and containers

Adam, the CTO at a mature software company, has just completed a review of quarterly expenses. He was alarmed to see that cloud bills were 30% higher than projected, despite the fact that a year ago his team proudly a

ounced the completion of a major “to the cloud” migration project. Their flagship monolithic application, which had been running in the company’s server room for years, was moved by a lift-and-shift method to powerful virtual machines on AWS. But the expected benefits - flexibility, cost savings, improved reliability - did not come. Deployments were still slow and risky, the application did not scale dynamically, and costs proved higher than before. In a meeting with the architecture team, Adam came to a painful conclusion: “We didn’t move to the cloud. We just built a very expensive virtual version of our old, inefficient server room.” He realized that his company was not taking advantage of the true power of the cloud. They need to stop thinking of the cloud as a “place” and start thinking of it as a “way of building.” They need to become cloud-native.

Adam’s story is the story of thousands of organizations that have mistaken simple migration for real transformation. Cloud-native is not a marketing buzzword. It’s a fundamentally different paradigm for designing, building and running software to take full advantage of the potential offered by the cloud-native model. It’s an architectural philosophy for creating applications that are inherently flexible, scalable and resilient to failure. This article is a comprehensive guide for technology leaders, architects and engineers who want to move beyond simply “being in the cloud” and start consciously building applications for the cloud. We demystify the key pillars of the cloud-native approach - microservices, containers and serverless - and show how to combine them into a cohesive, future-proof strategy that is the foundation for any modern, digital organization.

What are the fundamental pillars and principles (according to CNCF) that define the cloud-native approach?

“Containers have become the standard unit of deployment, enabling consistent environments from development to production.”

— Docker, Docker Overview | Source

The cloud-native approach is not a chaotic collection of technologies, but a coherent philosophy based on several complementary pillars. Understanding them is the key to designing modern systems.

1 Microservices Architecture (Microservices): This is the backbone of cloud-native applications. Instead of building a single, large, monolithic application, the system is decomposed into a collection of small, independent and loosely coupled services. Each service performs one specific business function and can be developed, deployed and scaled independently of the others. As detailed in our **guide to microservices migration **, this architecture is the foundation for agility and resilience at scale.

2 Containers: This is the standard “packaging unit” for microservices. Each microservice is packaged, along with all its dependencies, in a lightweight, portable container (usually Docker). As we explained in our article on **CI/CD automation **, containers ensure that software runs the same in any environment - from a developer’s laptop to production - and enable its efficient management.

3 Orchestration & Dynamic Management: In a cloud-native world, where we may have hundreds or thousands of containers, we need an automated “conductor.” This role is filled by container orchestrators, with Kubernetes as the de facto standard. Kubernetes automates deployment, scaling, self-healing and network management for containerized applications, treating the entire infrastructure as an elastic pool of resources.

4 Automation & Declarative APIs (Automation & Declarative APIs): Cloud-native applications and infrastructure are managed in an automated maer through declarative APIs. Instead of issuing a series of imperative commands (“do this, then this, then that”), we define the desired end state in configuration files (e.g. “I want 3 replicas of my service to work”). The system (e.g. Kubernetes) itself makes sure that the reality matches this declaration. This is the basis for Infrastructure as Code (IaC) and GitOps practices.

5 Observability (Observability): In the distributed world of microservices, traditional monitoring is no longer sufficient. We need observability - the ability to ask arbitrary questions about the internal state of the system based on collected telemetry data (logs, metrics and traces). Observability is key to understanding and debugging complex cloud-native systems.

These five pillars, combined into a cohesive ecosystem, form the foundation on which to build applications capable of surviving and thriving in the dynamic, unpredictable and scalable world of the cloud.

Pillar 1: Why is microservices architecture the backbone of cloud-native applications?

Microservices architecture is a natural and almost necessary choice for cloud-native applications because its fundamental principles align perfectly with the nature of the cloud. The cloud is inherently distributed, elastic and designed for failure - and a well-designed microservices architecture is exactly the same.

1. enables granular scaling: In the cloud, we pay for resources consumed. A monolith that needs to be scaled in its entirety is not cost-effective. Microservices allow you to scale only the parts of the system that actually need it. If an e-commerce store grows traffic to a product page, we can dynamically add 10 more instances of the “catalog” service without touching the “payment” service, which has a low load at the moment. This leads to drastic cost optimization, which is at the heart of the FinOps philosophy.

2. increases resilience to failure (Resilience): The cloud is built with thousands of “unreliable” components. Failure of a single VM or disk is a normal, expected event. A monolithic application, in the event of such a failure, most often ceases to function in its entirety. In a microservices architecture, failure of a single service does not necessarily mean failure of the entire system. A well-designed system can degrade its functionality in an elegant way (graceful degradation). If a recommendation service stops working, the user simply won’t see personalized suggestions, but will still be able to browse and buy products. This is building fault-tolerant systems, which is a key principle of Site Reliability Engineering (SRE).

3 Enables independent, agile teams: As Conway’s Law says, the system architecture reflects the structure of the organization. Microservices allow the creation of small, autonomous product teams, each of which is fully responsible for its piece of the business. Such a team can develop, test and deploy its service independently of others, which is the basis for scaling development teams and achieving true agility in a large organization.

4. promotes technological polyglot: The cloud offers a huge variety of services and technologies. Microservices architecture allows you to take advantage of this diversity. Each service can be written in the technology best suited to its task. A service for real-time data processing can be written in Scala and use Kafka, while a simple CRUD service can be written in Python and use a DynamoDB database. This allows you to choose the optimal tool for your problem.

Microservices are not an end in themselves. They are an architectural tool that, when used correctly, unlocks the key promises of the cloud: flexibility, scalability, resiliency and cost-effectiveness.

Pillar 2: How do containers (Docker) and orchestration (Kubernetes) provide portability and scalability?

If microservices are an architectural philosophy, then containers (Docker) and orchestration (Kubernetes) are the technologies that make this philosophy practical and manageable at scale. They provide a universal “operating system” for cloud-native applications.

Docker: a standard “package” As we detailed in our **guide to CI/CD automation **, Docker solves the fundamental problem of portability and repeatability. By packaging each microservice in a standard, isolated container, we gain confidence that it will run identically in every environment.

For developers: No more “it works for me” problem.
For CI/CD: Pipeline builds a single, immutable artifact (container image) that is then promoted through all stages of testing to production.
For operations: You no longer need to worry about dependencies and configuration. All you need to do is run the container.

Kubernetes: a “conductor” at scale Ruing a few containers is easy. But how do you manage hundreds or thousands of containers that need to work together, scale and be resilient to failures? That’s where Kubernetes comes in.

Abstraction over infrastructure: Kubernetes creates a layer of abstraction over the physical or virtual infrastructure. The developer no longer has to think in terms of “server A” or “machine B.” He thinks in terms of a “cluster,” or pool of resources, and Kubernetes decides for itself where best to run a given container.
Declarative management: instead of telling Kubernetes “how” to do something, we tell it “what” we want to achieve. We define a desired state in YAML files (e.g., “I want 3 replicas of service X”), and Kubernetes continually works to maintain that state.
Automating key operational tasks: Kubernetes automates tasks that were extremely difficult and labor-intensive in the traditional world:
Self-healing: Automatically restarts containers that have failed.
Autoscaling: Automatically adds or removes containers in response to changing workloads.
Incremental Deployments (Rolling Updates): Allows you to safely deploy new versions of your application without downtime, gradually replacing old containers with new ones.

The combination of Docker and Kubernetes creates an extremely powerful platform that is ideally suited to the nature of microservices and cloud applications. It gives developers freedom and simplicity (Docker), while providing operations engineers (or SREs) with powerful tools to manage complexity at scale (Kubernetes). It is this duo that has become the de facto standard and technological heart of the cloud-native ecosystem.

Pillar 3: What is serverless and when is it a better alternative to containers?

Serverless is the next, even higher level of abstraction in the evolution of cloud computing. It’s a model in which the developer completely stops thinking about servers, virtual machines and even containers. He focuses solely on writing code (in the form of small, stateless functions), and all responsibility for running, scaling and managing the infrastructure is 100% transferred to the cloud provider.

The name “serverless” is, of course, a bit misleading - servers still exist somewhere. The point is that from a developer’s perspective they are completely invisible.

The most popular serverless implementation is the Functions as a Service (FaaS) model, offered by services such as AWS Lambda, Azure Functions and Google Cloud Functions.

How does FaaS work?

A developer writes a small function that performs one specific task (e.g., processes an image, handles an API request).
He uploads this feature to the cloud platform.
Configures a “trigger” (trigger) to trigger the function (e.g., a new HTTP request, the appearance of a new file in the S3 bucket, a message in the queue).
That’s it. From this point on, every time an event occurs, the cloud provider will automatically run the function, execute the code and shut it down. If 10,000 such events occur in one second, the platform will automatically run 10,000 parallel instances of this function.

When is Serverless a better alternative? Serverless and containers (Kubernetes) are not competitors, but complementary tools that work well in different scenarios.

For Event-Driven workloads: Serverless is ideal for event-driven architectures. Real-time data processing, asynchronous background tasks, simple API endpoints - all perfect use cases for FaaS.
For irregular and unpredictable loads: If your service has very irregular traffic - for example, it is used heavily for 5 minutes every hour and idle the rest of the time - serverless is much more cost-effective. You pay only for the actual time your code is executed (to the nearest millisecond), not for maintaining a constantly running server/container.
For maximum development speed (Time-to-Market): Serverless eliminates operational overhead almost entirely. Teams can focus 100% on business logic, allowing for rapid prototyping and deployment of simpler services.

When might containers (Kubernetes) be a better choice?

For constant, predictable loads: If you have a service that runs under a constant, high load 24/7, keeping it running in permanent containers is often cheaper than paying for millions of serverless feature calls.
For complex, long-running processes: Serverless functions have time limits (usually up to 15 minutes). For long-running computing tasks, containers are a better option.
To avoid “vendor lock-in.” Container-based applications are inherently more portable between different cloud providers than those that are deeply integrated into a particular vendor’s serverless ecosystem.

A mature cloud-native strategy often uses both models, choosing the right tool for the job.

How to design systems for resilience in a distributed cloud environment?

In the traditional on-premise world, the goal was to prevent failures at all costs by buying expensive, redundant hardware. In the cloud-native world, the philosophy is different: failures are inevitable, so we need to design systems that can survive them and automatically recover from them. This is the essence of resilience.

This approach requires building specific design patterns into the application architecture that deal with the unreliability of distributed systems.

1 “Circuit Breaker” pattern:

Problem: Service A calls service B. Service B crashes and stops responding. Service A, unaware of this, continues to try to call it, blocking its own resources (threads) while waiting for a response. Soon Service A runs out of resources and it too crashes. This is a cascading failure.
Solution: the Circuit Breaker pattern works like an electrical fuse. When a certain number of unsuccessful calls to service B are detected, the “circuit opens.” For the next specified period of time, all subsequent attempts to call service B are immediately rejected, with no attempt to connect, thus protecting service A’s resources. After a certain amount of time, the saver attempts one test call, and if successful, “closes the circuit.”

2 Timeouts and Retries:

Timeouts: Each network call must have a maximum timeout defined. This is a simple safeguard against waiting “forever” for a service that never responds.
Retries: In the case of transient network errors, intelligent, automatic query retries (preferably with a mechanism called “exponential backoff,” or exponentially increasing delay) can resolve the problem without affecting the user.

3 - Elegant Degradation (Graceful Degradation): A system should be designed so that if a less critical component fails, it can still provide its core functions. The aforementioned example of an e-commerce store that continues to operate despite the failure of a recommendation service is a perfect illustration of this principle.

4 Idempotency: operations, especially those that are repeated, should be idempotent. This means that repeatedly performing the same operation with the same input data produces the same result as performing it once. This is crucial for the safe use of repetition mechanisms.

5 Chaos Engineering: As we mentioned in the article on SRE, this is an advanced practice of proactive resiliency testing. It involves the deliberate, controlled injection of failures (e.g., shutting down random servers) into a production environment to verify that the system is behaving as expected and that the resilience mechanisms are working properly.

Designing for resilience is a fundamental mental shift. It’s accepting that in a distributed world, anything can fail at any time, and building that knowledge into the very DNA of our architecture.

How does the cloud-native approach affect costs and how does it fit into the FinOps philosophy?

One of the biggest promises of the cloud is cost optimization. However, as the story at the beginning of this article shows, simply moving applications to the cloud (“lift-and-shift”) often leads to an increase, not a decrease, in expenses. The cloud-native approach, if implemented correctly, is the key to unlocking the true cost-saving potential of the cloud and is inextricably linked to the FinOps philosophy.

1 Paying for value, not idleness: Cloud-native architectures, such as microservices and serverless, allow for extremely granular matching of resource consumption with real demand.

Scaling to zero: Many serverless and container components can be configured to scale to zero in the absence of traffic, at no cost.
Flexibility: Instead of maintaining a year-round infrastructure capable of handling the Black Friday peak, the cloud-native application scales up just for those few hours, and then returns to normal levels. This is a true realization of the pay-as-you-go promise.

2 Matching technology to cost: A polyglot technology-based architecture allows you to choose not only the best, but also the most cost-effective technology for a given task. A low-cost serverless function can be used for a simple ETL task, while a performance-optimized, more expensive instance can be chosen to support a critical database.

3 Visibility and cost allocation: A microservice architecture greatly simplifies the implementation of FinOps. Since each service is a separate, independent unit, it is much easier to accurately measure and allocate its cost to a specific team or product. This allows you to calculate “unit economics” and make informed ROI decisions for each part of the system.

4 Automation of optimization: The cloud-native approach relies on automation. FinOps practices, such as automatically shutting down unused environments or “rightsizing,” can be built directly into CI/CD pipelines and orchestration tools, becoming part of the automated application lifecycle.

New cost challenges: At the same time, cloud-native introduces new challenges:

The complexity of cost monitoring: In an environment consisting of hundreds of microservices and serverless functions, cost tracking becomes difficult without the right tools and a strict tagging policy.
Data and network costs: Data transfer between cloud services and availability zones can become a significant hidden cost. The architecture must be designed to minimize this traffic.

In summary, cloud-native provides powerful tools for cost optimization, but requires the implementation of a mature, data-driven FinOps culture to realize its full potential and avoid new pitfalls.

What are the key steps in an organization’s journey toward cloud-native maturity?

Cloud-native transformation is not a project that can be completed in one quarter. It is a long, evolutionary journey that involves technology, processes and culture.

Step 1: Build the foundation - culture and platform.

Start with culture: Before you start decomposing the monolith, start building a culture of DevOps, shared responsibility and psychological safety.
Invest in a platform: Start building the foundation of an internal development platform (IDP). Create a robust, automated CI/CD pipeline and start experimenting with containerization (Docker).

Step 2: Start the decomposition from the edge. Don’t start by rewriting the heart of your monolith. Apply the “strangler” patter **(Strangler Fig Patter **).

**Spi ** off your first simple service: Find one relatively independent functionality in your monolith and build it from scratch as your first microservice, running in a container on Kubernetes or as a serverless function.
Build an API Gateway: Implement an API gateway that will route traffic - some to the old monolith and some to the new service.
Learn and iterate: The first service is a testing ground. On it you will learn how to monitor, deploy and manage the new architecture.

Step 3: Accelerate and scale. When the foundation is solid and the team has gained experience, you can accelerate.

Continue iterative decomposition: Gradually, piece by piece, “push” more functionality from the monolith into new microservices.
Restructure teams: In parallel with architecture decomposition, restructure the organization by creating autonomous product teams, each responsible for its own set of microservices.
Implement SRE practices: As complexity increases, start implementing more formal reliability engineering practices such as SLOs and fault budgets.

Step 4: Optimize and innovate. At the highest level of maturity, the organization has a flexible, scalable platform that allows it to innovate quickly and safely.

Experiment with serverless: Use serverless architecture to build new event-driven functionality.
Leverage data: Use the wealth of data flowing from the system to optimize processes and personalize offerings.

It’s a long journey that requires patience, determination and strategic partnerships.

Comparison of cloud deployment models: from virtual machines to serverless

The following table compares the main cloud deployment models, showing the evolution toward higher and higher levels of abstraction.

Model	Implementation unit	Infrastructure management (your responsibility)	Cost model	Best use
Virtual Machines (IaaS)	Virtual Machine (VM)	Operating system, middleware, runtime environment, data, application.	Payment per hour/second of operation of the entire VM, regardless of its load.	Lift-and-shift migrations, legacy applications, complex custom configurations.
Containers as a Service (CaaS)	A container (e.g. Docker)	Data, application. (The platform manages the operating system and orchestration).	Payment for resources (CPU/memory) consumed by the cluster, often optimized by "bin-packing."	Microservices applications, portability between clouds, complex, long-running processes.
Platform as a Service (PaaS)	Applicatio	Only data and application code. (The platform manages everything else, from OS to scaling).	Platform-dependent, often based on consumed resources, but with less granularity.	Rapid development of standard web applications when you don't want to manage the infrastructure.
Functions as a Service (FaaS / Serverless).	Function (code snippet)	Only the application code. (The platform manages everything, including triggering and scaling from scratch).	Payment for the number of calls and execution time (to the nearest millisecond).	Event-driven architectures, simple APIs, asynchronous tasks, irregular traffic.

Need testing support? Check our Quality Assurance services.

Let’s discuss your project

Have questions or need support? Contact us – our experts are happy to help.

How does ARDURA Consulting’s partnership approach and expertise support building the infrastructure of the future?

At ARDURA Consulting, we understand that transitioning to a cloud-native architecture is one of the most fundamental and complex transformations an IT organization can undertake. It’s a journey that requires not only deep technical expertise, but also strategic vision and experience in leading the change. Our approach as a Trusted Advisor is holistic and supports our clients every step of the way.

1 Cloud-Native Strategy and Architecture: We help technology leaders develop a pragmatic strategy to transition to cloud-native. We don’t believe in “big bang” revolutions. We work with you to design an iterative roadmap, starting with a migration from the monolith and gradually building an architecture based on microservices, containers and serverless that is perfectly aligned with your business goals.

2 Platform Engineering and Construction: Our team of experienced cloud engineers, DevOps specialists and architects has hands-on, “trench” experience in building and managing modern platforms. We specialize in:

Design and deployment of scalable and secure Kubernetes clusters.
Building automated, mature CI/CD pipelines and implementing DevSecOps culture.
Design and implementation of advanced serverless architectures.

3 Cloud-Native Application Development: We do ’ t just build the infrastructure, we develop the software that runs on it. ARDURA Consulting specializes in developing software from the ground up, following cloud-native best practices, ensuring that your new applications are powerful, scalable and easy to maintain.

4 Access to Elite Competencies: We know how hard it is to find experts in Kubernetes, SRE or serverless architecture. In our flexible models, such as **Staff Augmentation **, we provide you with immediate access to world-class engineers who will not only accelerate your projects, but also enhance the competence of your internal team.

At ARDURA Consulting, we live and breathe cloud-native technology. It is not just another trend for us, but a fundamental way of building software. Our goal is to be the partner that helps you take full advantage of the power of the cloud and build the technological foundation for the future success of your business.

If you are ready to move from simply “being cloud-native” to truly “being cloud-native,” consult your project with us. Together we can build your infrastructure of the future.