Cloud native has become a buzzword modern businesses pursue in their modernization journey. The global cloud-native market has generated $5.3 billion in 2022 and is estimated to reach $48.8 billion by 2032, growing at 25.2% annually. Such exponential growth is driven by the scalability, flexibility, and cost-effectiveness cloud-native architecture can offer.
Although the cloud is central to the deployment of most applications today, not all use it the same way. In this article, we’ll highlight what sets cloud native apart, delve into its fundamental principles and challenges, and explore how embracing it can optimize your cloud expenses.
But let’s start by outlining what it means to be cloud native.
What is Cloud Native?
Cloud native is an approach to building, deploying, and running software applications that use cloud services to store, scale, and manage data and resources. It involves designing applications from the ground up to fully leverage cloud infrastructure and services. As a result, cloud-native applications are highly resilient, scalable, and flexible, as they can automatically adapt to load and infrastructure changes.
The Cloud Native Computing Foundation (CNCF), an open-source organization without vendor bias, aims to promote and support the adoption of this paradigm. It does so by nurturing an ecosystem of open-source projects, such as Kubernetes, Prometheus, Envoy, and many more. Yet, recent studies show that only 30% of companies worldwide have adopted a cloud-native approach for nearly all their development and deployment activities.
So what about the rest?
To better understand how cloud native stands out, let’s compare it to the most distinct cloud architecture patterns companies use today — cloud-compatible, cloud-enabled, and cloud-ready.
This pattern refers to applications initially designed for on-premises infrastructure, which can be migrated and run in the cloud with minimal or no modifications. They often rely on virtual machines (VMs) or IaaS (Infrastructure as a Service) for deployment.
Cloud-compatible applications may only partially use cloud platform features or managed services. This makes them less flexible than cloud-ready or cloud-native solutions.
This pattern refers to legacy applications modified or adapted to function in the cloud. Since such applications often retain monolithic structures and weren’t designed with cloud-native principles, they lack flexibility and can be more challenging to manage in dynamic cloud environments.
Very often, the underlying architecture remains unchanged, preventing them from fully harnessing the benefits of the cloud.
Cloud-ready (cloud-optimized) architecture
This architectural model covers applications specifically designed or refactored to work efficiently in a cloud environment. Such applications aim to be more cost-effective and scalable than their cloud-enabled and cloud-compatible counterparts.
Cloud-ready apps may involve rearchitecting components to be stateless, using microservices, and adopting modern DevOps practices. They also often embrace cloud features like auto-scaling, load balancing, managed databases, and PaaS (Platform as a Service) offerings. While this architectural model takes up many cloud-native techniques, it still has some legacy elements.
The key distinction between these terms lies in the degree to which applications are designed or adapted to work in the cloud, with cloud native being the most and cloud compatible being the least optimized for cloud infrastructure.
All of them are cloud-based applications but differ in development practices, performance, and their inherent level of integration with cloud technologies.
Key technology blocks of cloud-native architecture
CNCF lists immutable infrastructure, microservices, containers, declarative APIs, and service meshes as the technological blocks of cloud-native architecture.
Let’s discover how each caters to building flexible and resilient cloud applications.
In traditional on-premises models, servers undergo updates and modifications over time, resulting in configuration drift and potential inconsistencies. The power of cloud infrastructure lies in blazing-fast resource allocation. This opens up opportunities for the paradigm of immutable infrastructure, where there is no need to wait several minutes for updates and patching (which would cause the resource to be unavailable).
Instead, it’s more efficient to generate a new patched instance and instantly swap the old one for the new one. That’s why an immutable infrastructure promotes the establishment of servers that remain unchanged after deployment. If adjustments are necessary — a new version is deployed. This eliminates manual upgrades, guaranteeing consistency, reliability, and predictability.
Microservices are a collection of small, loosely coupled, independently deployable services that function collaboratively as cloud-native software.
Each microservice performs a specific business function, allowing for independent development, deployment, and scaling. Such modularity lets them adapt to varying demand levels and use precisely the resources they require, minimizing waste. This, in turn, enhances flexibility, accelerates development cycles, and facilitates continuous delivery. So, even if one microservice fails, the application remains up and running.
On the flip side, the number of microservices depends on the project size and can range from a few to hundreds. This may complicate inter-service communication, especially for complex business operations. The development team must strike a balance between the size and quantity of services to build an optimal architecture.
Application Programming Interface (API)
APIs are the glue that binds different microservices. It allows them to communicate and exchange information despite any variations in their underlying technologies. An API communicates what data a microservice requires and the outcomes it can deliver using various protocols.
The most popular and widely used protocol is REST API, although the new gRPC protocol is gaining popularity today. gRPC is known for its high-performance capabilities and support for multiple programming languages. This fosters its adoption, especially when low-latency communication and efficient data serialization are critical. Despite this, REST API remains a robust and versatile choice for many applications due to its simplicity and widespread support in the development community.
A service mesh is a dedicated infrastructure layer that handles communication between multiple microservices. It provides essential functionalities such as load balancing, service discovery, and security, offloading these capabilities from application-level libraries. With a service mesh, developers can focus on building business logic while the underlying infrastructure takes care of the complexities of communication and coordination.
For Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS) offerings, a service mesh is hidden deep within with no direct access to it as a standalone component. Instead, there are certain parameters through which you can configure specific safety and load-balancing properties to make a cloud-native setup more secure and scalable.
Containers are virtual packages for microservices and the smallest computing units of a cloud-native application. They bundle the microservice code and its necessary dependencies (e.g., resource files, libraries, and scripts) in cloud-native systems.
Сloud providers offer different Container-as-a-Service (CaaS) resources, each serving particular use cases. The 2022 CNCF Annual Survey showed that container adoption outpaces the maturity of cloud-native implementation. 62% of companies with less developed cloud-native techniques already use containers for pilot projects or limited production cases. What makes them so appealing?
The primary reason is that containerizing microservices lets cloud-native apps operate independently of the underlying operating system and hardware. Furthermore, containers ensure consistency in the application’s behavior, simplify dependency management, and enhance software portability across different platforms and environments.
Together, these five technology blocks empower companies to develop software that surpasses traditionally built apps in terms of scalability, reliability, and flexibility. Cloud-native architecture also facilitates the implementation of agile DevOps techniques. This synergy results in faster release cycles, improved deployment frequency, and enhanced change responsiveness.
Now that we’ve covered what cloud-native applications have under the hood, let’s explore how these foundational elements lay the groundwork for the main principles of cloud-native architecture.
8 Principles of Cloud-Native Development
Building cloud-native applications involves holistic adherence to eight major principles. These principles serve as guiding pillars for architects and developers, helping them build applications that are not merely hosted in the cloud but can fully harness its potential.
Let’s take a closer look at each of them.
This principle emphasizes breaking down applications into smaller, modular services that can operate independently and perform specific business functions. While microservices architecture is a prominent manifestation of service-oriented design, the flexibility inherent in this approach extends far beyond it.
For instance, service-oriented design can be seamlessly integrated into serverless computing models. There, it enables the creation of individual functions or serverless components that encapsulate specific business logic. Furthermore, its ability to encapsulate functionality into independently deployable services facilitates the integration of on-premises legacy systems with modern cloud services. This allows for a gradual migration towards a more distributed architecture.
This principle ensures that cloud-native apps can efficiently handle varying workloads, optimize resource utilization, and maintain performance under changing conditions. Auto-scaling mechanisms make it possible by adjusting the resources given to an application based on set criteria like CPU usage, network traffic, or other performance metrics.
Elasticity advocates designing the system to support horizontal scaling, allowing the dynamic addition or removal of instances in response to changes in demand. This can involve designing stateless services, using load-balancing mechanisms, and implementing distributed data storage.
All of it makes the elasticity principle a pivotal point in cloud cost optimization, as it allows you to use resources only for the current workload and pay for precisely what you require. This, in turn, helps to avoid unnecessary resource idling.
This principle involves collecting, analyzing, and visualizing data from software to gain insights into its performance, health, and behavior. Building a comprehensive observability system relies on three main components:
- Metrics — give you insights into key performance indicators, throughput, and resource utilization
- Tracing — allows you to track the flow of requests across your services, identify bottlenecks, and understand dependencies
- Logging — captures detailed events and errors, providing a timeline of activities for effective debugging and post-incident analysis
In practice, however, cloud providers offer robust logging tools, enabling efficient handling of hundreds of gigabytes of logs. Adding a correlation ID to the logs erases the fundamental distinction between logs and tracing, facilitating the identification and troubleshooting of issues across the entire application stack.
Beyond the traditional pillars of observability, alerting is a widely utilized aspect in fostering cloud observability. It covers services that notify stakeholders through various channels about exceeding specific parameters in metrics or the presence of logs with specific attributes (e.g., log levels fatal or even error).
Resilience is the core principle of cloud-native app development. It focuses on the system’s ability to withstand and recover from failures, disruptions, or unexpected changes.
Resilient architecture is built upon the following strategies:
- Fault tolerance. It involves designing and implementing mechanisms to detect and mitigate failures, preventing them from causing widespread outages.
- Auto-healing. The system can identify issues, initiate corrective actions, and restore regular operation without manual intervention.
- Redundancy. It involves having backup systems or components to ensure recovery in case of failure.
- Graceful degradation. It occurs when the system adjusts its functionality or performance during adverse conditions. Instead of a complete outage, the system continues to operate with reduced capabilities.
Great news: cloud providers can handle many failures automatically. So when an instance fails, the cloud promptly creates a new one, which will manage the failed request (if you’ve implemented a retry policy correctly).
Recovery mechanisms can be more involved in more intricate scenarios, such as complex distributed transactions and lasting outages. However, cloud infrastructure and a well-designed architecture minimize the likelihood of such problems, making their handling relatively straightforward. As a result, in practice, cloud developers don’t need to build their own resilience mechanisms and worry about them daily.
Since cloud-native applications are distributed across dozens or even hundreds of components, automation is a vital principle allowing effective system management. Embracing automation also brings additional benefits, like allowing cloud-native applications to adapt to dynamic demands and efficiently reduce manual intervention. Your dedicated development team can automate a long list of processes and activities in cloud computing, including server auto-provisioning, data backups, or discovery and elimination of idle processes.
Automation also ensures consistent, rapid, and error-free deployment and management of apps through:
- Infrastructure as Code (IaC)
- Continuous Integration and Continuous Deployment (CI/CD) pipelines
- Auto-scaling and configuration management
It extends to container orchestration, where tools like Kubernetes automate the coordination of containerized applications, simplifying their deployment and scaling. Additionally, automation is pivotal in monitoring, security, and backup processes, enabling proactive responses to issues.
However, cloud developers must ensure the automation process is controllable and its impact on the system is predictable. Thus, if any exceptions do occur, the scope of the error’s influence can be managed and deemed acceptable.
Traditional security methods involve creating a protective barrier around your infrastructure and monitoring and blocking external threats. However, this perimeter dissolves when you transition to cloud-native technologies like serverless. That’s when the Zero Trust security model comes in.
Zero Trust security assumes that no entity, whether internal or external, should be trusted by default. It supports the following principles:
- Always verify
- Contextualize request
- Secure admin environment
- Grant least privilege
The last ensures that entities have the minimum level of access required to perform their tasks. This minimizes the attack surface and limits the potential damage a compromised entity can cause.
Micro-segmentation is a key strategy in zero trust. It involves dividing the network into small, isolated segments to limit lateral movement in case of a security breach. Each segment is treated as an independent zone with specific access controls. This helps to reduce the potential impact of a compromised entity.
Zero Trust advocates for the use of encryption, multi-factor authentication, and authorization at every layer. This approach helps protect sensitive data and resources, especially in a dynamic and distributed cloud environment.
In a stateless system, each request from a client to a server is treated as an independent and complete transaction. So, the server retains no information about the session state or user data between consecutive requests. This independence simplifies the design and scalability of applications, as servers can efficiently distribute requests across multiple instances without needing to synchronize state.
Stateless processing enhances scalability, as servers can be added or removed from the environment without affecting the overall system. It also simplifies application design and maintenance. Since each request is self-contained, developers can focus on building independent and modular components. The absence of a shared state also makes the system’s behavior more predictable, leading to easier debugging and troubleshooting.
Continuous evolution of architecture
This principle emphasizes the iterative and adaptive nature of designing and refining the architecture of cloud-native applications. It involves continuously assessing, enhancing, and incorporating new technologies, methodologies, and practices.
For instance, existing products offered by cloud providers continuously evolve, expanding their range of features. So, at some point, it may become worth replacing a custom part of the infrastructure with a new feature of a cloud-native service. This will allow you to improve productivity, keep pace with evolving business needs, and reduce costs.
These eight principles make cloud-native architecture beneficial, especially in the area of cost optimization. So, let’s dive deep to see how it does it.
How Cloud-Native Architecture Helps Optimize Costs
Shifting to cloud-native architecture can significantly cut your company’s infrastructure, operational, and development expenses. Despite initial investments and adjustments, the long-term benefits of adopting cloud-native architecture can quickly pay off.
Let’s dive deeper into each aspect.
- Granular resource allocation. Cloud-native applications let you scale up or down each microservice independently. This granularity enables you to allocate resources precisely where needed, reducing the risk of overprovisioning.
- Serverless computing. Serverless platforms, like Azure Functions or AWS Lambda, let you run code in response to events without the need to provision and manage servers. Deloitte states that serverless applications can reduce development costs by 68%, especially for low-environment activities or event-driven workloads. However, you should remember that Function as a Service (FaaS) components are designed to handle isolated long-running workloads, making them not the most optimal approach to all cases.
- Efficient resource usage. Cloud-native features like load balancing, resource pooling, and dynamic allocation can ensure optimal resource usage, which reduces waste and improves cost efficiency.
- Reduction of operational tasks. Cloud providers offer various managed services for databases, storage, analytics, and more. These services let you offload operational tasks and reduce the costs of managing and maintaining infrastructure.
- Automation. Automated resource deployment, scaling, management, and termination reduce the need for manual intervention and minimize the risk of human error. This, in turn, saves time and helps to optimize cloud costs by avoiding overprovisioning during periods of low demand.
While cloud native offers significant cost-cutting opportunities, but it’s also crucial to grasp and address the complexities of this transformative approach.
Challenges of Adopting Cloud-Native Architecture
Gartner predicts that by 2025, over 85% of organizations will adopt a cloud-first approach. They also emphasize that the complete execution of digital strategies will heavily rely on integrating cloud-native architectures and technologies. That’s why it’s essential to understand the roadblocks that may hinder cloud-native adoption.
Absence of a cloud strategy
In its latest State of Cloud report, Pluralsight states that 70% of organizations need help leveraging cloud computing strategically for operational advantages. Even with massive investments in the cloud, the absence of a defined cloud strategy can hinder your company’s ability to accelerate business outcomes and outperform competitors.
Improper choice of technology
The cloud-native stack covers various technology categories that aim to overcome the limitations of traditional IT operating models. The same State of Cloud report finds that organizations face minimal challenges in adopting new cloud products and services, with 44% swiftly embracing the latest offerings upon release. The biggest struggle lies in choosing the right services to foster business operations. Otherwise, they will only drain your cloud budget.
Ensuring security and сompliance
Security considerations are paramount in cloud-native architectures. Yet in 2022, Pluralsight highlighted cloud security as the foremost challenge for leaders venturing into cloud initiatives. In 2023, the challenge remains at the forefront, with less than 50% of leaders having comprehensive security policies and practices in place.
The major roadblocks are the lack of security models required for cloud-based applications and the shortage of professionals with expertise in cloud security practices.
Studies show that over 65% of companies operate in multi-cloud environments, and 20% actively seek to add an additional cloud platform. However, engaging in multi-cloud scenarios can be risky, given economic uncertainty, layoffs, and undefined cloud strategies. And if a company doesn’t have experienced cloud developers familiar with handling multiple cloud providers simultaneously, going for a multicloud approach could compromise your software’s effectiveness.
According to the Stonebrunch 2023 Global State of IT Automation report, cost management and control remain top cloud concerns. This challenge often stems from the dynamic nature of cloud environments, where the consumption-based pricing model can lead to unpredictable costs. The variety of pricing models, overprovisioning, underused resources, and lack of visibility into cloud spending also contribute to the complexity of managing and controlling expenses.
As businesses transition to cloud-native environments, the demand for cloud experts grows at a rate of knots. However, a lack of talent can impede this transition. The 2023 Tech Forecast report shows that the number of tech leaders who want to launch cloud development projects (75%) outperform developers with extensive cloud experience (8%).
Furthermore, cloud-native technologies are evolving rapidly. Finding talent to adapt to these changes and keep up with the latest trends is essential for building resilient cloud systems.
While hiring experienced cloud developers in-house may be daunting, outsourcing software development to companies with comprehensive hands-on experience in the field can help you overcome most of these challenges.
Cloud native is one of the latest fundamental shifts in software development. It has changed the way of building, shipping, and managing cloud software to gain maximum efficiency, speed, and scalability. An immutable infrastructure in its core makes cloud-native applications more resilient to adverse events. With microservices, API, and containers, cloud native successfully outperforms its counterparts in terms of operational speed and cost efficiency.
The major challenge, however, is choosing cloud-native technologies correctly to gain the maximum value and cost savings. Leobit, an experienced software development company with offices in the US, Ukraine, Poland, and Estonia, can help you with that. We can cover the entire spectrum of cloud-native development needs — from expert guidance and consulting, cost and resource optimization, to migration and cloud-native solution development. Contact us, and we’ll gladly guide you through your cloud transformation.