What are the most common deployment strategies for cloud native applications?

Cloud-native applications are designed to take full advantage of cloud computing capabilities. It will enhance scalability, flexibility, and reliability. Common deployment strategies are: 1- IaaC 2- CI/CD 3- Containerization 4- Serverless computing 5- Microservices architecture 6- Multiple deployment methods like Blue/Green, Immutable, Canary, etc. 7- AutoScaling Choosing the right deployment strategy depends on the specific requirements and characteristics of your application

This article very briefly touched on monolithic applications. As per my experience people do consider these deployments for simplicity, rapid prototyping and for small teams with limited resources use cases. However, it's important to remember that these advantages are often outweighed by the drawbacks such as not scalable, not flexible, single point of failure, slow deployments, limited innovation when dealing with complex or large-scale applications.

Monolithic deployment, a time-tested method, packages the entire application into a single entity for cloud deployment. While simplicity reigns, drawbacks emerge. Resource inefficiency, sluggish updates, and scalability challenges loom. Despite its ease of implementation, this approach calls for a discerning balance between simplicity and the imperative need for resource optimization and agile scalability.

In monolithic deployments, the business logic and coding itself is a bit more complex than in a microservice deployment. But the deployment itself is fairly straightforward. In the end, the complexity is either in your code (monolithic approach) or in your pipeline and deployment process (microservice approach). In addition, it might be harder to deploy your code multiple times a day, and there is a high probability that you are wasting resources and money.

Microservices deployment is better than Monolithic deployment and also it offers significant benefits for scalability, agility, and maintainability. which makes it a popular choice for modern applications. However, it comes with increased complexity and overhead, which needs to be carefully weighed against the potential advantages.

Microservices deployment, a departure from monolithic tradition, dissects applications into nimble, autonomous services. Each service, encapsulating unique functionality, data, and communication protocols, stands alone in deployment—granting flexibility and scalability. Yet, with this liberation comes complexity. Coordinated efforts in monitoring, testing, and addressing network challenges become imperative. Navigating this terrain demands a strategic balance between the benefits of agility and the intricacies of managing a decentralized, interconnected ecosystem.

If your team has the right mindset for fully automated microservices, you can benefit greatly from this deployment method. But if you're not sure why you need it or don't have experience with microservices, it can be difficult. My approach in most cases is to start with a monolithic architecture. Build a proof of concept and show initial business value. When you have enough stakeholders, go for a microservice architecture.

Serverless deployment, a hybrid paradigm, dissects applications into functions—small, stateless code snippets triggered by events. Cloud providers handle infrastructure, scaling, and billing, slashing operational overhead. Yet, this convenience comes with caveats. Cold starts, potential vendor lock-in, and diminished application control pose challenges. Striking a balance between operational ease and retaining visibility is pivotal, as serverless paradigms redefine the landscape of deployment with both opportunities and considerations.

I see great potential in serverless. Because I only have to take care of the domain logic and not develop infrastructure code and the like. The serverless approach can help me to test my application three times faster, which is particularly important for PoCs or when the budget is very tight. In addition, the advantage of serverless is that deployment is possible without much configuration effort. In azure, cold starts can be bypassed by the permanent provision of at least one instance.

Blue-green deployment, a downtime-averse strategy, orchestrates seamless application updates. By maintaining identical active (blue) and inactive (green) environments, it enables risk-free version deployment. The traffic switch ensures minimal disruption, allowing swift rollback in case of issues. This meticulous approach enhances application reliability and quality, mitigating the impact of potential pitfalls. However, the benefits come with resource demands and coordination complexities, underscoring the need for strategic planning and resource allocation.

Canary deployment, a downtime-averse methodology, introduces a new application version gradually to a subset of users or servers. Monitoring performance and feedback in real-time allows for informed decisions. If successful, the new version expands; if issues arise, a rollback ensues, containing potential impact. This meticulous approach minimizes user disruptions, fostering enhanced user experience and bolstering application confidence. However, the benefits come with added complexity and tooling demands, emphasizing the need for robust monitoring and deployment frameworks.

Common deployment strategies for cloud-native applications encompass Blue-Green Deployment, ensuring seamless updates with two identical environments, and Canary Deployment, gradually releasing new versions to a subset for real-time feedback. Serverless Deployment leverages functions for streamlined operations, while Microservices Deployment breaks applications into agile, independent services. Each strategy presents a unique balance of advantages and challenges, offering cloud engineers a diversified toolkit for optimizing application deployment in dynamic cloud environments.