This week’s issue brings you:

• Breaking Down Stateful vs Stateless Design

• Core Principles of Solution Architecture Design (Recap)

• How AI Speeds Up the Software Development Cycle (Recap)

• 6 Types of API Testing Explained (Recap)

READ TIME: 5 MINUTES

Breaking Down Stateful vs Stateless Design

Stateless design is a powerful model that has led to the development of simple yet highly scalable and efficient applications.

“State” refers to stored information that systems use to process requests. This information can change over time as users interact with the application or as system events occur.

Before we dive into the specifics of stateless design, let’s take a look at the alternative—stateful applications.

What are Stateful Applications?

With stateful applications, client data such as user ID, session information, configurations, and preferences are stored to help process requests for a given user.

Depending on the functionality and requirements of the application, additional data may be saved, such as shopping cart information for an online store or transaction history for a FinTech service.

A stateful design allows applications to provide a personalized experience to their users while removing the need to share data across multiple requests.

For this reason, it is a popular approach for applications with user preferences such as streaming services and online games.

The Birth of Stateless Design

As applications grew in complexity and received increasing amounts of traffic, the limitations of stateful design became apparent.

Managing and maintaining session data adds significant performance overhead and complexity to a system.

This complexity made it difficult for systems to scale horizontally as sharing state across multiple instances becomes a challenge.

The rapid need for scalability and efficiency drove the popularity of stateless design.

Instead of having mechanisms for state management, requests contained all the information needed to process it.

This allowed systems to handle high levels of requests while adding flexibility to how the system scales, making it more resource efficient.

Use Cases for Stateless Design

Stateless design has risen in popularity due to its alignment with trends in modern computing such as serverless architecture and microservices.

One of the key principles behind microservices is that each service is stateless. This allows microservices to scale independently and ensures resource consumption stays efficient.

Serverless computing follows the same concept — each function is invoked independently.

Even applications that require session management can benefit from implementing a stateless design in components of their system.

For example, most RESTful APIs are stateless where each API call contains all necessary information.

Content delivery networks (CDNs) also follow a stateless design so that every request can be fulfilled by any server in the network without needing to sync session data between all servers or query a single session management store.

Disadvantages of Stateless Design

The size of requests can be considerably larger in stateless design.

Moreover, sending data across multiple requests can introduce significant inefficiencies that are far greater than the alternative of managing and querying this data from a central storage system.

It is important to note that stateless design should only be implemented for use cases that are truly stateless.

Although stateful design has its share of disadvantages, workarounds can add more complexity and fragility.

Final Thoughts

Most applications pick a hybrid approach between stateful and stateless design, depending on the needs and constraints of each component.

The key to a well-designed system is balance.

It should be scalable, simple, and fast without sacrificing functionality.

Core Principles of Solution Architecture Design (Recap)

Great architecture isn’t just about solving today’s problems—it’s about preparing for growth and change. Here are 7 foundational principles with actionable best practices to guide your designs:

1) Scalable – Design systems to handle increased load via horizontal scaling, auto-scaling, and distributed state management.

2) Highly available & resilient – Ensure uptime and fast recovery using failover strategies, redundancy, and data synchronization.

3) Performant – Optimize for low latency and high throughput using async processing, caching, and monitoring worst-case latencies (e.g., p99).

4) Secure – Bake in security from the start: use encryption, RBAC, OAuth2/JWT, and consider Zero Trust Architecture.

5) Loosely coupled – Enable flexibility and fault tolerance with modular design and event-driven messaging systems like Kafka or RabbitMQ.

6) Extensible – Support future growth by following the Open-Closed Principle and building backward-compatible APIs.

7) Reusable – Boost development speed with composable components, shared libraries, and domain-driven design (DDD).

Applying these principles leads to systems that are scalable, maintainable, and ready for whatever comes next.

Check out the full post here for an expanded explanation of each.

How AI Speeds Up the Software Development Cycle (Recap)

AI isn’t replacing engineers—instead it’s making us faster.

AI coding tools are making developers faster, especially during the coding stage. But most tools stop there.

GitLab Duo goes further—bringing AI to every stage of the SDLC, from design to deployment:

• System Design & Architecture: AI suggests improvements and flags vulnerabilities early.

• Code Development: Real-time autocomplete, refactoring tips, and code explanations.

• Testing & QA: Auto-generates tests and identifies security risks.

• Deployment & Release: Enhances CI/CD with smart summaries and troubleshooting.

• Security & Compliance: Self-hosted option ensures enterprise-grade control over data.

By integrating AI across the full development lifecycle, GitLab Duo helps teams ship faster, safer, and smarter.

Check out the full post here.

6 Types of API Testing Explained (Recap)

API testing is key to building reliable, secure, and high-performing applications. Here are six essential types:

1) Workflow Testing – Verifies that multiple API calls work together to complete real-world processes, like checkout flows.

2)Performance Testing – Measures speed, responsiveness, and stability under load to uncover bottlenecks.

3) Security Testing – Detects vulnerabilities to prevent unauthorized access and data breaches (e.g., using OWASP guidelines).

4) Data-Driven Testing – Tests the API with various input/output combinations to ensure consistent behavior across scenarios.

5) Endpoint Testing – Ensures individual endpoints return correct data, status codes, and errors.

6) Contract Testing – Confirms that API interactions follow agreed-upon request/response formats and avoid breaking changes.

See the full post for a more detailed explanation of each.

That wraps up this week’s issue of Level Up Coding’s newsletter!

Join us again next week where we’ll explore and visually distill more important engineering concepts.