Why Not Nano Services (Lambdas)? The Cost of Fragmentation in System Architecture

When working with a FinTech client, I encountered a product that had been designed with the goal of speed and efficiency—but in reality, had become an operational nightmare. In an effort to optimize processing, they had broken their system down into hundreds of AWS Lambdas, believing that smaller meant faster.

At first glance, this seemed like the ideal solution. Serverless computing, infinite scalability, and rapid execution.But as the system scaled, so did its complexity, costs, and inefficiencies.

Disassembled plaster casts of hands and forearms, showcasing intricate detailing for potential nano services applications.

When Too Many Lambdas Become a Problem

Imagine yourself divided into hundreds of disconnected pieces—your mouth separated from your body, your legs functioning in isolation. While each part has a role, the constant need to reconnect everything makes even the simplest tasks exhausting.

This was exactly what happened to my client’s system:

• Operational Overhead – Deployments, updates, and debugging became chaotic, requiring repetitive changes across hundreds of functions.

• Developer Fatigue – A single update meant modifying hundreds of files, slowing down development cycles.

• DevOps Complexity – Managing hundreds of individual deployments across scattered functions resulted in visibility and monitoring issues.

• Increased Costs – While Lambdas promised cost efficiency, the reality was higher execution costs and maintenance overhead.

• Platform Lock-in – AWS Lambdas worked well—until the client wanted to expand beyond AWS, which required rewriting major parts of the system.

From 300+ Lambdas to 2 Powerful Microservices

To solve this, we restructured the product into a loosely coupled, microservices-based architecture, eliminating inefficiencies while keeping the flexibility of modular design.

• Identity & Structure – Instead of fragmented functions, the system became a well-defined, structured microservices ecosystem.

• Simplified Deployments – Updates were now centralized, reducing redundant work across multiple components.

• Cross-Platform Flexibility – Without dependency on AWS-specific services, the system could now run on any cloud platform.

• Optimized Performance – Processing time improved significantly, while keeping resilience and fault tolerance in check.

• Lower Costs – The client reduced infrastructure and maintenance expenses while improving system scalability.

The Takeaway: Smaller Isn’t Always Smarter in System Architecture

While serverless architectures and Lambdas have their place, overusing them creates fragmentation, complexity, and platform dependence. Instead, structuring systems around loosely coupled, well-designed microservices allows for scalability, maintainability, and long-term flexibility.

Thinking about migrating your architecture or optimizing your system? Let’s talk.