Comparing Architectures: Lambda, Monoliths, and Microservices

In the world of software architecture, three models dominate discussions: Serverless with Lambda functions, Monolithic applications, and Microservices. Each one addresses scalability, complexity, and operational challenges differently. Choosing the right one depends on your project’s size, growth expectations, and team structure.

Lambda (Serverless / Function as a Service)

Lambda (or any serverless function) represents an event-driven execution model. Instead of running a long-lived server, you write small functions that execute only when triggered by an event such as an HTTP request, a message in a queue, or a scheduled job. The cloud provider handles provisioning, scaling, and tearing down of resources automatically.

This model is especially powerful for unpredictable workloads where you may go from zero to thousands of requests instantly. Instead of paying for idle servers, you only pay for the execution time of each function.

• Advantages:
  • Elastic scaling without configuration—functions spin up on demand.
  • Pay-per-use billing reduces costs when traffic is variable.
  • No need to manage servers, operating systems, or load balancers.
  • Accelerates prototyping and event-driven designs.
• Disadvantages:
  • Cold starts introduce latency after periods of inactivity.
  • Not advisable for code that is triggered by users repetitively because it can unnecessarily increase costs.
  • Execution limits (time, memory, package size) restrict heavy workloads.
  • Strong vendor lock-in, as each provider has unique APIs.
  • Harder to debug and monitor in distributed, function-based systems.

+--------------------------+
|        Event Source      |
| (HTTP, Queue, Cron Job)  |
+-----------+--------------+
            |
            v
+-----------+-----------+
|     Lambda Function   |
|  (Executes on Demand) |
+-----------+-----------+
            |
         Database / API

Monolithic Architecture

A monolith integrates all components—user interface, business logic, and data access—into a single deployable application. Traditionally, this is run as a single instance or scaled horizontally by running identical copies behind a load balancer. The load balancer ensures traffic is distributed evenly among instances.

This model is often the starting point for many companies due to its simplicity. However, as the application grows, scaling and team collaboration can become bottlenecks.

• Advantages:
  • Simpler to design, develop, and debug compared to distributed systems.
  • Unified deployment pipeline—deploy once, everything updates together.
  • Effective for small teams and projects in early stages.
  • Operationally lightweight, requires fewer tools and infrastructure.
• Disadvantages:
  • Scaling duplicates the entire system, even if only one module needs resources.
  • One faulty component can affect the entire application.
  • Large deployments slow down release cycles.
  • Harder to maintain in large teams due to tight coupling.

+----------------------------+
|        Load Balancer       |
+-------------+--------------+
              |
    +---------+---------+
    |  Monolithic App   |
    |  (UI + Logic +    |
    |   Data Access)    |
    +---------+---------+
              |
           Database

Microservices Architecture

Microservices break down an application into independent, loosely coupled services. Each service owns a distinct domain (for example: users, orders, payments) and can be developed, deployed, and scaled independently. Services usually expose APIs for communication.

Because of the distributed nature of microservices, orchestration tools like Kubernetes are essential. Kubernetes manages containerized services, provides networking, auto-healing, service discovery, and rolling updates. This infrastructure makes microservices feasible at scale but introduces operational overhead.

• Advantages:
  • Granular scaling—scale only the services under heavy load.
  • Improved resilience—failure in one service does not bring down the whole system.
  • Enables independent deployments for faster iteration.
  • Flexibility to use different technologies for different services.
• Disadvantages:
  • Significant complexity in deployment, networking, and monitoring.
  • Higher infrastructure costs due to multiple services running in parallel.
  • Debugging distributed failures is harder.
  • Requires strong DevOps practices and automation.

+--------------------------+
|       Kubernetes         |
|   (Orchestration Layer)  |
+-----------+--------------+
            |
    +-------+-------+
    |   Service A   |
    +---------------+
    |   Service B   |
    +---------------+
    |   Service C   |
    +---------------+
        ... etc ...

Microservices and Kubernetes

Each microservice usually runs in its own container. If you have 4 microservices in your backend, each one will have its own container image, and Kubernetes can scale them independently based on demand.

Kubernetes runs on a cluster of nodes, not necessarily a single server. It can run on a single machine for testing, but in production, it typically spans multiple servers for scalability and high availability.

• Each microservice = its own container (or Pod)
• Kubernetes manages replicas and scaling per service
• Pods can be distributed across multiple servers (nodes)
• Single-node Kubernetes is possible, but multi-node clusters are the norm

Visual Comparison between Microservices and Monolith architecture

Conclusion

Lambda: Ideal for lightweight, unpredictable workloads where automatic scaling and cost efficiency are critical.

Monolith: Best for smaller teams and early-stage products where speed and simplicity matter most, even if scaling is coarse.

Microservices: Perfect for large, complex applications with multiple teams, where resilience and granular scalability outweigh operational overhead.