The Case for Large Monoliths in a World Obsessed with Microservices
For the past decade, the software engineering world has been captivated by a single architectural narrative: microservices are the future, and monoliths are a relic of the past. Driven by the success stories of tech giants like Netflix, Amazon, and Google, thousands of companies rushed to decompose their applications into hundreds of tiny, independent services. The promise was clear: infinite scalability, independent deployments, and technological flexibility.
However, as the “microservices-first” honeymoon phase ends, many organizations are waking up to a harsh reality. They have traded a manageable “spaghetti code” monolith for a “distributed spaghetti” nightmare of network latency, complex debugging, and massive operational overhead. Today, a growing movement of senior architects and developers is advocating for a return to the “Majestic Monolith.” This article explores why the monolith is not only viable but often superior for the vast majority of businesses today.
The Hidden Tax of Microservices
Microservices were designed to solve a specific problem: organizational scale. When you have thousands of developers working on a single product, a monolith becomes a bottleneck for deployments. However, most companies do not have thousands of developers. When a team of 10 or 50 developers adopts microservices, they often end up paying a “complexity tax” without reaping the benefits.
- Operational Complexity: Instead of managing one web server and one database, you now manage dozens of containers, service meshes, API gateways, and distributed logging systems.
- The Network Fallacy: Microservices rely on the network for communication. Unlike in-process calls, network calls fail, timeout, and introduce latency. Handling these failures requires complex patterns like circuit breakers and retries.
- Data Consistency Woes: In a monolith, you have ACID transactions. In microservices, you often deal with “eventual consistency.” Managing distributed transactions across multiple databases is notoriously difficult and error-prone.
The Power of Operational Simplicity
The primary argument for the large monolith is simplicity. In a monolithic architecture, the entire application runs as a single process. This simplicity radiates through every stage of the software development lifecycle.
Simplified Deployment and CI/CD
Deploying a monolith is straightforward. You build one artifact, run one pipeline, and deploy it to a cluster. With microservices, you often need complex orchestration to ensure that Service A is compatible with the new version of Service B. While “independent deployment” is the goal, in practice, many teams find themselves performing “distributed monolith” deployments where multiple services must be updated simultaneously anyway.
Streamlined Debugging and Observability
When an error occurs in a monolith, the stack trace usually tells the whole story. You can follow the logic from the API entry point down to the database query within a single IDE window. In a microservices environment, tracing a single request requires distributed tracing tools (like Jaeger or Honeycomb). If a request fails, the root cause could be tucked away in the fifth service down the chain, making MTTR (Mean Time To Recovery) significantly higher.
Developer Productivity and Velocity
One of the biggest myths is that microservices make developers faster. While they allow teams to work independently, they also introduce significant cognitive load. A developer working on a feature in a monolith can easily navigate the codebase, refactor interfaces, and see the immediate impact of their changes.
In a microservices world, a simple feature change might require updates to three different repositories, updating internal client libraries, and navigating multiple PR processes. This “context switching” is a silent killer of developer velocity. Furthermore, local development becomes a chore; instead of running one command to start the app, developers find themselves running Docker Compose files that consume 16GB of RAM just to get a basic environment running.
Performance: The Cost of the Network Hop
In a world obsessed with milliseconds, it is ironic that many choose microservices. Every time one service calls another over the network, it incurs a performance penalty. You have to serialize data (JSON or Protobuf), send it over the wire, and deserialize it on the other end. In a monolith, this is a function call that takes nanoseconds and happens in-memory.
For applications that require high throughput and low latency, the “chattiness” of microservices can become a major bottleneck. By keeping the core logic within a single process, you eliminate the overhead of the network stack, leading to faster response times and lower infrastructure costs.
The Modular Monolith: The Modern Middle Ground
It is important to distinguish between a “Big Ball of Mud” and a “Modular Monolith.” The failure of early monoliths wasn’t that they were a single deployment unit; it was that they lacked internal structure. A modern Majestic Monolith is designed with strict boundaries, often using modules or “engines” to keep concerns separate.
Key Characteristics of a Modular Monolith:
- Clear Domain Boundaries: Different business logic (e.g., Billing, Inventory, User Management) lives in separate folders or packages.
- In-Process Communication: Modules communicate via well-defined interfaces or internal event buses rather than HTTP calls.
- Shared Database (with Logic Separation): While the database is shared, modules are restricted to their own tables, preventing “spaghetti joins” that make future separation impossible.
By building a modular monolith, you get the organizational benefits of microservices (clear ownership and separation of concerns) without the operational headaches of distributed systems.
When Should You Actually Move to Microservices?
The monolith is the right choice for most startups and mid-sized enterprises, but it isn’t a silver bullet. There are legitimate reasons to transition to microservices, but they should be driven by necessity, not fashion.
- Extreme Scaling Needs: If one specific part of your app (like video processing) requires 100x more resources than the rest, it makes sense to break it out.
- Massive Team Size: When you have hundreds of developers, the friction of a single deployment pipeline becomes greater than the friction of microservices.
- Diverse Tech Stacks: If a specific feature requires a library only available in Python, while your main app is in Ruby, a microservice is a valid solution.
Conclusion: Choose Architecture for Your Reality
The tech industry is prone to “Resume-Driven Development,” where engineers choose technologies based on what looks good on a CV rather than what solves the business problem. Microservices are an elegant solution for the “Google-scale” problem, but for 95% of companies, they are an unnecessary burden.
The case for the large monolith is a case for pragmatism. It is a case for spending more time shipping features and less time debugging Kubernetes configurations. By embracing the Majestic Monolith, companies can achieve higher developer velocity, lower operational costs, and a more robust system. Before you break your application apart, ask yourself: is the problem the monolith, or is it just bad code? Often, the solution isn’t more services—it’s better architecture.