Are Microservices Overhyped?

Microservices have been a popular choice in modern software architecture for years. Big names like Netflix and Amazon have successfully implemented them, making everyone want to jump on the bandwagon. But are they always the right choice? Do we really need 50 services to build what could fit in a single monolith? Let’s dive deeper into this.

Appeal of Microservices

Microservices promise scalability, flexibility, and fault isolation. Each service can be independently developed, deployed, and scaled. In theory, this makes your system robust and easy to manage.

For example, a streaming platform like Netflix can benefit from microservices because it has distinct domains like user management, recommendations, playback, and billing. Each domain operates independently, allowing for continuous deployment and scaling specific to its needs.

But in practice, many developers and companies overuse microservices, leading to unnecessary complexity. Let’s explore the hidden costs.

Hidden Costs of Microservices

1. Overused Infrastructure

Microservices require a lot of infrastructure.

You need:

• Orchestrators like Kubernetes to manage service deployments.
• Load balancers to distribute traffic.
• Service discovery tools to help services find each other.
• Monitoring systems like Prometheus and Grafana to keep an eye on the health of each service.

For example, a small SaaS tool for managing to-do lists doesn’t need Kubernetes. A single VPS running a monolith would be simpler, cheaper, and easier to maintain.

2. Fragile HTTP Chains

Microservices communicate through APIs, often using HTTP or gRPC. If one service fails, it can cause a domino effect, bringing down the entire system. For example:

• A payment service fails during a high-traffic sale.
• This causes the order service to hang because it’s waiting for payment confirmation.
• The user’s checkout experience is ruined, and sales are lost.

To prevent this, you might need circuit breakers (e.g., Hystrix) and retries, but these add more complexity to your system.

3. Async/await Overuse

To manage dependencies between services, developers often rely on asynchronous programming. While async/await can improve performance, it’s often a band-aid for poor design. Instead of addressing bottlenecks, developers patch the system with asynchronous calls, making debugging and tracing harder.

For instance, a notification service that sends emails and SMS might call multiple APIs asynchronously. If one API is slow or fails, it complicates error handling and debugging.

4. Operational Overhead

Managing dozens of microservices means:

• Separate CI/CD pipelines for each service.
• Versioning APIs to avoid breaking changes.
• Coordinating deployments to ensure compatibility.

For example, a simple feature update might require changes in 3–5 services, each with its own deployment process. This slows down development and increases the risk of errors.

Better Alternatives to Microservices

If microservices aren’t always the answer, what are the alternatives?

1. Modular Monoliths

A modular monolith keeps the application in a single codebase but enforces clear boundaries between modules. Each module handles a specific domain, like orders, payments, or users. This approach offers:

• Easier debugging.
• Simpler deployments.
• Lower infrastructure costs.

For example, a blogging platform could have modules for posts, comments, and user authentication. Each module interacts through internal function calls instead of external APIs, reducing latency and complexity.

2. Distributed Monoliths

A distributed monolith is a hybrid approach where the application is split into components that are independently deployable but tightly coupled in terms of data and communication. This approach is useful when you need some level of separation but don’t want the full complexity of microservices.

For instance, an e-commerce platform might separate its catalog and checkout services for independent scaling but keep them tightly integrated with a shared database to avoid complex data synchronization.

3. Message Queues

Instead of chaining HTTP calls, use message queues like RabbitMQ, NATS, or Kafka. For example:

• An order service publishes an “order placed” event.
• The payment service processes the event asynchronously.

This decouples services and reduces cascading failures. For instance, if the payment service is down, the order event is stored in the queue and processed later.

4. Vertical Scaling

Before splitting your app into microservices, try vertical scaling. Upgrade your server’s CPU, memory, or storage. Modern cloud providers make this easy with scalable instances.

For example, an analytics dashboard with moderate traffic might perform well on a single high-performance server instead of being split into multiple services.

5. Serverless Architectures

For event-driven tasks, serverless functions like AWS Lambda or Google Cloud Functions are excellent. They:

• Scale automatically.
• Charge only for usage.
• Eliminate server management.

For instance, a function that processes image uploads can scale to handle thousands of requests during peak times without requiring dedicated servers.

6. Simpler Tools

Sometimes, simpler tools are all you need:

• Docker Compose: Great for local development and small deployments. For example, running a monolith alongside a database and a caching service.
• SQLite: Perfect for low-traffic apps like personal blogs or small business tools.
• Redis Streams: Lightweight and efficient for real-time data. For example, a chat application could use Redis Streams to handle message delivery.

7. Event-Driven Monoliths

In an event-driven monolith, different modules communicate through events rather than direct function calls. This decouples the modules while keeping everything in a single codebase.

For example, a CRM system might have modules for leads, sales, and reporting. When a lead converts, an event is published, and the sales module updates automatically.

8. Service-Oriented Architecture (SOA)

SOA is a predecessor to microservices but with larger, coarser-grained services. These services handle broader responsibilities and are less fragmented than microservices.

For example, a travel booking system might have services for flights, hotels, and car rentals. Each service is a standalone application but interacts with others through defined APIs.

When to Use Microservices

Microservices aren’t inherently bad. They shine in specific scenarios:

• Large Teams: When multiple teams need to work independently. For example, an e-commerce platform with separate teams for search, recommendations, and payments.
• High Scalability Needs: Apps with millions of users, like social media platforms or video streaming services.
• Complex Domains: Systems requiring strict separation of concerns, like banking applications with compliance and auditing requirements.

But if your app doesn’t meet these criteria, consider simpler alternatives.

Final Thoughts

The best architecture is the one that solves your problem without unnecessary complexity. Microservices are powerful but overused. Modular monoliths, message queues, and simpler tools can often achieve the same goals with less effort.

For example, a SaaS tool for managing employee timesheets could start as a monolith. As the user base grows, you could extract critical components like authentication or reporting into separate services if needed.

What’s your experience with microservices? Have you faced challenges or found better alternatives? Let’s discuss in the comments!