Stateless vs Stateful – How to Scale Your Systems Like a Pro

This blog demystifies stateless vs stateful services with a focus on scalability. It explains why stateless services often scale more easily, what hurdles stateful services face, and how to choose the right approach for your needs.

Let’s say your app suddenly gets featured on Product Hunt.

Traffic explodes.

Thousands of users flood in—but only half of them get a response.

The rest?

Stuck waiting.

Welcome to the world of scalability problems, and often, the culprit is how you manage state.

Whether your backend remembers everything (stateful) or treats each request like a blank slate (stateless) can make or break your system’s ability to handle real-world load.

In this post, we’ll untangle stateless vs stateful services, understand how they impact scaling microservices, and explore what this means for your system design interview prep and production systems.

What Are Stateless vs Stateful Services?

A stateless service doesn't remember anything from one request to the next.

Every request stands on its own.

The server processes the input, returns a response, and then forgets about it.

For example, a REST API that returns data based only on the query (say, a weather or stock quote service) is stateless. Any server can handle any request because no context is stored.

In contrast, a stateful service maintains context between requests. It's like a continuing conversation – the service remembers who you are or what you did last.

Think of an online shopping cart or user session: when you add an item to your cart and navigate to another page, the server remembers your cart contents.

That server is maintaining state about your session.

Stateful services are needed for features like shopping carts or multiplayer games where the application must track ongoing interactions.

Both models work, but they scale very differently.

Scaling: Why State Matters

Stateless services are easier to scale horizontally.

Since no session data is tied to any one server, you can add more servers under a load balancer to handle extra traffic without hassle.

Any server can handle any request.

If one server goes down, another can seamlessly take over because no user-specific data was lost.

When demand spikes, you simply spin up more instances and distribute the load.

Stateful services are harder to scale.

When a server stores user data in memory (sessions, game state, etc.), you can't freely send that user’s requests to a different server without worrying about where that data lives.

A common solution is to use sticky sessions so the load balancer always sends a given user to the same server.

This preserves session continuity but reduces flexibility – if that server gets overloaded or crashes, the user's session can be lost.

To scale stateful services, teams often externalize the state.

Instead of keeping session data on one server, store it in a shared database or cache that all servers can access.

Then any server can retrieve or update the client’s state from this central store.

For example, an e-commerce site might keep shopping cart data in a Redis cache or database.

Then any web server can fetch your cart from Redis and serve you, so you're not stuck to one machine. This makes scaling out easier and avoids losing session data if a server dies.