Deconstructing the Giant: The Backend Architecture and Databases Behind ChatGPT

Welcome to a new post on Code With Botina. We all use ChatGPT daily to debug code, write emails, or understand complex concepts. But, as software engineers, we cannot just stop at the user interface; we must ask ourselves: What on earth is happening in the backend when I press "Enter"?

Today, we are going to dissect the architecture of ChatGPT. We will talk about how their services are distributed, what databases they use to remember your conversations, and how they achieve that famous "real-time typing" effect.

1. The Base Infrastructure: The Azure Empire

OpenAI does not have its own traditional data centers; they run entirely on Microsoft Azure infrastructure.

To handle the massive amount of global traffic, the entire architecture is containerized and orchestrated with Kubernetes (Azure Kubernetes Service - AKS). This allows them to scale horizontally: if half a million students suddenly log in at the same time to do their homework, Kubernetes instantly spins up hundreds of new containers (pods) to handle the load.

The real physical "muscle" comes from gigantic clusters of thousands of NVIDIA GPUs (like the A100 or H100), connected by ultra-high-speed networks called InfiniBand.

2. The Best Kept Secret: LLMs are Stateless

This is where the concepts we've discussed on the blog come together. Just like the JWTs we explained before, AI models have no memory. They are stateless systems.

If you tell ChatGPT "Hi, my name is Botina" and then in another message you ask "What is my name?", the model itself has no idea. For the AI to remember, the backend has to send the entire chat history with every new HTTP request.

How do the microservices flow then?

API Gateway / Load Balancer: Your request comes in and is routed to the least busy server.
Session/History Service: Before touching the AI, this microservice goes to the database, fetches your latest messages from that session, and concatenates them with your new prompt.
Moderation Service: All that text passes through a secondary security API that checks that you are not asking for illegal or dangerous things.
Inference Fleet: Finally, the full text reaches the servers with the GPUs running the model (GPT-4), which process the response and send it back.

3. What Databases does ChatGPT use?

To maintain this flow at the speed of light, a traditional relational database (like MySQL) would collapse under the weight of millions of global reads and writes per second. Their data strategy is divided into layers:

Cache Layer (Redis): Used to store the context of the active conversation. Redis lives in RAM, so the History Service can retrieve your latest messages in milliseconds to build the prompt.
Persistent Database (Distributed): To permanently save your chat history (that sidebar you see on the left), they use highly distributed NoSQL databases (most likely Azure's Cosmos DB or a Cassandra-style database). These databases replicate information across multiple regions of the world, ensuring that if a server in the US goes down, your chats remain safe.

4. The "Typewriter" Effect: Server-Sent Events (SSE)

If ChatGPT used a normal HTTP request (GET or POST), you would have to stare at a blank screen for 10 or 20 seconds until the AI finished thinking of the entire response to send it all at once.

To avoid that bad user experience, they use Server-Sent Events (SSE). It is a one-way data stream. The client opens an HTTP connection and keeps it alive. As the GPU guesses the next word (token), the server immediately pushes it to the client.

Here is what a basic example of an endpoint simulating this would look like in a Node.js/Express backend:

// Streaming (SSE) Endpoint Example
app.get('/api/chat/stream', (req, res) =&gt; {
    // Set headers to keep the connection open
    res.setHeader('Content-Type', 'text/event-stream');
    res.setHeader('Cache-Control', 'no-cache');
    res.setHeader('Connection', 'keep-alive');

    const aiResponse = ["Hello, ", "I am ", "ChatGPT ", "and ", "I am ", "typing."];
    let iteration = 0;

    // Simulate the AI generating tokens every 500ms
    const interval = setInterval(() =&gt; {
        if (iteration &lt; aiResponse.length) {
            res.write(`data: ${aiResponse[iteration]}\n\n`);
            iteration++;
        } else {
            res.write('data: [DONE]\n\n');
            clearInterval(interval);
            res.end();
        }
    }, 500);
});

Conclusion

Behind the "magic" of Artificial Intelligence, there is a classic microservices architecture taken to the extreme. Load balancers, in-memory databases, state management, and persistent connections. Everything you learn today about backend development is the foundation for building the systems of the future.

What part of ChatGPT's architecture do you find most fascinating? Let me know in the comments!

If you are passionate about backend engineering and want to keep discovering how real-world applications are built, keep reading Code With Botina.