Our AI Engineering Stack: Built on the Platform We Ship

Carmen López · 2026-04-20

At Cloudflare, we don't just build tools for developers; we live and breathe them. This is the story of our internal AI engineering stack, a system we crafted using the same platform we ship to you. It's not about theory; it's about what works in the trenches, every single day. We wanted a stack that's fast, reliable, and secure, without the usual headaches. So, we started with the question: what if we used our own products to solve our own problems? The answer was a stack that's as pragmatic as it is powerful. ### Why Build Your Own Stack? You might wonder, why not just use off-the-shelf solutions? The truth is, we needed something that could scale with our unique demands. We handle a massive amount of traffic and data, and we needed a stack that could keep up. Plus, by building on our own platform, we get to eat our own dog food. It's the ultimate test of our products. This approach gives us a few key advantages: - **Full Control:** We can tweak every layer to fit our exact needs. - **Performance:** We optimize for our specific workloads, not a generic use case. - **Security:** We know exactly where our data lives and how it's protected. - **Speed:** We iterate fast because we're not waiting on third-party vendors.  ### The Core of Our Stack Let's break down the main components. It's not a monolith; it's a set of interconnected services that work together seamlessly. #### The Data Layer Everything starts with data. We use a combination of our own edge network and cloud-native databases. This means our AI models can access information with minimal latency, no matter where in the world a request comes from. Think of it as having a super-fast brain that's always close to the action. #### The Compute Layer For running our AI models, we rely on serverless functions. This lets us scale up or down automatically, paying only for what we use. It's like having an engine that only revs up when you step on the gas, saving energy when you're coasting. #### The Inference Layer This is where the magic happens. We use our own AI inference platform to run models like large language models and image recognition. The key here is efficiency. We've optimized our models to run quickly, often in milliseconds, so users don't notice any delay. ### What We Learned Building this stack wasn't always smooth sailing. We hit our share of roadblocks. One big lesson was the importance of caching. By caching common AI requests, we cut response times by over 50 percent. It sounds simple, but it made a huge difference. Another thing we learned is that you don't need the most expensive hardware to get great results. With smart engineering, we've achieved amazing performance using cost-effective, off-the-shelf components. It's proof that a well-designed system beats raw power every time. > "The best tool is the one you actually use. By building our stack on our own platform, we ensure it's battle-tested and ready for anything." ### How This Helps You Here's the best part: everything we learned and built is available to you. Our platform is designed to give you the same power and flexibility we enjoy internally. You don't need to be a Cloudflare engineer to benefit from this stack. Whether you're building a chatbot, a recommendation engine, or a security tool, you can leverage the same components we use. It's like having a shortcut to a proven, high-performance system. ### The Future of AI Engineering We're just scratching the surface. As AI evolves, so will our stack. We're already looking at ways to integrate more advanced models and make everything even faster. The goal is always the same: give developers the tools they need to build amazing things, without the complexity. So, next time you're thinking about your AI infrastructure, remember that sometimes the best solution is the one you can build yourself, using tools you already trust. That's the philosophy that drives us, and it's the foundation of our internal stack.