Not every AI request requires the most powerful model available. Summarizing a short email, extracting information from an invoice, and debugging a complex application are all different tasks, yet it's entirely possible for an application to send every request to the same large language model. That approach is
AI agents can do quite a bit more than generate text. They can retrieve information, interact with APIs, create tickets, and even execute code. But for any of that to happen, the agent needs a way to communicate with the systems it's supposed to work with. Traditionally, developers
Most of us are familiar with AI chatbots by now. You ask a question, the model generates a response, and the interaction ends there. Whether you're debugging code or summarizing a document, the model is primarily generating information for you to use. AI agents take this a step
Traditional databases are excellent at finding information when you know exactly what you're looking for. Give a database a customer ID, email address, or order number, and it can retrieve the corresponding record efficiently. But searching for information when you don't know the exact terminology can
Large language models can answer questions about almost anything, from writing code to explaining complex technical concepts. However, they don't automatically have access to your company's internal documentation, customer records, or the latest changes to your infrastructure. For example, you could ask an LLM to explain
We've all heard about the amount of computing power required to develop large AI models. Thousands of GPUs, enormous datasets, and training runs that can take weeks or even months. With how quickly AI is being adopted, it's reasonable to wonder whether every company building an
When you're deploying a traditional web application, choosing a server usually comes down to how much CPU, memory, and storage you need. You estimate the workload, pick an instance size, and adjust as traffic grows. AI workloads introduce another consideration: GPUs. GPUs have been around for decades, primarily
We've all gotten pretty used to opening ChatGPT, typing in a question, and receiving a response within seconds. Whether it's writing code, summarizing documents, or helping troubleshoot something, the experience is fairly straightforward. Behind the scenes, there's quite a bit of infrastructure involved. Every
At some point, every infrastructure diagram becomes fiction. A quick fix here. A hot patch there. A change made “just this once” to get things back online. Weeks later, production still works, but nobody is entirely sure why. That quiet gap between what you think is running and what’s
Uptime numbers look simple. Systems are either up or down. In reality, availability is a spectrum, and fault tolerance sits at the far and expensive end of it. High availability and fault tolerance are often used interchangeably. They shouldn’t be. They solve different problems and come with very different
Scaling in the cloud often sounds easier than it actually is. Add more instances. Put a load balancer in front. Problem solved. Until traffic increases, users start getting logged out, and things behave inconsistently across servers. More often than not, the root cause isn’t compute or networking. It’s
Cloud outages rarely start with something dramatic. It’s usually a small change. A dependency times out. A zone hiccups. Someone deploys on a Friday. Designing for failure isn’t about being pessimistic. It’s about being realistic. In the cloud, failure is not a surprise event. It’s a