Cloud as a Story - Vunvulea Radu

 With over 1,800 cloud projects delivered by one organization, it’s hard to answer a simple question: what cloud services are actually used where? As projects grow, teams change, and repositories multiply, tracking which AWS or Microsoft Azure services are used in each project, or across industries, becomes a real challenge. This knowledge often exists only in people’s heads or scattered across codebases, making it harder to find than expected. For me, this is not just curiosity. I needed this information for three practical reasons. First, I want to understand patterns across industries and customers. One customer uses a lot of storage and messaging. Another one is more about identity, monitoring, and eventing. When you have many repos, your brain cannot keep this map in its head. Second, RFP questions can be very specific. They don’t ask “Do you have cloud experience?” They ask, “How many projects have you delivered with DynamoDB?” or “Do you have experience with Azure Functions...

 Many organisations struggle to get value from AI, even after moving to the cloud. The main obstacle is outdated cloud infrastructure, which impedes the use of AI. Only with a modern cloud foundation can AI deliver real and lasting business value. But there is one big question that always comes up when people consider investing in modernisation: “How can we show the business value in a simple way, not just with technical terms?” In this post, I will share five metrics we often use with clients. These are easy for non-technical leaders to understand and clearly show how updating the cloud helps unlock AI’s potential. 1. Customer-Facing Throughput First, this metric shows how many customer requests, predictions, or transactions the system can handle in a short period. If an AI recommendation service slows down or cannot scale, customers notice the impact right away. Modernising the cloud increases throughput by allowing systems to scale and process data faster. This results in a bett...

 When adopting Azure Spot Virtual Machines, one of the first challenges is understanding how billing and eviction actually work. Many people initially believe Spot VMs require a minimum runtime, such as 5 minutes. This is not correct. Spot VMs follow the same billing model as standard Pay-As-You-Go VMs: they are billed per second, with a 60-second minimum charge. If a VM runs for less than a minute, you pay for the full minute. After that, you pay only for the exact seconds it runs. The 5-minute intervals you may see on Azure pricing pages are only part of the UI graphs—they are not related to billing. Spot pricing is based on Azure’s unused capacity. Because of this, Spot VMs often come at massive discounts—sometimes 65% to 90% off On-Demand pricing. The exact discount varies by VM family, region, availability zone, and overall demand. When provisioning a Spot VM, you can set a max price bid. Setting it to “-1” means you agree to pay up to the normal On-Demand price. Your VM will ...

 These days, AI tools like GitHub Copilot or ChatGPT feel less like helpers and more like real partners in our engineering work. Now comes vibe coding—a funny-sounding idea with huge impact. Vibe coding means working with AI to express our software intentions and context, rather than focusing solely on precise code. It shakes up how we think about writing, fixing, and running software. Building on this, for years, code was our bridge to machines. We sweated over rules, patterns, and clean syntax to make them understand us. But AI gets our intentions now, straight from the vibe, the goal, the context, what we really want. No more line-by-line translation needed. Of course, this shift isn't without its quirks. The code AI generates isn't always pretty; it might look messy or hard for humans to read. But that's okay. Future SRE and support won't be people, they'll be AI agents. These agents regenerate logic, fix bugs, and tweak things without whining about style. They ...

 Working on legacy applications has always been intimidating. When I began a large migration of a .NET  project or an old Java system to Azure, it felt like going back in time to unravel years of decisions made by people who might no longer be there. Before you can write any code, you often spend days or weeks just figuring out how everything fits together. By the time you understand it all, it’s easy to feel your motivation slipping away. That’s why Microsoft’s new AI-powered tools are such a relief. With the latest GitHub Copilot and Azure features, modernization begins with clarity instead of worry. One major improvement is how the GitHub Copilot App Modernization Agent reviews legacy code. It highlights outdated frameworks, risky patterns, library issues, and migration risks. It’s like having a teammate who already understands the system. When I use it with Dr. Migrate, which checks the app on Azure’s side, I get a much clearer, faster view of what I’m dealing with. The ne...

AI’s true potential comes from advances in cloud platforms, not just from building better models.  Many AI projects run into problems because their cloud environments are not prepared to support them. In this article, I’ll share a practical guide on how organisations can move from a traditional cloud setup to an AI-Native platform in five clear steps. This process is based on what we see with clients whose cloud foundations are not ready for AI adoption. Let’s look at how these five steps can turn a basic cloud into a platform that learns, adapts, and grows. Step 1: Cloud-Native Refactoring Many organisations begin by lifting and shifting workloads into virtual machines (VMs) without changing how their applications are built. These apps still act like they’re running in a traditional data centre, with local data storage, slow scaling, tight dependencies, and all functions bundled together. When you add AI workloads, these systems often can’t keep up. Refactoring for the c...

  In technology conversations, “best practices” are mentioned everywhere—architecture reviews, governance frameworks, and delivery checklists. They are part of how we design and operate digital platforms. But in many projects, especially those with low or moderate workloads, best practices may feel theoretical. They look good on paper, yet the business impact is not always visible.   I recently worked on a project that challenged this perception. We pushed Azure Batch to operate at over 100,000 vCores, stretching the service's limits and placing significant pressure on Azure Storage, Azure Container Registry, and the networking layer. At this scale, every detail matters. And suddenly, all those Microsoft recommendations that previously seemed optional became essential.   1. Best Practices Deliver Real Value When Systems Become Truly Intensive For smaller systems or early-stage products, it is easy to overlook best practices. Everything works fine. For example: ...

For the last decade, the 6 Rs of cloud migration have been used to describe how enterprises should adopt the cloud: Rehost, Replatform, Refactor, Retain, and, sometimes, Retire. The 6 Rs of cloud migration have guided enterprises in adopting the cloud. However, with AI now central to digital transformation, these Rs alone are no longer sufficient. Cloud migration is just the first step; true AI-Native status requires a deeper cloud-native transformation. Customers labelling their migrations as Cloud-Native often have applications that still behave like on-premises systems, resulting in manual operations, static systems, and locked data that hinder AI programs. This is where a new perspective is required to build AI capabilities on top of the 6Rs. Pure cloud-native solutions are difficult for large enterprises. Realistically, we need to identify gaps and what is needed to prepare for AI integration. In the next part of the article, each R will be analysed in terms of AI-Native needs. R...