Cloud as a Story - Vunvulea Radu

 When you're moving SQL to Azure, you'll face a lot of choices. Azure has several different ways you can run SQL, with each own tradeoffs around control, compatibility, cost, and how much day-to-day work is involved. If you focus only on moving quickly, you might end up with higher costs down the road, run into missing features, or give yourself more maintenance headaches than expected. Before you start comparing the different products, step back and think about what you really need to accomplish. As you weigh your options, keep these three big requirements in mind: 1. Scalability and cost shape Decide early on if you'll need to scale up (make a server bigger) or scale out (spread across more servers). Scaling up is usually easier, while scaling out gives you more flexibility, but might mean changing your application and being more disciplined operationally. Think about your workload: if it's pretty steady, provisioned compute is a good fit; if it varies a lot, you'...

 Traditional IT solutions typically remain stable for years, requiring only occasional patches, scaling, or feature additions. In contrast, AI systems operate in a changing environment, so ROI must be monitored and recalculated regularly. Here are the three main reasons for the volatility of AI in Azure and other cloud projects: Models are frequently replaced or retired. In the Microsoft and Azure ecosystem, model families evolve rapidly. A model used last year may become unavailable or inferior. Even without application changes, costs, speed, and quality can fluctuate. Quality can change over time. New documents in SharePoint, evolving policies, and new user questions can impact performance. Without regular updates to your RAG pipeline, prompts, and evaluation sets, accuracy and trust decline, reducing system adoption. Costs can increase with usage. Token cost is only one factor; vector search, storage, observability, and human review become crucial as adoption grows. What appears...

Most people know what ROI means, but it’s harder to calculate for AI projects. The numbers are less predictable than with traditional platforms because many AI projects never reach stable production. IDC says only about 44% of custom AI apps and 53% of third-party AI apps make it from proof of concept to production. That’s why it’s important to look at ROI through a risk lens, not just cost versus benefit. One useful approach is to use a risk-adjusted formula: AI ROI = (AI Business Value Income / (Initial Investment + Annual Costs)) × Success Probability where, >AI Business Value Income (over N years) Consider a 2 to 3 year period and include both direct and indirect value: Direct: time saved, fewer tickets, higher conversion, lower fraud. Indirect: improved customer or employee experience and quicker decisions. For these, use measurable stand-ins like CSAT, churn, time to resolution, or hours saved, and estimate conservatively. >Initial Investment This covers more than just buil...

 Many companies use AI tools such as ChatGPT Enterprise and Microsoft Copilot to raise efficiency and reduce repetitive tasks. However, it is essential to clarify the meaning of the “private” label. In an enterprise setting, “private” typically refers to daily sharing restrictions rather than absolute confidentiality. Organizations may still access these chats for governance, security, or legal reasons. ChatGPT Enterprise OpenAI states that, by default, ChatGPT Enterprise does not use business data (inputs and outputs) to train its models. Customers retain ownership and control over their data, including retention settings. OpenAI also maintains compliance with requirements such as GDPR through contractual agreements, such as a Data Processing Addendum (DPA). Within an enterprise workspace, “private chat” generally means chats are not shared with colleagues, but it does not guarantee that administrators cannot access them. Enterprise plans may use compliance tools such as the Compl...

After 17 years working with cloud technology, I’ve seen a clear pattern. AI projects rarely fail because the model is weak. More often, the problem is that the platform was built for traditional applications, not for AI. GenAI and agents add extra demands on the architecture. AI also brings unpredictable traffic and new security and governance challenges. Here’s a reference architecture I use when designing AI-native platforms on Microsoft Azure. It’s not a strict blueprint, but a practical structure to keep teams aligned and prevent surprises as the solution grows. User and API entry layer Start with a clear entry point. Focus on predictable performance, strong security, and access control. On Azure, many teams use Azure Front Door or Application Gateway for incoming traffic, then add Azure API Management to manage API exposure, throttling, authentication, and versioning. A common mistake is exposing AI endpoints directly to the internet. It might seem quick for a proof of concept, bu...

 For large organizations, Azure success depends on solid governance, clear requirements, planned initiatives, and business priorities. Start with a clear hierarchy to apply rules consistently across the organization, not just to individual projects. First, I set up core elements: management groups, subscriptions, resource groups, and then resources. This structure is practical and important for scaling access and compliance controls. Management groups matter if you have multiple subscriptions and want a uniform baseline. I keep them shallow, three to four levels, since more are hard to manage. Azure allows up to six (excluding the tenant root and subscription level). Assignments at higher levels cascade down, so hierarchy matters. I use subscriptions as boundaries for billing and scaling. Splitting development, testing, and production into separate subscriptions isolates costs and risks. A dedicated subscription for shared network services, such as ExpressRoute or Virtual WAN, simp...