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Azure App Service doesn’t get much love. It’s not shiny. It’s not trendy. It doesn’t give you the satisfaction of saying “we’re fully containerized.” And yet, it’s where some of our most reliable production workloads live. After running App Services, Functions, and Container Apps side by side, I’ve reached a conclusion that feels almost unpopular in 2025: Boring infrastructure is often the best infrastructure. The Problem With Exciting Compute When teams evaluate Azure compute options, the conversation usually starts with features: ...

Because mistakes scale faster. Infrastructure mistakes scale with traffic. AI mistakes scale with usage and trust. That is faster. AI Errors Feel Plausible AI failures are dangerous because they often look reasonable. The output is coherent. The tone is confident. The result is wrong. That combination spreads errors quietly. When infrastructure fails, it fails loudly. Services return 500 errors. Dashboards turn red. Alerts fire. The failure is obvious and immediate. ...

At first, Terraform was just a way to create resources. It lived next to the platform. It supported it. It automated it. Then, quietly, Terraform became the platform. The Shift Was Subtle There was no announcement. No rewrite. No big migration moment. It happened when: new environments required Terraform first access flowed through Terraform definitions changes without Terraform felt unsafe platform discussions started with code Terraform stopped being an implementation detail. It became the source of truth. ...

Guardrails over guesses. We are deliberate about how we use AI. We do not ask it to invent solutions. We ask it to follow patterns. Patterns Encode Experience Patterns exist because something worked. They represent lessons learned, mistakes avoided, and decisions made. AI is very good at following rules. It is less good at choosing them. Patterns are distilled experience. A retry pattern with exponential backoff exists because someone learned the hard way that constant retries overwhelm systems. A specific logging format exists because it makes debugging easier. A particular testing structure exists because it catches common mistakes. ...

Enforcing consistency costs almost nothing now. Structure used to be expensive. Templates took time. Reviews took energy. Enforcement took social capital. AI changed that. Structure Is No Longer a Bottleneck We can now ask for: the same format the same sections the same tone the same constraints Every time. Without friction. That makes structure cheap. Before AI, enforcing structure was a people problem. You could create templates and style guides, but getting engineers to follow them required constant effort. ...

Boring wins beat flashy demos. The biggest wins from AI were not dramatic. They were boring. Structure and Consistency AI helped most with: consistent formatting predictable layouts standard language repeatable patterns Things humans are bad at doing repeatedly. That alone improved quality. Humans are inconsistent at boring tasks. We get tired. We forget details. We take shortcuts when deadlines loom. We apply standards differently depending on mood or urgency. AI does not have these problems. It applies rules mechanically and consistently. If you tell it to format all documentation with the same structure, it does. Every time. Without fatigue or shortcuts. ...

AI did not replace engineers. It changed how engineering time is spent. That difference is important. Multipliers Amplify What Exists AI amplifies habits. Good structure gets better. Bad structure gets faster. Ambiguity spreads quickly. If a team lacks standards, AI accelerates inconsistency. If a team has standards, AI reinforces them. AI acts like a force multiplier on existing team practices. It makes whatever you are already doing faster and more visible. ...

Why usage patterns surprise teams. Teams often assume AI scales like infrastructure. More users. More requests. More cost. That intuition breaks down quickly. Compute Scales With Load Traditional compute scales with demand. Requests per second. Concurrent users. CPU utilization. You can model that. You can predict it. You can cap it. AI does not behave that way. Infrastructure costs are relatively predictable. You know how much a server costs per hour. You can estimate how many requests a service handles per second. You can project cost based on expected traffic growth. ...

Terraform does something uncomfortable very well. It preserves mistakes. At first, that feels like a problem. Over time, it becomes one of its biggest strengths. Before Terraform, Mistakes Were Ephemeral Before infrastructure lived in code, mistakes were scattered. Someone changed a setting in the portal. Someone applied a hotfix directly. Someone clicked a checkbox to make something work. Those mistakes disappeared into history. They could not be explained. They could not be repeated. They could not be intentionally fixed. ...

Boundaries matter more with AI than with humans. Trust is contextual. We trust engineers with secrets because they are accountable. We do not trust AI with secrets because it is not. That distinction matters more than people admit. AI Has No Sense of Boundary AI does not understand intent. It does not understand sensitivity. It does not understand consequences. It only understands inputs and outputs. If a secret appears in a prompt, the model treats it as data, not as something to protect. ...