ARM templates look powerful on paper – but have you noticed how every deployment turns into a maze of JSON and copy-pasted sections? Many teams find that what should be a straightforward rollout quickly becomes cluttered, brittle, and frustrating to manage. That’s where Bicep comes in. In this podcast, we’ll break down why ARM often trips teams up, show how Bicep fixes those pain points, and walk through examples you can actually reuse in your own Azure environment. By the end, you’ll see how to make deployments simpler, faster, and far more consistent. Before we get into it, drop a comment with the biggest issue you’ve hit when using ARM templates. I want to see how many of you have wrestled with the same problems. So let’s start with the basics — why does something as small as deploying a single resource often feel like wrestling with far more code than it should?

Why ARM Templates Break More Than They Build

ARM templates were meant to make cloud deployments predictable and consistent, but in practice they often do the opposite. What looks straightforward on the surface tends to collapse into complexity the moment you write a real template. Take something as basic as spinning up a single virtual machine. You’d expect a few short definitions. Instead, a template like that quickly sprawls into hundreds of lines. Each piece is wrapped in JSON syntax, parameters are duplicated, dependencies stretch across the file, and the whole thing feels heavier than the task it’s supposed to handle. That mismatch between promise and reality is the biggest complaint teams share. The appeal of ARM lies in its declarative model—define the desired state, and Azure figures out the rest. But once you start building, the weight of formatting, nesting, and long property strings drags the process down. It’s less like writing infrastructure code and more like juggling brackets until something finally compiles. The closest analogy is building furniture from instructions. With a brand like IKEA, you at least get diagrams that guide you through. ARM feels like the opposite: no clear diagram, just dense text spelling out every screw and hinge in excruciating detail. You’ll end up with the finished product, but the road there feels unnecessarily painful. And the pain doesn’t stop at writing. Debugging ARM templates is where most teams hit the wall. Error messages rarely explain what’s actually broken. Instead, you’ll get vague references to invalid structures or missing parameters with no pointer to where the fault lies. That leaves you scrolling through a massive JSON file, trying to match braces and commas while the deployment pipeline blocks your release. The language itself is brittle enough that a missing bracket or an extra comma somewhere across those hundreds of lines can stop everything cold. For that reason, many Azure admins admit they spend far more time troubleshooting ARM than they’d like to admit. It’s a common story: a deployment fails for reasons that aren’t obvious, hours get burned tracking the issue, and eventually someone caves and applies the fix directly in the Azure portal. It works at that moment, but the template becomes useless because what’s in the file no longer reflects what’s actually running. One IT team I spoke with described this cycle perfectly. They had a template designed to set up a handful of basic resources—storage, load balancers, the usual. When it refused to deploy cleanly, they chipped away at the errors one by one. Every “fix” uncovered something else. Eventually, under pressure to meet a deadline, they gave up on the JSON and finished the changes manually. By the end, the live environment worked, but the template was so far out of sync it couldn’t be reused. That scenario isn’t unusual; it’s the pattern many teams fall into. Small workarounds like that are what make ARM especially risky. Because templates are supposed to act as the single source of truth, any time someone bypasses them with manual changes, that truth erodes. A firewall rule added here, a VM tweak applied there—it doesn’t seem like much at the time. But after a while, what’s meant to be a reliable, reusable script turns into little more than a skeleton you can’t actually trust. The template still exists, but the environment it represents has drifted away. This cycle—verbose files, vague errors, brittle syntax, and manual fixes—explains why so many people grow frustrated with ARM. The tool designed to simplify Azure ends up creating overhead and eroding consistency. And while it’s tempting to blame user error, the truth is that the language itself sets teams up for this struggle. Later in this video, I’ll show you what this looks like with a real demo: the same deployment written in ARM versus in its modern replacement, so you can see the difference side by side. But before we get there, there’s another effect of ARM worth calling out—one that doesn’t become obvious until much later. It’s the slow drift between what your template says you have and what’s actually happening in your environment. And once that drift begins, it introduces problems even ARM can’t keep under control.

The Silent Killer: Configuration Drift

Environments often start out looking identical, but over time something subtle creeps in: configuration drift. This is what happens when the actual state of your Azure environment no longer matches the template that’s supposed to define it. In practice, drift shows up through quick portal edits or undocumented fixes—like a firewall tweak during testing or a VM change applied under pressure—that never get written back into the code. The result is two records of your infrastructure: one on paper, and another running live in Azure. Drift builds up silently. At first, the difference between template and reality seems small, but it compounds with each “just one change” moment. Over weeks and months, those small edits grow into systemic gaps. That’s when a dev environment behaves differently from production, even though both were deployed from the same source. The problem isn’t in the template itself—it’s in the growing gap between written intent and working infrastructure. The operational impact is immediate: troubleshooting breaks down. A developer pulls the latest ARM file expecting it to mirror production, but it doesn’t. Hours get wasted chasing nonexistent issues, and by the time the real cause is found, deadlines are in jeopardy. Security risks are even sharper. Many incidents aren’t caused by brand-new exploits but by misconfigurations—open ports, unpatched access, forgotten exceptions—that came from these quick changes left undocumented. Drift essentially multiplies those gaps, creating exposures no one was tracking. A simple example makes the point clear. Imagine creating a rule change in the portal to get connectivity working during a test. The fix solves the immediate issue, so everyone moves on. But because the ARM template still thinks the original configuration is intact, there’s now a disconnect between your “source of truth” and what Azure is actually enforcing. That single gap may not cause a failure immediately, but it lays a foundation for bigger, harder-to-find problems later. Think of drift like a clock that loses small fractions of a second. Early on, the difference is invisible, but over time the gap grows until you can’t trust the clock at all. Your templates work the same way: a series of small, unnoticed changes eventually leaves them unreliable as a record of what’s really running. ARM doesn’t make this easier. Its bulk and complexity discourage updates, so people are even less likely to capture those little changes in code. Long JSON files are hard to edit, version control conflicts are messy, and merge collisions happen often. As a result, entire teams unknowingly give up on the discipline of updating templates, which accelerates drift instead of preventing it. The cost reveals itself later during audits, compliance checks, or outages. Teams assume their templates are authoritative, only to learn in the middle of a recovery effort that restoring from them doesn’t rebuild the same environment that failed. By then it’s too late—the discrepancies have been accumulating for months, and now they break trust in the very tool that was supposed to guarantee consistency. That’s why configuration drift is sometimes referred to as the “silent killer” of infrastructure as code. It doesn’t break everything at once, but it erodes reliability until you can’t depend on your own files. It undermines both day-to-day operations and long-term security, all while giving the illusion of control. The frustration is that drift is exactly the kind of problem infrastructure as code was meant to solve. But in the case of ARM, its structure, size, and difficulty in upkeep mean it drives drift instead of preventing it. Later in this video, I’ll show how Bicep—through cleaner syntax and modular design—helps keep your code and your environment aligned so drift becomes the exception, not the norm. And while that addresses one hidden challenge, there’s another looming issue that shows up as soon as you try to scale an ARM deployment beyond the basics. It’s not about drift at all, but about the sheer weight of the language itself—and the breaking point comes much faster than most teams expect.

Where ARM Templates Collapse Under Their Own Weight

Once templates start moving beyond simple use cases, the real limitations of ARM become unavoidable. What feels manageable for a single VM or a storage account quickly becomes unmanageable once you add more resource types, more dependencies, and start expecting the file to describe a real-world environment. The growth problem with ARM has two parts. First, there is no clean way to create abstractions or reuse pieces of code, so copy-paste becomes the only real option. Second, every copy-paste increases size, clutter, and repetition. A modest deployment might start neat, but scaling it means ballooning templates, duplicated sections, and files that are far longer than they should be. The consequences show up just as quickly. Large templates lead to endless merge conflicts. Reviews drag on because no one can confidently trace which section controls what. Teams get so wary of breaking something that changes are postponed, or worse, avoided altogether. Instead of being a shared source of truth, the template becomes a fragile document everyone is nervous to touch. Picture a spreadsheet that keeps growing until it takes forever to open—that’s the ARM experience. The code still works, but managing it feels like a slog just to keep the system running. Engineering teams often describe this as “merge wars.” When multiple people work inside a JSON template that’s thousands of lines long, pull requests collide. Braces and brackets stack up, sections duplicate, and even small updates create cascading conflicts. It’s frustrating enough that different groups sometimes split the work into separate templates just to bypass the pain. But splitting files erodes the whole point of having a single declarative definition—suddenly no one is sure which version is current or authoritative. The organizational cost is just as heavy. A growing environment means larger files, which means slower reviews, longer feedback cycles, and a higher risk of human error. Instead of accelerating delivery, ARM slows it. Teams spend their time managing syntax instead of delivering infrastructure. What’s worse, the very goal of infrastructure as code—having clarity and consistency—gets lost under the weight of sprawling, repetitive JSON. The difficulty isn’t that teams don’t know how to write infrastructure. It’s that ARM doesn’t provide a model for doing it efficiently at scale. Complexity multiplies instead of tapering off. The larger your environment grows, the less maintainable the templates become. And this is where teams start asking a natural question: is there a way to define infrastructure without drowning in bloated files and endless merge conflicts? That’s the point where another option comes into focus—an approach designed with readability and modularity as the foundation, so scaling an environment doesn’t have to come at the cost of clarity.

The Bicep Revolution: What ARM Got Wrong, Fixed

What ARM made complicated, Bicep sets out to simplify. This is Microsoft’s answer to the pain points of ARM: a cleaner syntax, modular design, and an approach that feels like actual infrastructure code instead of pages of dense JSON. Bicep isn’t a wrapper or a bolt-on—it’s a rethinking of how Azure resources should be defined. At first glance it might look like another layer of tooling, but here’s the important distinction: Bicep compiles directly into ARM behind the scenes, so you get the same underlying deployments without touching JSON yourself. You write in Bicep, Azure interprets it the same way as an ARM template, and you sidestep the clumsy syntax. That means the benefits aren’t theoretical—they’re baked into how you write and maintain the files. The improvements show up immediately. Bicep uses a concise, readable format that cuts away the noise. An environment description that would stretch across a long ARM template is often written in a fraction of the length with Bicep. You can scan the whole thing without losing context, and reviewers can step through it without wading through endless curly braces. That reduction isn’t just about aesthetics; it lowers real-world friction in code reviews, pull requests, and day-to-day maintenance. Another major shift is modules. With ARM, repetition is normal—you copy and paste blocks of JSON for every variation of a common pattern. In Bicep, you can package a set of resources into a module once, then reuse it across files and teams. Think of it like moving from typing out every screw and hinge in a diagram to snapping in place a prebuilt block. Network setups, security groups, storage definitions—these become standardized building blocks instead of reinvented code. The payoff is consistency, and fewer opportunities for mistakes to slip in. And let’s talk about the biggest time suck with ARM: syntax errors. We’ve all been there—one missing comma and the whole file fails, with error messages that don’t help. Bicep eliminates those traps. The language design removes classes of JSON errors and shifts your focus back to building resources instead of debugging punctuation. It feels much closer to working in a proper programming language, where the effort goes into logic and design, not formatting. On top of that, Microsoft introduced Azure Verified Modules (AVMs). These are official, pre-published modules that cover common building blocks like VNets, storage, monitoring, and more. They’re reviewed, kept up to date, and designed to align with recommended practices. Instead of every team reinventing the same patterns, AVMs give you reliable starting points you can trust, while still letting you adapt them as needed. It’s a way of adding guardrails without forcing you into a locked-down framework. Now, instead of me telling you another story about a company that migrated to Bicep, the better approach is to show you. In the video, we’ll put a small ARM snippet side by side with the equivalent Bicep file so you can see the difference yourself. It’s not just fewer lines; it’s about clarity, readability, and maintainability. Watching the two versions will make obvious why teams that switch describe the experience as freeing up time they used to waste on troubleshooting. The larger point here is simple: Bicep fixes the issues that ARM left unresolved. Cleaner syntax makes files understandable. Modules allow reuse and stop endless duplication. Verified modules provide shared patterns that standardize deployments across organizations. And the syntax itself strips away the error-prone traps that cost teams time and focus. It’s a set of practical improvements aimed squarely at the problems people actually hit every day. In short, Bicep reduces repetitive work, makes templates readable, and enables reuse—so teams spend time building, not troubleshooting. And once you unlock that level of consistency in your infrastructure, it leads directly into the next challenge: how to take those advantages and use them to future-proof deployments across an entire organization.

Future-Proofing Your Azure Deployments

Future-proofing your Azure deployments starts with one simple realization: consistency matters more than complexity. Most organizations find themselves repeating the same work in slightly different ways—networks set up with small differences, mismatched firewall rules, naming conventions that don’t line up. The outcome is predictable: infrastructure that looks similar on the surface but behaves differently in practice. Instead of gaining clarity from infrastructure as code, teams end up managing dozens of one-off variations that introduce both delays and risk. This is where Azure Verified Modules step in. These modules are built around Microsoft’s recommended patterns and give teams a trusted baseline to build from. Rather than starting with a blank file or recycling patched JSON, you begin from modules designed to follow standard practices. This doesn’t lock you in—it simply means the basics, like storage encryption or network security groups, follow a proven approach from the beginning. From there, teams can focus their energy on adapting configurations to their specific needs instead of redoing core definitions. Private modules extend that same benefit when organizations need to handle internal requirements. You can host private modules so that compliance rules, monitoring integrations, or custom configurations are shared across teams without being manually duplicated into every template. This keeps organizational logic consistent while protecting sensitive internal standards, and it gives central IT a practical way to distribute reusable code without slowing down delivery. The benefits show up quickly. Standard modules reduce the spread of unique templates. Every team works from the same set of building blocks, which minimizes subtle configuration differences that turn into problems months later. Onboarding is smoother, because new hires don’t need to decode a backlog of JSON; they only need to understand the shared modules the organization relies on. Central updates also become easier—fix a rule or update a dependency once in the module, then redeploy, instead of chasing down changes across dozens of separate files. This approach also has a direct impact on reducing drift. Instead of patching quick fixes into individual templates that are hard to track, the changes happen at the module level. That creates a single, verifiable source of truth that aligns environments automatically. Teams stop juggling duplicate configurations and spend less time resolving errors that came from differences between files. The whole process leans away from reactive troubleshooting and back toward active delivery. Think about how many of your current templates repeat nearly identical patterns. A virtual network here, a storage account there, a load balancer configured almost exactly like the last one—but written again from scratch. Now imagine extracting that pattern into a reusable module once, then calling it whenever you need it. It doesn’t just save time in the moment; it steadily builds a catalog of proven components the organization can trust. That catalog becomes the backbone of faster, safer deployments across the company. This isn’t about grand, sweeping change. It’s about adopting small, repeatable improvements that build momentum. Start with one of your most-used ARM templates. Convert it into a modular Bicep template. Use that as the first entry in your team’s library, then expand from there. The process scales naturally—one module saves a few hours, ten modules save weeks, and over time the pattern reshapes how the entire organization deploys infrastructure. Staying on large, monolithic ARM templates keeps teams in a maintenance-heavy model: slow reviews, duplicate code, and ad hoc fixes that compound risk. Moving toward a modular approach with Bicep shifts that balance—less noise, fewer inconsistencies, and a workflow that actually supports scaling without the same drag. And that brings us to the larger point. The story of ARM versus Bicep isn’t just about syntax or tooling—it’s about efficiency, reliability, and growth. Once you see how much time and consistency you gain through modular, future-ready practices, it’s hard to justify staying tied to the old way. Because the question isn’t whether modular deployments work better; the question is why you’d keep holding on to oversized, fragile templates when the lighter, faster option is already here.

Conclusion

Many teams find ARM templates slow and fragile in practice, while Bicep offers a cleaner, more maintainable way to manage Azure resources with less effort. The contrast isn’t about theories or buzzwords—it’s about whether your deployments stay consistent and manageable over time. If you’re curious, start small: take a non-critical template, rewrite it in Bicep, and run the deployment. That single experiment will show whether the simpler workflow fits your team. And if this breakdown helped, drop a comment with your toughest ARM pain point, and don’t forget to like and subscribe so I know to keep making guides like this.

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit m365.show/subscribe