Last week at AWS re:Invent, amid many product announcements and cloud messages, AWS introduced AWS AI Factories. The press release emphasizes accelerating artificial intelligence development with Trainium, Nvidia GPUs, and reliable, secure infrastructure, all delivered with the ease, security, and sophistication you’ve come to expect from Amazon’s cloud. If you’re an enterprise leader with a budget and a mandate to “do more with AI,” the announcement is likely to prompt C-suite inquiries about deploying your own factory.

The reality warrants a more skeptical look. AWS AI Factories are certainly innovative, but as is so often the case with big public cloud initiatives, I find myself asking who this is actually for—and at what ultimate cost? The fanfare glosses over several critical realities that most enterprises simply cannot afford to ignore.

First, let’s get one uncomfortable truth out of the way: For many organizations, especially those beholden to strict regulatory environments or that require ultra-low latency, these “factories” are little more than half measures. They exist somewhere between true on-premises infrastructure and public cloud, offering AWS-managed AI in your own data center but putting you firmly inside AWS’s walled garden. For some, that’s enough. For most, it creates more headaches than it solves.

Innovative but also expensive

AWS AI Factories promise to bring cutting-edge AI hardware and foundation model access to your own facilities, presumably addressing concerns around data residency and sovereignty. But as always, the devil is in the details. AWS delivers and manages the infrastructure, but you provide the real estate and power. You get Bedrock and SageMaker, you bypass the procurement maze, and, in theory, you enjoy the operational excellence of AWS’s cloud—homegrown, in your own data center.

Here’s where theory and practice diverge. For customers that need to keep AI workloads and data truly local, whether for latency, compliance, or even corporate paranoia, this architecture is hardly a panacea. There’s always an implicit complexity to hybrid solutions, especially when a third party controls the automation, orchestration, and cloud-native features. Instead of true architectural independence, you’re just extending your AWS dependency into your basement.

What about cost? AWS has not formally disclosed and almost certainly will not publish a simple pricing page. My experience tells me the price tag will come in at two to three (or more) times the cost of a private cloud or on-premises AI solution. That’s before you start factoring in the inevitable customizations, integration projects, and ongoing operational bills that public cloud providers are famous for. While AWS promises faster time to market, that acceleration comes at a premium that few enterprises can ignore in this economy.

Let’s also talk about lock-in, a subject that hardly gets the attention it deserves. With each layer of native AWS AI service you adopt—the glue that connects your data to their foundation models, management tools, and development APIs—you’re building business logic and workflows on AWS terms. It’s easy to get in and nearly impossible to get out. Most of my clients now find themselves married to AWS (or another hyperscaler) not because it’s always the best technology, but because the migrations that started five, eight, or ten years ago created a dependency web too expensive or disruptive to untangle. The prospect of “divorcing” the public cloud, as it’s been described to me, is unthinkable, so they stay and pay the rising bills.

What to do instead

My advice for most enterprises contemplating an AI Factories solution is simple: Pass. Don’t let re:Invent theatrics distract you from the basics of building workable, sustainable AI. The hard truth is that you’re likely better off building your own path with a do-it-yourself approach: choosing your own hardware, storage, and frameworks, and integrating only those public cloud services that add demonstrable value. Over the long term, you control your stack, you set your price envelope, and you retain the flexibility to pivot as the industry changes.

So, what’s the first step on an enterprise AI journey? Start by honestly assessing your actual AI requirements in depth. Ask what data you really need to stay local, what latency targets are dictated by your business, and what compliance obligations you must meet. Don’t let the promise of turnkey solutions lure you into misjudging these needs or taking on unnecessary risk.

Second, develop a strategy that guides AI use for the next five to ten years. Too often, I see organizations jump on the latest AI trends without a clear plan for how these capabilities should develop alongside business goals and technical debt. By creating a strategy that includes both short-term successes and long-term adaptability, it’s much less likely you’ll be trapped in costly or unsuitable solutions.

Finally, look at every vendor and every architectural choice through the lens of total cost of ownership. AWS AI Factories will likely be priced at a premium that’s hard to justify unless you’re absolutely desperate for AWS integration in your own data center. Consider hardware life-cycle costs, operational staffing, migration, vendor lock-in, and, above all, the costs associated with switching down the line if your needs or your vendor relationships change. Price out all the paths, not just the shiny new one a vendor wants to sell you.

The future has a bottom line

AWS AI Factories introduce a new twist to the cloud conversation, but for most real enterprise needs, it’s not the breakthrough the headlines suggest. Cloud solutions, especially those managed by your cloud provider in your own house, may be easy in the short term. However, that ease is always expensive, always anchored to long-term lock-in, and ultimately much more complex to unwind than most leaders anticipate.

The winners in the next phase of enterprise AI will be those who chart their own course, building for flexibility, cost-efficiency, and independence regardless of what’s splashed across the keynote slides. DIY is harder at the outset, but it’s the only way to guarantee you’ll hold the keys to your future rather than handing them over to someone else—no matter how many accelerators are in the rack.