A CTO told me they’re building their own fine-tuned version of an open-source model to reduce vendor dependency.

I asked: Why?

The answer was: “So we’re not locked into one vendor.”

That’s the right concern. The execution is usually backwards.

Building your own fine-tuned model doesn’t make you independent. It makes you dependent on a different thing: your ability to maintain it. And that’s a dependency most enterprises underestimate.

Independence Isn’t About Which Model You Use

Strategic independence in AI is a real problem. I’ve written about it already (vendor dependency, lack of abstraction layers). But what independence actually requires is often misunderstood.

Most organizations think independence means: “Don’t use foundation models. Build or fine-tune your own.”

That’s treating the symptom, not the disease.

The disease is structural dependence on any single infrastructure or capability that you can’t replicate if the vendor relationship changes. The cure is designing your systems so that core capabilities either: a) exist across your organization as institutional knowledge, b) can be replaced without rebuilding the entire application, or c) are decoupled from your core business logic.

Building your own fine-tuned model can accomplish this. But so can building abstraction layers. So can designing for model rotation. So can maintaining real technical depth in how your models work.

The question isn’t “should we build or buy.” The question is “what do we need to maintain control over, and how do we actually maintain it?”

What Independence Actually Costs

Let me be concrete about what it means to build and maintain your own fine-tuned models.

First: initial build cost. You need engineers who understand model training, which means data scientists with 5+ years of experience. If you need to build from scratch (not fine-tune), you’re talking $1-3M in development cost, 6-12 months of timeline, and a team of 4-8 people.

Second: ongoing compute costs. A foundation model API costs you based on usage. Running your own model costs you compute (GPU infrastructure, maybe $100K-500K annually depending on scale) plus the electricity, networking, and datacenter costs that come with that.

Third: maintenance burden. When a new model comes out that’s better, should you retrain? When your domain shifts and your training data becomes stale, should you retrain? When you discover bias in your model, how do you fix it? Each of these is an active maintenance project, requiring someone to be responsible for it.

Fourth: talent concentration. You now have a capability that only a few people in your organization understand deeply. If your model expert leaves, or is overloaded, or moves to a different team, you have a problem. You’ve traded vendor dependency for person dependency.

Fifth: opportunity cost. While you’re building and maintaining your own model infrastructure, you’re not building the applications that use it. You’re spending engineering time on infrastructure that, for most organizations, a vendor could provide more efficiently.

The argument for building your own typically goes: “This gives us independence.”

The reality usually is: “This trades one dependency for another, often a more expensive one, and concentrates risk in a different place.”

When Building Your Own Actually Makes Sense

There are scenarios where building your own model is the right answer:

Scenario 1: Proprietary advantage through data. You have data that nobody else has access to, and that data gives you competitive advantage. You’re not just using a foundation model as a starting point. You’re incorporating proprietary data to create a capability that competitors can’t easily replicate. The privacy and competitive protection from keeping this internal is worth the build cost.

Example: A financial services firm with 50 years of internal transaction history that becomes part of the model. A manufacturing firm with sensor data from their equipment that’s unique to their process.

This is a real reason. But notice: you still might use a foundation model as a base and fine-tune it with your proprietary data. You don’t necessarily need to train from scratch.

Scenario 2: Extreme scale or cost sensitivity. You’re running this model so many times that the marginal cost of API calls is expensive relative to running your own infrastructure. You’ve done the math, and your compute costs are 30% lower running your own.

Example: A company processing millions of requests per day where each API call costs money, and running your own infrastructure has better economics.

This is a real reason. But notice: you still need the expertise to maintain the infrastructure. And you need to be comfortable with the operational burden.

Scenario 3: Regulatory or data residency requirements. Your industry requires that models be trained on data that never leaves your infrastructure. You can’t send data to a third-party API. You need the model to run in your environment.

Example: A healthcare provider with patient data, a financial institution with transaction data, a government agency with classified data.

This is a real reason, and it’s absolute. You have no choice. But notice: you might still use a foundation model as a base (open-source, self-hosted, or fine-tuned on your infrastructure) and then add your domain-specific fine-tuning.

In all three scenarios, the justification is about data (proprietary, sensitive, scale-related). Not about “independence from vendors.”

What Independence Actually Requires (If You’re Not Building Your Own)

If you’re not building your own model, but you still want strategic independence, here’s what actually works:

First: Design abstraction layers that are model-agnostic.

Instead of writing code against a specific model vendor’s API, write code against an abstraction. Different models can satisfy that abstraction. It’s more complex. It’s less efficient (you can’t optimize for vendor-specific quirks). But if your primary model changes, you can migrate without rebuilding.

This is the most practical approach for most organizations. It gives you real independence without the cost and complexity of building your own. This applies whether you’re building on any cloud AI platform, open-source models, or proprietary APIs—the principle is architectural, not about any specific vendor.

Second: Maintain real technical depth about how your models work.

Don’t treat models as black boxes. Understand what they’re doing, why they work, what they fail at. This requires people who can debug models, understand prompt behavior, interpret outputs. If you have people who really understand models deeply, you’re not dependent on any single vendor because you have the skill to work with alternatives.

Third: Build model rotation into your roadmap.

Plan for changing models not as a crisis scenario, but as a regular architectural activity. Every 18-24 months, do an evaluation of foundation models. Test alternatives. Understand switching costs. Keep up relationships with multiple vendors. When you do need to switch, you’re not starting from zero.

Fourth: Keep the model separate from your application logic.

The model is a component, not the core. Your application has business logic that’s independent of which foundation model it’s using today or tomorrow. The model is pluggable. This architecture costs more upfront but gives you real flexibility later.

Fifth: Maintain financial optionality.

Budget for alternatives. Set aside 5-10% of your AI budget for evaluating options. Build organizational practices where model choice is a strategic decision, not a habit. If your only model is because it’s the one you started with, that’s dependency. If your primary model is chosen and re-evaluated regularly, that’s strategy.

Why This Matters For Your Board

Strategic independence in AI infrastructure will matter more as enterprise AI becomes more central to business operations. Right now, most organizations are still experimenting. Dependency feels theoretical.

Over the next two years, as AI systems become embedded in core business processes (customer service, decision-making, optimization), a vendor change becomes a significant operational event. Your systems, your teams, your customer-facing capabilities depend on that vendor’s continued cooperation and reasonable terms.

The organizations that will have optionality are the ones that designed for it from the start. Not the ones that panic later and decide to “build independence” by rewriting their infrastructure from scratch.

Building that independence doesn’t require building your own models. It requires:

• Architecture decisions that allow model swapping
• Technical depth so you understand models, not just use them
• Regular evaluation of alternatives so you don’t wake up dependent
• Clear separation between model and application
• A serious answer to “what would we do if this vendor changed terms?”

The organizations that get this right won’t necessarily switch models. They’ll just have the ability to, which means they’ll negotiate better, maintain strategic clarity, and adapt faster when the foundation underneath them shifts.

Independence is possible. It just doesn’t come from building your own model. It comes from designing your systems as if you might need to change it.