Multi Model AI Procurement Is Now the Responsible Default

Building your product around a single AI provider is a concentration risk, and I no longer think it’s defensible for a B2B company to run that way by default.

Key takeaways

• Depending on one AI lab exposes a company to their pricing, safety, and outage decisions
• The competition between OpenAI and Anthropic for enterprise customers should be used, not picked a side in
• Multi model architecture is now achievable with modest engineering effort for most SaaS products
• Vendor concentration in AI carries the same governance weight as any other single supplier risk
• Hedging across providers protects margin, uptime, and negotiating leverage all at once

Through FirstMotion, my AI Search and GEO agency for B2B SaaS, I sit inside the model layer of client products every week, watching which providers actually hold up under real production load. I’ve also sat on the other side of this as a founder, having built and exited both Obby and Baluu, where every vendor dependency eventually became a board conversation. This piece is about why I now tell every founder I advise through VAQA to build for more than one model provider from the start.

Why single provider dependency is a real risk

Every company that has bet its product entirely on one AI lab has quietly accepted three risks it probably wouldn’t accept anywhere else in its supply chain. It has accepted that provider’s pricing decisions, whatever they turn out to be. It has accepted that provider’s safety and policy calls, even when they change the product’s behaviour overnight. It has accepted that provider’s outages, on that provider’s timeline, with no fallback.

Any procurement team would flag that combination for a hosting provider or a payments partner. AI is treated differently mostly because it’s new, not because the underlying risk is any smaller.

The OpenAI and Anthropic contest is a signal, not a choice

Watching OpenAI and Anthropic compete directly for enterprise customers over the past couple of years has been instructive. Both have pushed hard on enterprise features, pricing structures, and safety commitments aimed squarely at winning long term corporate contracts.

I read that competition as a signal to hedge, not as an invitation to pick a permanent side. The moment a company commits fully to one lab’s roadmap, it loses the leverage that competition was meant to hand it in the first place, and it starts absorbing risk that a multi provider setup would have spread out.

What multi model procurement actually requires

Founders often assume multi model architecture means a major rebuild. In my experience advising on AI implementation, it’s usually far smaller than that, provided the product wasn’t built with hard coded assumptions about a single provider’s API from day one.

The practical requirements are fairly consistent across the B2B SaaS products I’ve worked with:

1. An abstraction layer between your application logic and any specific model provider’s API
2. At least one qualified fallback model for every production use case, tested and ready
3. A routing decision, whether by task type, cost, or latency, made deliberately rather than left to whichever model was integrated first
4. Regular testing of the fallback path so it isn’t discovered to be broken during an actual outage

None of that requires betting against the leading labs. It just means not betting everything on one of them.

What concentration risk costs when it shows up

Concentration risk doesn’t announce itself gradually. It tends to show up all at once, usually at the worst possible moment: a pricing change that hits margin overnight, a policy update that breaks a workflow customers depend on, or an outage that takes the product down with no fallback in place.

I’ve watched companies scramble to build a fallback integration in the middle of an actual incident, which is the single most expensive time to do that work. The engineering cost of building it in advance is a fraction of the cost of building it under pressure while customers are watching the product fail.

A board or investor asking about this dependency for the first time during a crisis is a bad conversation to have, and it’s an entirely avoidable one.

Hedging is the responsible default now

I don’t think multi model procurement is a nice to have anymore. For any B2B SaaS company with real customer dependence on its AI features, it’s the baseline standard of responsible technical governance.

The labs themselves are, quite reasonably, trying to win exclusive or near exclusive relationships with enterprise customers. That’s their commercial incentive, and I don’t fault them for pursuing it. It just means the discipline of resisting that pull, and keeping genuine optionality across providers, has to sit with the company buying the technology, not the company selling it.

Treat model choice the way you’d treat any other critical vendor relationship: with a primary, a qualified alternative, and a plan for the day the primary doesn’t work.

Through my AI Implementation advisory work at VAQA, I help founders and leadership teams design AI architecture that doesn’t leave them exposed to a single provider’s decisions. If your product’s AI layer is built around one lab and you haven’t stress tested what happens if that relationship changes, get in touch and I’ll walk through what I advise on in more detail.

Frequently Asked Questions

Does multi model procurement mean using multiple models in production at once?

Not necessarily at the same time for the same task. It usually means having a qualified, tested alternative ready for each critical use case, even if only one model handles that task in production on a normal day.

Is switching between OpenAI and Anthropic difficult from an engineering standpoint?

It’s more straightforward than most founders expect if there’s already an abstraction layer between the application and the model provider’s API. Without one, it’s harder, which is exactly why building that layer early matters.

How does VAQA help with AI vendor risk specifically?

I work with founders and leadership teams under my AI Implementation advisory pillar to assess model dependency, design fallback architecture, and bring that risk into normal governance conversations rather than leaving it unmanaged.

Isn’t sticking with one AI provider simpler and cheaper to manage?

It’s simpler in the short term and more expensive in the long term once you account for the pricing, outage, and policy risk you’re carrying without a fallback. The extra engineering cost of a second provider is usually small compared with the risk it removes.

Should early stage startups worry about this before they have real scale?

Yes, though the level of investment should match the stage. Building the abstraction layer early, even before adding a second live provider, saves a much larger rebuild later once the product depends heavily on one model’s behaviour.

What’s the FirstMotion connection to this topic?

FirstMotion is my AI Search and GEO agency for B2B SaaS, and its work sits inside the model layer of client products constantly, which is where I see provider dependency and its consequences first hand.

Tom Batting is a Forbes 30 Under 30 entrepreneur, founder of Obby and Baluu, and founder of FirstMotion. He advises founders and leadership teams through VAQA.