OpenAI, Anthropic or Google: choosing a model provider without betting the business
The honest answer to 'which AI company should we use?' is: build so the question stops mattering. Here's what that looks like, and how we choose per task in the meantime.
OpenAI, Anthropic and Google all ship excellent models, leapfrog each other quarterly, and price aggressively. Treating the choice as a marriage is the mistake; treating it as plumbing is the win.
How do OpenAI, Anthropic and Google compare for business?
OpenAI has the broadest ecosystem and the most developer familiarity: the default choice, in both the good and lazy senses. Anthropic's Claude models have a strong reputation for long-document work, careful instruction-following and natural prose, and the company has pushed hard on agentic tooling (M22 compared them in detail here). Google's Gemini models are priced sharply, integrate deeply with Workspace, and their cheap tiers are excellent for high-volume work. All three offer UK/EU-compatible commercial terms with no training on your API data.
What architecture lets you switch AI provider easily?
- One thin routing layer. Every AI call in your systems goes through a single internal interface. Swapping provider becomes a configuration change, not a rebuild.
- Task-level routing. Classification and extraction go to cheap fast models; drafting to a mid-tier; the genuinely hard reasoning steps to a premium model. In a well-routed system, most of the volume goes to models costing pennies per task.
- Your own eval set. Twenty to fifty real examples from your business (your emails, your documents, your edge cases) with known good answers. When a new model ships, run the set, read the failures, decide in an afternoon. Without this you're choosing on vibes and blog posts.
- Prompts as assets. Keep prompts versioned and provider-neutral where possible; they're business logic, not throwaway strings.
What does AI vendor lock-in actually look like?
Rarely the API; those are easy to swap. Real lock-in hides in provider-specific features with no equivalent elsewhere, fine-tuned models you can't port, and data living inside one vendor's tooling. None are fatal, but each should be a deliberate trade, not an accident.
The uncomfortable truth: for most business automation, the difference between the top providers matters far less than the quality of the system around the model: the data it can see, the approval points, the measurement. Which is convenient, because that part you get to own.
How does M22 help with this?
M22 Consultancy is model-agnostic: M22 builds on whichever model fits each task, Claude, ChatGPT or Microsoft Copilot, which is the per-task choice this article describes. The AI Audit prices each opportunity before anything gets built, and the process map and ranked opportunities are the client's to keep regardless of who builds the fix. The AI Audit starts at £1,500, a fixed fee agreed before day one. Book a thirty-minute call to talk through which model fits your systems, or see more on M22 Consultancy.