If you missed it, Replit made OpenAI the default model provider for Replit Agent instead of Anthropic (Amjad Masad announced it at TechCrunch Disrupt on 16 October), and The Information then reported that most of Replit’s AI spend, a contract said to be worth more than $100M a year, moved over after OpenAI offered a discount of more than 30%. Replit hasn’t confirmed the terms, and Claude is still there if you pick it. But the default is what most users get, so for Anthropic that’s a big number walking out of the door. And, according to the same reports, Replit isn’t the only one: Rippling and DataCamp got similar offers. I’m not going to argue about which model is better (that changes every few weeks anyway, which is rather the point). What interests me is the question every CTO reading those headlines should be asking: if a competitor of my AI provider offered me 30% off tomorrow, could I actually take it?
Here’s the thing: a year or two ago the AI bill was a rounding error for most products. Nowadays, for anything with an agent in it, tokens can easily be one of the biggest lines on the cloud invoice. As such, the ability to switch providers isn’t an engineering nicety any more, it’s a negotiating position. Replit could only take that discount because its product could run on someone else’s model. If switching would have taken them two quarters of rewrites, the discount would have been academic.
And bear in mind it’s not only about price. Providers change rate limits, deprecate models, have outages, change their terms, or (as we’ve seen this month with discounts being withdrawn) change the commercial relationship. If one vendor going sideways can take your core feature down, that’s a single point of failure, and we’d never accept that for a database.
I had a look at our own code in Calendrz with this in mind, and I’ll be honest: it’s a mixed picture, which I suspect is true for most teams.
We have two AI features. The simpler one turns your free slots into a friendly paragraph you can paste into an email. It calls the Anthropic API directly, with the URL and the model right there in the class. Not pretty! But it has a plain-text fallback if the call fails, so the worst case of losing the provider is a less chatty message, and swapping it for another vendor is an afternoon’s work.
The Smart Scheduler (the agent you can email to book meetings) is a different story. On the plus side, the model name is configuration, not code, the system prompt lives in a Markdown file rather than in Java strings, every call goes through a single gateway interface, and there’s a circuit breaker so a provider outage degrades gracefully instead of piling up requests. On the minus side, the tool definitions and the conversation handling are built on the Anthropic SDK’s own types. That gateway interface was written for testability, not portability, so switching would mean rewriting the tool plumbing and, more importantly, re-testing every prompt against a new model. I’d guess days rather than weeks, but not an afternoon.
And that last point is the bit people underestimate. The API call is the easy part; you can wrap it in an hour. The real lock-in is in the prompts, the tool-calling quirks and the behaviour you’ve tuned without realising it. Every model reads instructions slightly differently, so a prompt that works beautifully on one can be subtly wrong on another, and “subtly wrong” for a scheduling agent means a meeting booked at the wrong time.
Don’t get me wrong, I’m not advocating some grand abstraction layer that supports every model on the planet. That’s how you end up with the lowest common denominator of all of them, plus a framework to maintain. Right tool for the right job. What I’d do instead:
The evaluation suite is the one I’d start with, by the way, because it pays off even if you never switch: it’s also how you know whether next month’s model upgrade from your current vendor quietly broke something.
Bottom line: you don’t have to switch providers, but you should be able to. Could you do it by Friday? I’d be very interested to hear how other teams have handled this, drop me a line.
Originally published on liviutudor.com.