The models worth using change every few months. If your product calls one provider's SDK from forty places, you can't take advantage of that — and you inherit their outages and their pricing.
A thin layer between your product and whoever is serving the model. It costs a week to build and saves the rewrite every time the landscape shifts.
One internal interface, several providers behind it. Switching models becomes a config change rather than a refactor — and this field changes fast.
Token streaming, cancellation, and partial results, so a response that takes eight seconds doesn't feel like the page has frozen.
Per-tenant budgets, caching for repeated queries, and routing cheap work to cheap models. AI bills scale with usage in a way most teams model badly.
Who can use which capability, on which data, with limits enforced server-side rather than trusted to the client that calls it.
A record of prompts, outputs, and decisions with a retention policy that satisfies legal without keeping customer data forever by accident.
Providers have outages and rate limits. A second route and a degraded-but-working path keep the feature usable instead of returning an error.
Nothing here is exotic. It's the ordinary engineering that gets skipped when an AI feature is built as a prototype and then quietly shipped.
If the AI feature is already live but was never built for production, that's the same job we describe on prototype to production.
Tell us what the feature does and which provider it's tied to today. We'll come back with the abstraction, the cost model, and what switching would take afterwards.