Building an AI MVP without overengineering it

There are two ways to build an AI MVP badly. One is the vibe-coded version that skips everything and breaks the first time a real user does something unexpected. The other is less talked about but just as common: the MVP that spends three weeks on infrastructure nobody has validated needs yet — a queue system for load that doesn't exist, a multi-model fallback chain for a feature nobody's confirmed anyone wants.

Both waste the thing an MVP exists to protect: the time before you know if this is worth building at all. Here's a rough map of what actually earns its place on day one, and what's a well-disguised way to avoid finding out.

Build this on day one

• Server-side API calls. Never a model provider key in the client bundle — this isn't a scaling concern, it's a five-minute fix that's much more expensive after the key leaks.
• A fixed evaluation set of 15–30 real examples, even a tiny one. Without it, every change to a prompt is a guess dressed up as a decision.
• Basic auth and authorisation, enforced server-side. Not enterprise SSO — just real checks on real endpoints, from day one, because retrofitting this later touches everything.
• One fallback state for the model call failing. It doesn't need to be sophisticated. It needs to exist, because model calls fail more often than most other dependencies.
• A data model that can represent the actual product, even if the implementation is simple. Undoing a wrong schema costs far more than undoing a missing feature.

Defer this until it's earned

• Multi-provider fallback chains. One reliable provider with basic retry logic is enough until you have actual data on provider outages affecting your users.
• A queue and worker system for background jobs. Most MVPs have order-of-magnitude fewer requests than the architecture that justifies this — a simple synchronous call or a basic cron job is usually enough.
• A custom admin dashboard. Query the database directly or use an off-the-shelf tool. Nobody has validated the workflow enough to know what the internal tool actually needs to do yet.
• Fine-tuning a model. Prompt engineering against a good evaluation set solves the large majority of early-stage quality problems for a fraction of the cost and iteration time.
• Microservices, or any architecture decision justified by 'scale' before you have users to scale for.

The infrastructure that looks responsible is often the infrastructure that's avoiding the actual risk, which is that nobody wants this yet.

The test that separates the two lists

Ask, for anything you're about to build: does skipping this risk data loss, a security hole, or an unrecoverable bad experience for an early user? If yes, it's day-one work. If the honest answer is 'it would be more efficient at scale' or 'a bigger team would want this,' it can wait — you don't have the scale or the team yet, and building for them before they exist is a bet, not a requirement.

The uncomfortable part of this discipline is that it means shipping something you know isn't the final architecture. That's correct. An MVP's job is to answer whether the product is worth the final architecture at all — over-building it before that question is answered is just a slower, more expensive way to find out the same thing a scrappier version would have told you in half the time.