AI proof of concept vs MVP difference: a POC proves the model can do the task on a sample. An MVP proves the product can ship to real users in your environment. Confusing them is the most common reason AI projects stall.
What a POC actually contains
- A Jupyter notebook or Python script.
- A handful of test inputs, often hand-picked.
- A model that gives 'good enough' answers in the demo.
- No eval set, no integration, no auth, no telemetry.
What an MVP actually contains
- Working slice in the customer's environment, integrated with at least one real system.
- Eval suite of 100+ cases running in CI on every change.
- Cost telemetry, retry logic, fallback chain, kill-switch.
- A runbook your on-call engineer can use at 3am.
- Real users (internal or external) interacting with the feature.
When you actually need a POC
- Genuine uncertainty about whether the model can do the task at all.
- Vendor selection between 2-3 model providers before you commit.
- Customer-funded feasibility study with a fixed deliverable.