What is an AI API? Definition and business implications

It is a technical interface that lets an application send requests to an AI model hosted by a provider and retrieve its responses. It is the standard way to access AI in the enterprise, as opposed to hosting the model locally, which is reserved for cases with strong sovereignty or volume constraints.

Almost always pay-per-use, based on the tokens consumed at input and output, with no fixed subscription. Technically, the application sends an HTTPS call authenticated by a secret key to a provider endpoint, which runs the inference and returns the response, often streamed token by token.

Three at a minimum: inference cost (ideally broken down by use case, not just a monthly total), the actual latency seen by users, and the API error rate. For an industrial SME integrating an assistant into its ERP, that meant about 280 euros per month for 2,000 daily calls, 1.1 seconds to first response and 0.4% errors over three months. Without these metrics, the deployment is flown blind.

Because what matters is the latency actually experienced by the user, distinct from the raw latency the provider advertises. An application latency degrading to 4 seconds kills adoption, even if the model itself responds in 1 second. It must therefore be measured on the user side, not only on the API side.

Mainly in how API keys are managed. A single key shared by all developers is a major security risk. The good practice is to isolate keys by user or by use, and to know at all times where they are stored and who can use them.