What is an API?

Also called Application Programming Interface.

An API is a door between two systems. Your app asks for an order, a payment, or a model answer, and the other system replies in a structured way. People do not see the door. They see the feature that uses it. No API, or no permission to use it, means someone is still copying and pasting.

For AI, the API is how your product calls a model without pasting into a public chat. The questions that matter to you are commercial, not technical. What does each call cost. Where does the text go. What happens when the vendor is down. Can you cap spending. A demo key with no cap is how a test becomes a large invoice.

Think of it this way: An API is the drive-through window. You place your order through a defined interface. The kitchen handles everything behind the counter. You get the output without seeing or managing the process.

A CRM adds a button that summarizes a customer record. It sends the record to a rented model and shows the summary beside the record. The team did not have to host any AI of its own.

A product team prototypes by pasting customer emails into a chat window. It works, and the emails now sit in a personal account. The launch version calls the model through an API from the company's own systems, with a contract that says the vendor does not train on that text, and a monthly spend limit.

For accessing AI capability in your products without running your own models. The trade-off is convenience and speed against dependency on the provider's uptime, pricing, and data handling terms. When sending data to another company's system breaks your rules on where data may live. In that case, download a model and run it on servers you control.

RaftLabs prices the running cost before the build, so a feature people like does not become a loss. You get a number for a busy month, not only a demo. The related work on our side is API development.

This sits with the other deployment & economics terms on the glossary. What you pay to run AI, and the choices that change the bill. Worth reading next: Open vs Closed Models, Inference Cost, and Latency.