Endpoints

In simple terms, an endpoint is a defined address where you send a request and get a response from a system. It often looks like a web address (URL) that a program can call to ask for data, start a process, or send information.

In artificial intelligence, endpoints are how apps connect to AI models in the cloud or on a server. For example, a chatbot, image analyzer, or recommendation engine is usually exposed through a specific endpoint that receives input (like text or images) and returns AI-generated output.

Understanding endpoints is essential because they are the bridge between your product and AI capabilities. Knowing how they work lets teams integrate models quickly, control costs, secure data, and iterate faster. With clear, well-designed endpoints, AI becomes a reusable, modular service instead of a complex, one-off integration.

To use an AI endpoint, your app sends a request with the needed data and settings, and then waits for a response. Typically this is done over HTTP using methods like POST or GET, and the data is sent and received in formats such as JSON.

For example, your product might:

• Send a user question to a language model endpoint.
• Receive a suggested answer in the response.
• Display that answer in your interface.

Developers configure authentication keys, request headers, and payloads so that the endpoint can process the input safely and efficiently.

Imagine you want to add automatic email drafting to your app. You connect to a text-generation endpoint provided by an AI platform. Your app sends a request like: “Write a friendly follow-up email about yesterday’s demo, in under 150 words.” The endpoint processes this prompt, runs it through a language model, and responds with a complete email draft. Your app then shows the draft to the user, who can edit or send it.