An AI API is the interface that lets your own software send a request to a hosted AI model and receive its output, without you having to run the model yourself. In practice you send a prompt over the internet, the provider runs it through a model on their hardware, and you get the response back a moment later. It is the standard way apps add AI features today: you write a little code, call the endpoint, and the heavy machinery stays on the provider side. This explainer covers what a request looks like, how billing works, and when to call an API versus running a model in-house.
How an AI API works
An API — application programming interface — is simply an agreed way for two programs to talk. An AI API applies that to a model. Your app makes a request, usually containing your prompt and a few settings; the provider processes it and returns the result. From your code it feels like calling any other web service. In fact, most AI APIs are a specialized REST API: a defined endpoint you send structured data to and get structured data back.
The model itself is usually a large language model. The API hides all of its complexity behind one call.
What a request and response look like
| Part |
What it carries |
| Endpoint |
The URL you send the request to |
| Auth key |
A secret that proves you are allowed to call it |
| Prompt or messages |
The instruction or conversation you send in |
| Parameters |
Settings like length limit or randomness |
| Response |
The model output, plus token usage counts |
You send the prompt and parameters, you get text (or an image, or audio) back. The response also reports how many tokens you used, which is what you pay for.
How pricing works
Most AI APIs bill per token, where a token is a chunk of text roughly a few characters long. You pay for both the tokens you send (input) and the tokens the model returns (output), and output usually costs more. That means cost scales directly with how much you ask and how much the model says. Long prompts, large documents stuffed into context, and chatty responses all add up.
Practical levers to control spend:
- Trim the prompt — send only the context the model actually needs.
- Cap the output — set a sensible maximum response length.
- Cache repeats — do not re-ask the same question every time.
- Pick the right model tier — a smaller, cheaper model often handles simple tasks fine.
API versus running your own model
For almost every normal application, calling a hosted API wins. Running a model yourself means provisioning GPUs, loading model files, handling scaling, and keeping it updated — real work for an uncertain payoff. Self-hosting starts to make sense only with strict data-privacy rules, very high steady volume where per-token cost adds up, or a need to customize the model deeply.
What to skip
- Do not self-host to save money on day one. The infrastructure usually costs more than the API until you are at serious scale.
- Do not leave your API key in client-side code. Keep secrets on your server.
- Do not send more context than needed. You pay for every token, so bloated prompts quietly raise the bill.
FAQ
What is an AI API in simple terms?
It is a way for your software to send a prompt to a hosted AI model and get its response back over the internet, so you do not have to run the model yourself.
How much does an AI API cost?
Most charge per token, billing both the text you send and the text the model returns. Costs scale with usage, so longer prompts and longer answers cost more.
Do I need to host the model myself?
No. The point of an API is that the provider hosts and runs the model. You only write code to call it.
Is an AI API the same as a regular API?
It works the same way — a defined endpoint you send data to and get data back — but the response is generated by an AI model rather than read from a database.
Where to go next
Learn what a REST API is, understand the language model behind the endpoint, and see how an AI workflow chains API calls together.