What is an AI API?
An AI API (Application Programming Interface) is a web service that lets developers integrate AI model capabilities into their applications without running the model themselves. Instead of hosting a large language model or image generator locally, developers send requests to the API and receive model outputs in return.
Why It Matters
AI APIs are how most businesses actually use AI. Running frontier models requires specialized hardware costing millions — APIs make these capabilities accessible to any developer for pennies per request. The OpenAI API, Anthropic API, Google Gemini API, and others have created an entire ecosystem of AI-powered products built on top of foundation models.
How It Works
A typical AI API interaction:
- Authentication — the developer uses an API key to identify themselves and their usage quota
- Request — send a structured request with the prompt, model selection, and parameters (temperature, max tokens, etc.)
- Processing — the API provider runs the model on their infrastructure
- Response — receive the model's output (text, image, embeddings, etc.) in a structured format (usually JSON)
Common AI API patterns:
- Chat completions — send a conversation history, get a model response (OpenAI, Anthropic, Google)
- Embeddings — convert text to vector representations for search and retrieval
- Image generation — send a text prompt, receive a generated image
- Audio — transcription (STT), text-to-speech (TTS)
- Function calling / tool use — the model returns structured function calls for the application to execute
Pricing models:
- Per-token — pay for input and output tokens (e.g., $3/$15 per million tokens)
- Per-image — pay per generated image
- Per-minute — pay for audio processing time
- Rate limits — requests per minute and tokens per minute caps
Example
A developer building a customer support chatbot sends a POST request to the Anthropic API with the customer's question and conversation history. The API returns Claude's response in JSON format within seconds. The developer never needs to manage GPUs, model weights, or inference infrastructure — they just pay per token.