AI APIs explained: keys, pricing, free tiers and what to choose

An AI API is the bridge between your product and a model. Instead of opening a chat interface, your app sends a request to a provider and receives text, code, image, audio or structured data back. That is how AI chatbots, support widgets, writing tools, coding assistants and automation platforms connect to models behind the scenes.

What an AI API actually does

Most providers expose the same basic pattern: you create an account, generate an API key, send a prompt or file to an endpoint, and get a response. The key identifies your project and lets the provider count usage, apply rate limits and charge the right account.

• For chat and text generation, the API returns an answer, summary, plan, translation or JSON object.
• For image and video models, the API usually returns a generated file URL or a job ID that you poll until the result is ready.
• For embeddings, the API turns text into vectors so you can build search, recommendations and retrieval systems.

How pricing works

Text models are usually billed by tokens: small pieces of text in the prompt and in the answer. Image, speech and video models are more often billed by generation, seconds, resolution or compute time. Free tiers can help you test an idea, but production apps need budget limits, logging and fallbacks.

• Set daily or monthly usage caps before connecting the API to public traffic.
• Store API keys on the server, not in frontend JavaScript or mobile app code.
• Log prompts, costs and errors in a way that does not expose private user data.

Popular AI API providers

OpenAI is a common choice for chat, structured output, agents and multimodal tasks. Anthropic Claude is strong for long documents, careful writing and coding workflows. Google Gemini fits teams already using Google Cloud, search and Workspace integrations. Mistral, DeepSeek and other providers can be attractive when price, open weights or regional availability matter.

Free and low-cost options

Free API credits change often, so treat them as testing budget rather than a long-term plan. For experiments, also look at local or open-source models through Ollama, LM Studio or cloud platforms that host Llama-family and diffusion models. They can reduce vendor lock-in, but you still pay with hardware, latency or setup time.

How to choose

• Choose a flagship model when answer quality matters more than cost.
• Choose a cheaper fast model for drafts, classification, routing and bulk processing.
• Choose an open or local model when you need more control over data and deployment.
• Choose a provider with strong docs and SDKs if you are building the first version quickly.

A simple first request

POST /v1/chat/completions
Authorization: Bearer YOUR_API_KEY
 
{
  "model": "your-model",
  "messages": [
    { "role": "user", "content": "Summarize this support ticket in one sentence" }
  ]
}

The exact endpoint differs by provider, but the principle is the same: send clear input, request a predictable output format, handle errors and keep your key private.