Short answer
AI APIs expose model inference over HTTP so applications can send prompts or media and receive generations, embeddings, or transcriptions at scale.
Why it matters
Most product teams integrate AI through APIs. Auth, rate limits, token pricing, safety layers, and observability decide production readiness.
Article
Prod
Separate keys by environment; never ship secrets in clients.
Examples
Support draft reply API
A helpdesk backend sends ticket text plus retrieved policy chunks to a chat model, then shows a draft to an agent for approval before sending.
Semantic FAQ search
An app embeds help articles once, embeds each user query at runtime, retrieves top matches, and optionally summarizes with a second model call.
FAQ
What is an embedding API used for?
Embeddings map text (or other inputs) to vectors for similarity search, clustering, and RAG retrieval. They do not generate free-form answers by themselves.
How should I estimate cost?
Use provider tokenizers or counting tools on representative prompts, multiply by expected traffic, and include output tokens, retries, and evaluation runs. Monitor production with budgets and alerts.
Should I call model APIs from mobile apps directly?
Avoid shipping long-lived secret API keys in clients. Prefer a backend that enforces auth, quotas, and prompt templates.
Sources
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OpenAI API reference
OpenAIOfficial API reference for widely used model endpoints.
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Anthropic API docs
AnthropicOfficial Claude API documentation.
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Google AI Gemini API docs
GoogleOfficial Gemini API documentation for developers.