Realtime AI API

ProductionProtocols and integrationPublished By Simon Budziak

A realtime AI API keeps a live connection open so an application can exchange audio, text, events, and model output with minimal delay. Unlike a single request and response call, it supports ongoing sessions, streamed results, interruptions, and state changes needed for responsive voice and multimodal experiences.

What makes an AI API realtime?

The client and model service exchange small events over a persistent connection instead of waiting for a complete request. The session can accept new input while output is still arriving, which enables barge-in and live tool updates. OpenAI’s Realtime documentation describes audio, text, and event based sessions using WebRTC or WebSocket transports.

What must a production integration control?

Set a latency budget for capture, transport, inference, and playback. Reconnect logic must preserve the right conversation state without replaying completed actions. Log session events through LLM observability, apply permission checks before tools run, and test streaming inference under packet loss and interrupted speech rather than only on a stable developer connection.

Frequently asked questions

Is a realtime AI API the same as ordinary token streaming?

No. Token streaming returns one response incrementally, while a realtime API maintains a session with two-way events and changing input.

Which transport does a realtime AI API use?

Implementations commonly use WebRTC or WebSocket connections, depending on whether the client is a browser, phone, or server.

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