Streaming API

Benched.ai Editorial Team

A streaming API delivers model outputs over an open connection (HTTP SSE, gRPC, WebSocket) as soon as they are available, enabling low-latency incremental consumption.

Protocol Comparisons

Protocol	Transport	Ordering Guarantee	Backpressure Support
HTTP Server-Sent Events	HTTP/1.1	Yes	Limited via client close
WebSocket	TCP	Yes	App-level flow control
gRPC streaming	HTTP/2	Yes	Built-in windowing

Use Cases

Chat interfaces needing real-time token display.
Speech synthesis pipelines chaining ASR → LLM → TTS.
Long-running data generation consumed by downstream workers.

Design Trade-offs

Streaming reduces perceived latency but complicates retry logic.
Persistent connections may exhaust load-balancer slots.
Message framing overhead is higher than batch replies for small outputs.

Current Trends (2025)

HTTP/3 QUIC streams lowering head-of-line blocking¹.
CDN edge workers terminating SSE and multiplexing to origin.
Token-level billing calculated on the fly during stream.

Implementation Tips

Flush heartbeat comments every 15 s to keep connection alive.
Send final JSON frame with "done": true flag.
Cap maximum stream duration to defend against slow-loris attacks.

Cloudflare Blog, QUIC for Real-Time AI Streaming, 2025. ↩