LLM Streaming Under the Hood: SSE, Tokens, and Tradeoffs
When a chat interface shows an answer appearing word by word, the model usually is not sending a finished paragraph in pieces. The application is receiving a sequence of events while inference is still running. Understanding that path helps you choose a transport, handle failures, and avoid misleading latency measurements.
From prompt to first event
A typical streaming request passes through several stages. First, your client sends a prompt and enables streaming. An API gateway authenticates the request, applies routing and limits, then forwards it to the selected model. The model processes the prompt into tokens and begins generating output. As results become available, the serving layer packages them into events and sends them over the open connection.
The first visible text is not necessarily the first generated token. There can be time spent on network setup, queueing, prompt processing, and model prefill. Developers therefore track time to first byte and time to first token separately. The first measures when any response data arrives; the second measures when usable generated content reaches the application.
What a stream contains
Many APIs use Server-Sent Events (SSE), a text-based format carried over a long-lived HTTP response. The server writes event data as it becomes available. A client reads the response incrementally, parses each event, and appends its text delta to the interface. OpenAI-compatible streams commonly represent content as chunks with a delta field; Claude-style streams use named event types for message starts, content blocks, deltas, and completion.
These chunks are not guaranteed to match words, sentences, or model tokens. A network read may contain part of an event, multiple events, or a fragment of a multibyte character. Robust clients buffer incomplete data, parse complete event boundaries, and treat an explicit finish event or stream closure as distinct signals. They should also surface API errors that arrive after the HTTP response has begun.
SSE versus WebSockets
SSE advantages: It works over ordinary HTTP, is straightforward to inspect, and naturally fits one-way generation from server to client. Existing HTTP infrastructure and SDKs often handle it well. For a typical prompt-and-response workflow, it keeps implementation simple.
SSE drawbacks: The connection is mostly one-directional, and proxies or load balancers may buffer output unless configured to pass it through. Reconnection does not automatically resume model generation; the application needs its own request and replay strategy.
WebSocket advantages: It supports messages in both directions on one persistent connection. That can suit interactive voice, live transcription, or workflows where the client sends updates during generation.
WebSocket drawbacks: It adds connection lifecycle and scaling complexity, and can be less convenient with HTTP-focused tooling. For a standard text completion, its bidirectional capability may not justify the extra work. The right choice depends on interaction requirements, not on the assumption that one transport makes the model generate faster.
Latency, buffering, and backpressure
Streaming improves perceived responsiveness, but it does not necessarily reduce total generation time. Measure time to first token, tokens per second, and total completion time under realistic load. Disable response buffering where needed, flush event data promptly, and set sensible timeouts. If a client renders every tiny delta immediately, excessive UI updates can also become a bottleneck; batching updates for a short interval can keep the interface smooth.
Backpressure matters when the consumer reads more slowly than the producer writes. Use streaming readers instead of collecting the full response in memory, and cancel the request when the user stops generation. Cancellation can save compute and prevent abandoned work, though the client should still clean up its connection and state.
Choosing an API for streaming work
Before adopting a relay or provider, verify the behaviors your application depends on: streaming format, SDK compatibility, model availability, error reporting, and cancellation. 59API provides pay-as-you-go access to Claude Opus, Sonnet, Haiku, and Fable, as well as GPT models, through native official-quality models rather than downgraded substitutes. Its base URL is https://api.59api.com, and it is compatible with Claude Code, Codex, and OpenAI SDKs. That combination can make it a low-cost option for testing or running streaming applications, with a referral rebate also available.
Streaming is ultimately a chain of model inference, event framing, network delivery, and client parsing. Test each layer independently, compare transports against your actual interaction pattern, and choose a provider whose compatibility and pricing fit the workload. Developers evaluating lower-cost access can sign up with 59API and try the models through their existing compatible tools.