Response streaming has become the default UX pattern for AI applications, following ChatGPT's lead in displaying partial responses as they're generated rather than waiting for complete outputs. The technique splits into two main implementations: Server-Sent Events for simple one-way streaming, and WebSockets for bidirectional communication needed in complex workflows like multi-agent systems or code assistants. While streaming improves perceived responsiveness, it doesn't actually make model inference faster.
The streaming obsession reveals a fundamental misunderstanding about AI app performance. Builders focus on the last mile — how quickly users see text appear — while ignoring the real bottlenecks. Model selection, prompt optimization, and intelligent caching deliver actual latency improvements. Streaming just masks slow responses with better UX, which matters but shouldn't be your first optimization. We've seen too many teams implement elaborate streaming setups while their apps still take 8 seconds to generate a simple response.
What's missing from most streaming discussions is the infrastructure complexity it adds. SSE requires maintaining persistent connections, handling network interruptions, and managing state across partial responses. WebSockets are even more complex, requiring bidirectional message handling and connection lifecycle management. For most AI applications, this added complexity isn't justified — especially when proper prompt caching and model routing would deliver better performance gains with less engineering overhead.
For developers building AI apps: implement streaming after you've optimized your actual model performance, not before. Start with response caching, experiment with faster models for simple tasks, and optimize your prompts. Streaming should be your polish, not your performance strategy. Users notice the difference between a 2-second and 8-second response more than they notice streaming effects on already-fast responses.