LLM API integration patterns for REST backends: sync, async, and streaming
Choosing between synchronous responses, queued jobs, and SSE/WebSockets when exposing LLM features from a Laravel REST API — latency, cost, and UX trade-offs.
Short completions can stay synchronous with aggressive timeouts. Long reports belong in queued jobs with a poll endpoint or webhook — the same pattern as heavy exports in enterprise Laravel apps.
Streaming improves perceived latency for chat UIs but complicates error handling and billing. If you stream, log token usage per chunk and handle client disconnects without leaking workers.
Cache stable prefixes where safe (system prompts, tool schemas). Never cache user-specific PII in shared keys. Redis helps rate limiting per tenant as much as caching.
These patterns bridge “AI product demo” and “API our mobile app depends on” — the core of AI-related backend roles for US and EU remote teams.