AI backend integration in Laravel: LLM APIs without sacrificing production discipline
How senior backend teams wire LLM providers into Laravel apps — HTTP clients, retries, timeouts, and guardrails — while keeping auth, data integrity, and observability non-delegable.
Adding an LLM to a Laravel product is not a front-end experiment — it is backend work. You need a provider client with explicit timeouts, idempotency keys where billing matters, structured logging, and a clear boundary between “model output” and “data the app trusts.”
I start with a thin integration service: one place that calls the provider, normalizes errors, and returns typed DTOs. Controllers stay thin; policies and Form Requests still own authorization. The model never writes directly to the database without a human-reviewed code path.
Rate limits, circuit breakers, and queue-backed jobs for long generations keep the API responsive. Feature flags let you ship to internal users first. This is the same discipline I use on Proxify client work — AI accelerates scaffolding; I own merges and production.
If you are hiring for AI backend or LLM integration roles, look for engineers who talk about guardrails and observability, not just prompt quality. Prompts matter; production outcomes matter more.