managing API infrastructure for multi-model geo pipelines
we've been pushing hard into geo lately. to make sure our brand can show up in those ai search overviews, i've been running a split workflow. I use qwen 3.6 to draft the copy so it sounds human and dodges spam filters, and then lean on gpt-5 to parse the raw search intent data and spit out the schema markup at scale. the strategy works, but managing the api infrastructure was turning into a mess. hitting direct apis for bulk geo ops just sucks. we had almost zero observability, so figuring out which specific prompt tests were burning our budget was basically impossible. juggling separate vendor invoices every month was annoying too. but the real killer was the timeouts. a random api drop mid-generation would just crash the script and leave us with broken datasets. we finally switch to llm gateway. using a single gateway centralized the billing into one dashboard, so we can finally see our exact token usage per model. the biggest win is the stability though. if a provider randomly drops, the auto failover just reroutes it to a backup model without stopping the whole run. how are you guys handling api routing and failovers when scaling these multi-model setups?