The Batch LLM Pipeline Blind Spot: Queue Design, Checkpointing, and Cost Attribution for Offline AI
· 12 min read
Most production AI engineering advice assumes you're building a chatbot. The architecture discussions center on time-to-first-token, streaming partial responses, and sub-second latency budgets. But a growing share of real LLM workloads look nothing like a chat interface. They look like nightly data enrichment jobs, weekly document classification runs, and monthly compliance reviews over millions of records.
These batch pipelines are where teams quietly burn the most money, lose the most data to silent failures, and carry the most technical debt — precisely because the latency-first mental model from real-time serving doesn't apply, and nobody has replaced it with something better.
