2 posts tagged with "batch-processing"

Most teams building with LLMs optimize for the wrong workload. They obsess over time-to-first-token, streaming latency, and response speed — then discover that 60% or more of their LLM API spend goes to nightly summarization jobs, data enrichment pipelines, and classification runs that nobody watches in real time. The latency-first mental model that works for chat applications actively sabotages these offline workloads.

The batch LLM pipeline is the unglamorous workhorse of production AI. It's the nightly job that classifies 50,000 support tickets, the weekly pipeline that enriches your CRM with company descriptions, the daily run that generates embeddings for new documents. These workloads have fundamentally different design constraints than real-time serving, and treating them as slow versions of your chat API is where the problems start.

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.