Batch and real-time integration patterns
The first key decision when evaluating batch versus real-time integration approaches is around data immediacy – when exactly do you need the GenAI outputs? This boils down to whether you require responsive just-in-time results, like servicing on-demand user queries, versus use cases where insights from model outputs can accumulate over time before getting consumed.
Let’s illustrate this with an example of RAG, where LLMs evaluate search results to formulate human-friendly query responses. This is a real-time use case; you need AI-generated answers with minimal latency to deliver a quality user experience in applications like conversational assistants or search engines. The data has to be put into action as it is produced.
Contrast that with something like automated content generation workflows, for example, extracting metadata from a product catalog. While you still want that content quickly, there’s more flexibility around when model outputs get ingested downstream. You could run generative models in batches, queueing up prompts and processing them asynchronously based on available capacity. The generated texts then flow into your e-commerce databases on their own schedule.
The real-time interactive integration mode prioritizes low latency and responsive experiences above all else. Users receive AI results virtually instantly through request/response app interfaces. Batch mode disconnects that coupling, sacrificing instantaneous interactivity for higher overall throughput and cost efficiency at scale. Longer-running jobs optimize utilization across pooled models.
So, the batch versus real-time decision depends on analyzing data freshness requirements. For experiences demanding perceivable instant gratification, like querying information or iterating on creative ideation, you’ll want a request-scoped interactive architecture. But when targets are more about maximizing generative model output volume with flexible latency tolerances, then batching prompts yields better economics. Getting that integration pattern right is key to harnessing GenAI’s value.
