Batch API

We want models to feel cheap. Not because they are—but because we can spread the cost out. A Batch API helps. It groups requests together, so the system runs them in one shot. Think of it as carpooling for inference.

Why batch matters

Running one prompt at a time is like driving alone. Fast but pricey. Batch lets us throw a hundred prompts in the same trunk. The model chews through them together, and we pay less per ride. We don’t lose accuracy; we just waste less gas.

Batch jobs

Batch jobs are just work lists. We queue up prompts, send them off, and come back later. No waiting with a spinning wheel. She processes the pile while we sleep, then hands us the results. Good for big data passes—classification, tagging, translation.

Offline inference

Some tasks don’t need answers now. That’s where offline inference comes in. We push jobs into storage, schedule her to run overnight, and collect the outputs in the morning. It’s cost-efficient because GPUs aren’t idling for our impatience.

Scaling the habit

The trick is to think in groups. Don’t build features that need answers one at a time unless users are staring at the screen. Bundle everything else. Batch APIs keep her busy, and we get lower bills without losing speed where it matters.

We keep learning: AI isn’t just fast math—it’s also about planning our traffic. Turns out scaling is less magic and more logistics.