Batch Inference

Batch Inference is the practice of sending many inputs to a model in a single request and getting all the results together, instead of one at a time. It trades a little latency for much higher throughput and lower cost per prediction.

How it works

1. 1Prepare batch: collect multiple inputs and group them together.
2. 2Send batch: send the entire batch to the model in one request.
3. 3Model processes: the model runs all inputs in parallel.
4. 4Return batch: all outputs come back together in a single response.
5. 5Use results: consume the results for analysis, automation or downstream tasks.

Key points

• Higher throughput — process thousands of inputs in one pass.
• Lower cost by reducing per-prediction overhead.
• Better GPU/TPU utilisation because the hardware stays busy.
• Choose batch size carefully: too big can cause memory issues, too small wastes throughput.

Why it matters

For large workloads — data pipelines, moderation at scale, bulk translation — batch inference is dramatically cheaper and faster than calling the model one item at a time. It's the default for offline and high-volume jobs.

Frequently asked questions

When should I use batch vs single inference?

Use single (real-time) inference for interactive, low-latency needs; use batch for high-throughput, offline or bulk jobs.

Does batching hurt latency?

For any single item, yes slightly — but total time for the whole set drops sharply because the hardware is used efficiently.