do I need a batch inference pipeline?

Maybe—but only if your use case fits it.

You likely need a batch inference pipeline if:

• You score large volumes of records on a schedule (hourly, daily, weekly).
• Low latency doesn’t matter; results can arrive minutes or hours later.
• You want to separate model scoring from your online app.
• You need cost efficiency for high-throughput inference.
• You process files, tables, or data warehouse extracts rather than live requests.
• You need to re-run inference on historical data or backfills.

You probably don’t need one if:

• Predictions must be returned immediately to a user/request.
• Your traffic is small and ad hoc.
• Your model is only used in a single application flow with real-time needs.
• You can run scoring inline in a service or notebook without operational complexity.

A simple rule:

• Real-time decisions → online inference
• Large scheduled jobs → batch inference

If you want, I can help you decide based on your specific setup. Just tell me:

1. how often you score data,
2. how much data you score,
3. whether you need real-time responses,
4. where the data lives.