I'm building a batch scoring workflow for ML models - should I use hosted inference?

Maybe — but for batch scoring, hosted inference is usually not the first choice unless you need one of a few specific benefits.

Use hosted inference if you want:

• Fast setup / low ops overhead: no need to manage serving infra.
• Simple, occasional, or low-to-medium batch jobs: e.g. nightly scoring runs.
• Model versioning / deployment management handled for you.
• Autoscaling during bursty workloads.
• Consistent online-like environment for a model you already deploy for real-time use.

Prefer self-managed batch processing if:

• You’re scoring large datasets regularly.
• You need cost efficiency at scale.
• You want fine control over:
  • CPU/GPU allocation
  • parallelism
  • retries and checkpointing
  • input/output formats
  • scheduling
• You need to run custom preprocessing/postprocessing over entire files or partitions.
• Your workflow is more like ETL + ML scoring than request/response inference.

Rule of thumb

• Small/medium, convenience-focused batch jobs → hosted inference can be fine.
• Large-scale, recurring, cost-sensitive batch scoring → build a batch pipeline using:
  • job orchestration (Airflow, Prefect, Dagster, etc.)
  • distributed compute (Spark, Ray, Kubernetes jobs, serverless batch)
  • model loaded once per worker for many records

Common pattern

A lot of teams do this:

1. Train model
2. Deploy it to hosted inference for real-time
3. Use a separate batch scoring pipeline for offline predictions

That gives you the best of both worlds.

Practical decision

Choose hosted inference if you answer “yes” to most of these:

• Do I need predictions only in a managed endpoint?
• Is the batch volume modest?
• Is time-to-launch more important than cost/performance tuning?
• Do I already use this endpoint for online inference?

If not, build a batch job instead.

If you want, I can help you choose based on:

• dataset size
• latency/throughput requirements
• cloud provider
• model type
• whether you need GPUs