Inference / NAVIN RESEARCH
When to Use Batch Inference for AI Workloads
Move delay-tolerant, independent jobs to batches while preserving identifiers, retries, and quality checks.
Move delay-tolerant, independent jobs to batches while preserving identifiers, retries, and quality checks.
Short answer: Batching is appropriate when tasks do not depend on immediate responses and can be processed independently: offline classification, embeddings, backfills, and evaluation runs. Keep interactive and dependent agent loops on synchronous paths.
Implementation steps
- Set a service-level deadline for the workload.
- Create stable item IDs and idempotent output writes.
- Group compatible model and configuration requests.
- Reconcile partial failures rather than rerunning an entire batch blindly.
Validation checklist
- Verify one output per input ID.
- Measure queue time and completion distribution.
- Compare price and operational overhead with synchronous traffic.
Common mistakes
- Batching multi-step tasks whose next input depends on the prior output.
- Losing item identity in concatenated prompts.
- Ignoring cancellation and data-retention requirements.
Practical decision
Ship the smallest design that passes the checks above. Record the model, prompt, tool, dataset, and runtime versions used in testing so later changes can be compared rather than guessed. Recheck the linked documentation before relying on provider-specific limits, model names, prices, or preview features; those details change more quickly than the engineering principles in this note.
