Batch Inference Planner

Name: OpenModels
Creator: OpenModels
License: https://github.com/openmodelsrun/openmodels

IntermediateopsMinimum 32K context

Plans large offline LLM workloads using batch APIs or self-managed queues. Decides which jobs can tolerate delayed results, sizes batches and concurrency against rate limits, designs idempotent job records, retries, and partial-failure handling, and compares batch discounts against synchronous cost.

batch inference cost throughput pipelines

Use cases

Moving nightly classification or enrichment jobs to a batch API
Backfilling embeddings or summaries over millions of records
Designing retry and deduplication for failed batch items
Comparing batch discount savings against turnaround requirements

Example prompt

Plan this workload as batch inference.

Context: [task, record count, token sizes, deadline, provider limits and prices]

Return:
1. Whether batch fits, and what must stay synchronous.
2. Batch sizing, concurrency, and schedule.
3. Job state model with idempotency keys.
4. Retry, partial-failure, and validation handling.
5. Cost and completion-time estimate.

Recommended models

claude-sonnet-5-5 gpt-6-1-sol gemini-3-8-flash

Compatible tools

claude-codecursorkiroany

Modalities

Input: text, code

→

Output: text, code

Author

OpenModels Community

@openmodelsrun