Comparison
Batch API vs Offline Evaluation
Batch API and Offline Evaluation are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Batch API
Any time the work is bulk, async, and not user-facing — embedding pipelines, evals, synthetic data, batch labeling.
Generating embeddings for 10M support tickets via OpenAI Batch: $0.05 / 1M tokens instead of $0.10, completed overnight.
When you would reach for Offline Evaluation
Offline Evaluation comes up when the question is fundamentally about evaluation.
A RAG team's offline eval: 500 (question, gold answer) pairs, scored by LLM-as-judge on faithfulness and relevance, run on every prompt PR.
Frequently asked
What is the difference between Batch API and Offline Evaluation?
Batch API: Batch APIs (OpenAI, Anthropic) accept up to 50K LLM requests in a single submission, run them asynchronously over hours, and return results at ~50% of the synchronous price. The cheap option for bulk processing. Offline Evaluation: Offline evaluation runs a fixed dataset of inputs through a candidate model or prompt, scores each output, and reports aggregate quality — the standard way to compare changes before shipping.
When should I use Batch API vs Offline Evaluation?
Any time the work is bulk, async, and not user-facing — embedding pipelines, evals, synthetic data, batch labeling. Offline Evaluation applies when you are focused on evaluation.
Are Batch API and Offline Evaluation the same thing?
No. Batch API is inference; Offline Evaluation is evaluation. They are related but address different parts of the AI stack.