Comparison
Batch API vs Synthetic Data
Batch API and Synthetic Data 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 Synthetic Data
Synthetic Data comes up when the question is fundamentally about training.
Phi-3 trained heavily on textbook-quality synthetic data.
Frequently asked
What is the difference between Batch API and Synthetic Data?
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. Synthetic Data: Synthetic data is training data produced by a model — instructions distilled from GPT-4, code generated and filtered by tests, reasoning traces sampled from a stronger model — rather than handwritten by humans.
When should I use Batch API vs Synthetic Data?
Any time the work is bulk, async, and not user-facing — embedding pipelines, evals, synthetic data, batch labeling. Synthetic Data applies when you are focused on training.
Are Batch API and Synthetic Data the same thing?
No. Batch API is inference; Synthetic Data is training. They are related but address different parts of the AI stack.