Guide · 2026-04-11 · 6 min read
Batch API Pricing: The 50% Discount Most Teams Ignore
Most LLM providers offer batch endpoints at half the cost of real-time APIs. Here's when to use them and how to architect your pipeline for maximum savings.
What Are Batch APIs?
Most major LLM providers offer two ways to make API calls:
1. Real-time (synchronous): Send a request, get a response in seconds. Standard pricing.
2. Batch (asynchronous): Submit a file of requests, get results back within 24 hours. 50% off.
The tradeoff is simple: if you don't need instant responses, you can cut your costs in half.
Provider Batch Pricing
Output tokens get the same 50% discount, so total savings are consistent across input-heavy and output-heavy workloads.
Ideal Use Cases
Content Generation Pipelines
If you're generating product descriptions, blog drafts, or marketing copy, there's rarely a need for real-time responses. Queue them up, submit as a batch, process results the next morning.
Example: An e-commerce company generating 10,000 product descriptions/week saved $2,400/month by switching to batch.
Data Extraction & Classification
Processing invoices, categorizing support tickets, extracting entities from documents — these are perfect batch workloads. The data isn't going anywhere; a few hours of latency is irrelevant.
Evaluation & Testing
Running model evaluations, A/B testing prompts, or benchmarking across models generates thousands of API calls. Batch pricing makes experimentation affordable.
Nightly Reports & Summaries
Daily digest emails, analytics summaries, or weekly reports can all be generated via batch during off-peak hours.
How to Implement
OpenAI Batch API
Submit the file, poll for completion, download results. Most SDKs handle this with a few lines of code.
Architecture Pattern
The key component is the batch aggregator — it collects individual requests over a time window (e.g., 1 hour), bundles them into a single batch file, and submits to the API.
When NOT to Use Batch
- Chat applications — users expect instant responses
- Real-time search — latency matters for UX
- Streaming use cases — batch doesn't support streaming
- Low volume — if you're making < 1,000 requests/day, the operational complexity isn't worth the savings
Hybrid Architecture
The smartest teams use both: real-time for user-facing features and batch for everything else. A typical split might be:
- 30% real-time (chat, search, autocomplete) → full price
- 70% batch (reports, extraction, generation) → 50% off
Effective blended discount: 35% off total spend.
Compare batch vs. real-time costs for your models on our Pricing Table.
Related Reading
- API Pricing Strategies — Batch is one piece — see how it fits with committed use and provisioned throughput.
- 5 Tactics That Cut Our LLM Costs by 50% — Batching is tactic #3 — combine it with the other four.
- Prompt Caching Explained — Stack caching on top of batch pricing for compound savings.
- Subscription vs. API — Still on a subscription? See when switching to API (with batch) saves more.