Guide · 2026-04-18 · 5 min read

Introducing the Batch API Savings Calculator

Most teams using OpenAI or Anthropic are paying 2× what they need to. We built a calculator that shows your exact savings in seconds.

The 50% Discount Hiding in Plain Sight

OpenAI and Anthropic both offer a feature most production teams either don't know about or never get around to implementing: the Batch API. Submit your requests, get the results back within 24 hours, pay exactly 50% less than the standard rate.

That's it. No volume commitments. No special contracts. No quality difference — same models, same outputs, half the price.

So why is almost no one using it?

The Mental Model Problem

Most engineers think of LLM APIs as real-time. You hit an endpoint, you get a response in two seconds, you ship it to a user. That model is correct for chat interfaces, copilots, and search — but it's wrong for the majority of production LLM work.

A typical AI-heavy product has a mix of workloads:

Real-time — chat replies, autocomplete, semantic search
Near-real-time — moderation queues, intent classification on incoming events
Async — nightly summarization, document enrichment, eval pipelines, dataset generation, classification backfills, embeddings re-indexing

The async bucket is usually 40-80% of total token volume. Every one of those requests qualifies for the 50% Batch API discount. Most teams are paying full price for all of it.

What "Async-Eligible" Actually Means

A request is async-eligible if your product can tolerate a delay between submitting it and using the result. The actual SLA from both providers is "within 24 hours" — in practice, most batches finish in minutes to a few hours.

Common workloads that fit:

Document enrichment — extract entities, summaries, or tags from new content overnight
Bulk classification — categorize support tickets, label training data, score sentiment
Eval and regression pipelines — run your test suite against a new model version
Fine-tuning dataset generation — synthesize training examples
Background analysis — generate weekly insights, customer health scores, churn signals
Re-processing — apply a new prompt to historical data

If a human won't be staring at the result the moment it's generated, it probably qualifies.

Why We Built the Calculator

Telling someone "you should be using Batch API" is forgettable. Showing them they're leaving $2,400 a month on the table is not.

We built the Batch API Savings Calculator to do exactly that. You enter:

The model you're using
Your average input and output tokens per request
Your daily request volume
A slider for the share of requests that don't need real-time responses

The calculator shows your monthly and yearly savings as a single, dominant number — animated when it changes. Below that, a side-by-side comparison of standard vs. batch costs and a horizontal bar chart of the gap.

The state lives in the URL, so you can share your estimate with your team in one click.

What the Numbers Actually Look Like

A few realistic scenarios at current pricing:

The leverage is non-linear in two directions: high-volume workloads compound savings, and the more of your traffic that's async-eligible, the closer you get to a flat 50% reduction across the board.

How to Actually Switch

The implementation is straightforward — usually a one-line endpoint change plus a polling loop:

1. Identify async-eligible pipelines. Anything not gated on a user waiting in front of a screen.

2. Swap the endpoint. Both providers expose batch endpoints alongside their standard ones, accepting the same payload format.

3. Poll for completion. Submit a batch, get back a job ID, check it on a schedule. Most batches finish in well under an hour.

4. Wire results back into your pipeline. Handle the small share of failed requests with a retry on the standard endpoint.

That's the whole architecture change. Most teams that try it ship the migration in an afternoon.

Official docs: OpenAI Batch API and Anthropic Batch API.

The Caveat

Batch API is not a silver bullet. It's wrong for:

Anything user-facing in real time
Workflows where downstream steps depend on immediate results
Models or providers that don't expose a batch tier (currently Google, Mistral, Groq, and most open-source hosts)

The point isn't to migrate everything — it's to find the slice of your traffic that genuinely doesn't need to be real-time and stop overpaying for latency you don't use.

Try It

Pull up the Batch API Savings Calculator, drop in your real numbers, and see what the gap looks like at your scale. If it's a number worth chasing, the implementation is one of the cheapest cost-optimization wins in the LLM stack.