Batch API Savings Calculator

What it does

The major LLM providers — Anthropic, OpenAI, Google, DeepSeek — all offer a Batch API variant that trades synchronous response time for a flat 50% discount on input and output tokens. The economic logic: batch jobs let providers schedule inference opportunistically across cluster capacity, packing requests into otherwise-idle GPU slots and amortizing infrastructure differently than the real-time path. For customers, the tradeoff is response time — batch jobs typically return in 1-6 hours, with a 24-hour SLA cap. So the question for any workload is: do you actually need the response in the next second, or could you accept “sometime within 24 hours” for half the cost?

The calculator takes your monthly token volume (input + output, per provider) and shows the dollar savings of switching eligible workloads to batch. For a workload spending $5,000/month on Sonnet at standard rates, batching the asynchronous portions would save up to $2,500/month — meaningful for any AI-heavy product. Workloads that batch well: bulk classification or labeling (every record is independent, doesn’t need live response), nightly summarization of documents/conversations/transactions, embedding generation for vector indexes, prompt evals and benchmarks (you’re testing across hundreds of variants), training-data synthesis, and content moderation queues where 1-6 hour latency is acceptable.

What does NOT batch: any user-facing synchronous interaction (chat, search, completion-as-you-type), real-time agents, streaming responses, anything triggered by a user click and showing a loading spinner. Most production LLM apps split into hot and cold paths: hot path uses real-time API for user-facing requests, cold path uses batch for asynchronous work. Done right, this can cut overall AI costs by 30-60% with no UX degradation. Provider-specific notes: Anthropic batch caps at 100K requests per batch, returns within 24h; OpenAI batch returns within 24h; Google batch returns within 24h; DeepSeek batch is similar with slightly tighter SLAs.

How to use it

1. Enter your monthly input + output token volume per provider.
2. Mark which workloads can tolerate 1-24h latency (bulk classification, embeddings, summarization, evals).
3. Read the 50% savings calculation across all four providers.
4. Compare to current spend — split-path architectures (hot real-time + cold batch) typically save 30-60% overall.
5. Plan the migration: tag your async workloads, queue them through the batch endpoint instead of streaming API.

When to use this tool

• Estimating savings before adopting Batch API for cold-path workloads.
• Justifying a batch-pipeline architecture to engineering leadership with concrete dollar numbers.
• Comparing batch economics across the 4 major providers (Anthropic, OpenAI, Google, DeepSeek).
• Annual budget planning — projecting AI spend with split hot/cold architecture.

When not to use it

• Real-time user-facing workloads — never batch what users wait for in a UI.
• Streaming responses (chat) — batch endpoints don’t support streaming output.
• Workloads requiring tool use / function calling with multiple synchronous turns — batch is single-request only.
• Tiny token volumes (<$50/month) — savings are real but operational complexity often isn’t worth it for small spend.

Common use cases

• Onboarding a colleague who needs the same calculation/conversion
• Verifying a number or output before passing it on
• Quick calculation during a typical workday
• Pre-decision sanity-check on inputs and outputs