AI Agent Batch vs Real-Time Cost Calculator
See when batch API discounts beat real-time APIs and when a local GPU is cheaper than either. Built for evals, backfills, and hybrid agent traffic.
Workload
Estimates are directional in USD. Last updated: 2026-07-08. See notes.
Cheapest option
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Cost breakdown
Comparison & ROI
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Provider batch discounts
| Provider | Batch discount | Batch input / 1M | Batch output / 1M | Typical turnaround | Best for |
|---|---|---|---|---|---|
| OpenAI Batch API | 50% | $1.25 | $5.00 | 24h | Large offline jobs, evals, backfills, content generation |
| Anthropic Batch API | 50% | $1.50 | $7.50 | 24h | Long-context research, document review, classification |
| Google Gemini Batch Prediction | 40% | $0.30 | $0.90 | 12h | High-volume, price-sensitive classification and extraction |
| Together AI Batch | 30% | $0.63 | $0.63 | 6h | Open-weight model fine-tuning inference at scale |
| Fireworks AI Batch | 25% | $0.68 | $0.68 | 4h | Fast open-weight batch inference |
Frequently asked questions
When should I use a batch API instead of real-time?
Use batch when latency does not matter: nightly evals, content backfills, classification jobs, data enrichment, and offline audits. Batch APIs typically offer 25–50% discounts because providers can schedule work during idle capacity.
How do I model a hybrid workload with both batch and real-time?
Set the batch share slider or input to the percentage of requests that can tolerate async turnaround. The calculator splits token cost between the provider's batch price and its real-time price, then compares the total against self-hosted GPU options.
Is a local GPU cheaper than batch APIs?
Local GPUs win at high, steady volume where utilization stays above ~60–70%. Batch APIs win for bursty, offline, or experimental workloads because you pay only for tokens and avoid hardware capex.
What hidden costs does this calculator include?
For self-hosted GPUs it includes amortized hardware cost, electricity, and an optional hourly labor estimate. For managed APIs it only counts token pricing; add your own integration and storage costs if significant.
Why does the batch share affect the GPU recommendation?
Batch jobs can be queued and run at high utilization, making owned hardware more efficient. Real-time traffic needs headroom and fast failover, which raises the effective cost per request on local GPUs.
Pricing and throughput estimates are directional benchmarks. Always verify current provider pricing before committing to a production architecture. Batch APIs may have different SLAs, latency guarantees, and availability regions than real-time endpoints.