Batch Inference Cost Calculator
Calculate cost savings of batch vs real-time API inference from volume and latency tolerance. Enter values for instant results with step-by-step formulas.
Formula
Savings = (Total_RT_Cost) - (Batch_Cost + Remaining_RT_Cost)
Total real-time cost is calculated from all requests at the standard rate. Mixed cost combines batchable requests at the discounted batch rate with remaining real-time requests at the standard rate. The difference is your savings.
Worked Examples
Example 1: SaaS Company Batch Optimization
Problem: A company makes 200,000 API calls/day at 400 tokens each, $0.03/1k tokens. 60% can be batched at 50% discount.
Solution: Total tokens/day = 200,000 x 400 = 80,000,000\nAll real-time cost = (80,000,000 / 1,000) x $0.03 = $2,400/day\n\nBatchable: 120,000 requests x 400 tokens = 48,000,000 tokens\nBatch cost = (48,000,000 / 1,000) x $0.015 = $720/day\n\nReal-time remaining: 80,000 requests x 400 tokens = 32,000,000 tokens\nRT cost = (32,000,000 / 1,000) x $0.03 = $960/day\n\nMixed cost = $720 + $960 = $1,680/day\nSavings = $2,400 - $1,680 = $720/day
Result: Daily savings: $720 | Monthly savings: $21,600 | 30% cost reduction
Example 2: Startup with High Batch Potential
Problem: A startup runs 50,000 inference calls daily, 800 tokens average, $0.06/1k tokens. 80% batchable at 50% discount.
Solution: Total tokens/day = 50,000 x 800 = 40,000,000\nAll RT cost = (40,000,000 / 1,000) x $0.06 = $2,400/day\n\nBatchable: 40,000 requests x 800 = 32,000,000 tokens\nBatch cost = (32,000,000 / 1,000) x $0.03 = $960/day\n\nRT remaining: 10,000 x 800 = 8,000,000 tokens\nRT cost = (8,000,000 / 1,000) x $0.06 = $480/day\n\nMixed = $960 + $480 = $1,440/day\nSavings = $2,400 - $1,440 = $960/day
Result: Daily savings: $960 | Monthly savings: $28,800 | 40% cost reduction
Frequently Asked Questions
What is batch inference and how does it differ from real-time inference?
Batch inference processes multiple requests together in a single job rather than handling each request individually in real time. Real-time inference returns results within milliseconds to seconds, making it suitable for interactive applications like chatbots or live recommendations. Batch inference accepts higher latency, often processing requests over minutes to hours, but at significantly reduced cost. Cloud providers typically offer 50% or greater discounts for batch processing because it allows them to schedule workloads during off-peak times, use spot instances, and optimize GPU utilization more efficiently across their infrastructure.
What types of workloads are suitable for batch inference?
Batch inference works well for any task where results are not needed immediately. Common use cases include nightly data analysis and report generation, bulk document summarization or classification, large-scale content moderation, email campaign personalization, product catalog enrichment with AI-generated descriptions, periodic sentiment analysis of customer reviews, and pre-computing recommendations. If your application can tolerate latency of minutes to hours, batch processing can dramatically reduce costs. A good rule of thumb is that any task scheduled to run on a timer rather than triggered by a user action is a candidate for batch inference.
How are batch inference costs typically calculated?
Most API providers charge batch inference per token processed, similar to real-time inference, but at a discounted rate. For example, if a provider charges $0.03 per 1,000 tokens for real-time inference, they might charge $0.015 per 1,000 tokens for batch processing, representing a 50% discount. Some providers also factor in priority levels where lower-priority batch jobs get even steeper discounts. The total cost equals (number of requests multiplied by average tokens per request divided by 1,000) multiplied by the per-1,000-token rate. Additional costs may include storage for input and output data during batch processing.
What is the optimal batch size for cost efficiency?
Optimal batch size depends on your provider, model, and latency requirements. Larger batches generally provide better per-unit economics because of reduced overhead per request. Most providers recommend batches of at least 1,000 to 10,000 requests to maximize discount tiers. However, extremely large batches may take longer to process and increase the risk of partial failures requiring retries. A practical approach is to batch requests that accumulate over your latency tolerance window. If you can tolerate 6-hour latency, collect all requests from a 6-hour window into a single batch for maximum efficiency.
What is the difference between training and inference?
Training is the process of teaching a model by adjusting weights using labeled data, which is computationally expensive and done once or periodically. Inference is using the trained model to make predictions on new data, which is faster and done repeatedly. Training costs are typically 10-100x higher than inference costs.
Can I use Batch Inference Cost Calculator on a mobile device?
Yes. All calculators on NovaCalculator are fully responsive and work on smartphones, tablets, and desktops. The layout adapts automatically to your screen size.