Model the exact financial return of switching model tiers, turning on prompt caching, or running jobs through 24-hour batch queues.
Optimization Levers
Monthly Budget Savings
Equal to $37,728.00 in annual cloud savings.
Savings Breakdown by Lever
Migrate simple extraction, classification, and FAQ workflows from flagship models to balanced or budget tiers like GPT-5.6 Terra or Luna.
Typical Savings: 60% – 85%Place system prompts and reference context first in your payload to exploit automatic KV cache hits across OpenAI, Anthropic, and Gemini.
Typical Savings: 50% – 90% (Input)Enqueue non-urgent synthetic generation, embeddings, and analytics passes through async 24-hour batch queues.
Typical Savings: Flat 50%The calculator models your total monthly input and output token consumption based on your target request volume and average tokens per request. It then compares your current model against a target alternative, factoring in official cached-input discounts and 50% batch API processing multipliers.
In production chatbots and RAG setups, realistic cache hit rates range between 60% and 85%. This assumes a static system prompt (>1,024 tokens) and consistent retrieved context chunks positioned at the beginning of the message payload.
Yes. Providers like OpenAI and Anthropic allow prompt caching discounts to stack with Batch API pricing, delivering compounding discounts of over 85% on large-scale asynchronous workloads.
Output tokens are priced 3× to 6× higher than input tokens across virtually all major providers due to the autoregressive generation computation required. Even small reductions in output length yield disproportionate savings.