Why AI Cost Optimisation Matters
As AI-powered applications scale, LLM API costs can quickly become the largest variable expense in your infrastructure budget. A single GPT-4o call costs roughly 15–25× more than the equivalent GPT-4o mini call. For high-volume applications handling thousands of requests per day, the difference between a naive "always use the best model" approach and an intelligent routing strategy can be the difference between profitability and unsustainable burn.
These calculators help developers, founders, and engineering teams make data-driven decisions about model selection, prompt caching, and storage architecture — before committing to expensive API contracts or infrastructure investments.
Model Routing: The Biggest Lever
LLM routing — directing simple queries to cheaper models and complex queries to capable ones — consistently delivers 40–70% cost reduction in production systems. The key insight: most real-world query distributions have a "long tail" of simple requests (factual lookups, short summaries, classification) that do not require frontier model capabilities. Routing these to GPT-4o mini, Claude Haiku, or Gemini Flash dramatically reduces per-request costs without user-visible quality degradation.
Prompt Caching: Immediate Savings
Anthropic, OpenAI, and Google all offer prompt caching — reusing the KV cache from a previous request when the prompt prefix is identical. For applications with long system prompts or repeated context (RAG chunks, tool definitions, conversation history), caching reduces input token costs by 50–90%. The savings compound quickly at scale.