Estimate your monthly AI API spend by model, token usage, and call volume. AI spending up 340% YoY — know what you're actually burning.
AI API costs follow a counterintuitive pattern: per-token prices have fallen dramatically, but total spend is rising faster than ever. GPT-4 cost $60 per million tokens in early 2023. GPT-4o today costs $2.50 input and $10 output — a 95%+ price reduction. But developer teams are running 100x more calls. The result is that AI API budgets are growing 300–400% year-over-year even as individual calls get cheaper. Without real tracking, teams routinely underbudget by 3–5x.
Everything in LLM pricing is measured in tokens — roughly three-quarters of a word in English. A typical user message is 50–200 tokens. A long document analysis might be 10,000–50,000 tokens. A system prompt setting up an AI agent might itself be 500–2,000 tokens. Input tokens (what you send) and output tokens (what the model generates) are priced separately, and output is always more expensive — typically 3–5x the input rate. The length of model responses matters enormously for cost. A model configured to respond in 500 words costs roughly twice as much as one configured for 250 words on the same task.
By May 2026, the major providers have settled into a clear three-tier structure:
Most teams underestimate caching. If 30% of your API calls repeat the same question — common in customer service, FAQ, and search applications — a caching layer can cut your API bill by 20–35% with zero quality impact. Anthropic's prompt caching reduces the cost of repeated system prompts by up to 90%. OpenAI's batch API is 50% cheaper than synchronous calls for non-real-time use cases. These optimizations do not require switching models — they are infrastructure changes that compound over high call volumes.
The highest-leverage cost decision is model selection per task type. A frontier model answering "what is the capital of France?" is paying $25/M for a task a $0.40/M model handles perfectly. Routing architectures — systems that classify incoming queries and send simple ones to budget models and complex ones to frontier models — can reduce average per-call cost by 60–80% while maintaining quality on the tasks that matter. For teams spending over $500/month on AI APIs, this analysis typically pays back in the first month.