How to Calculate Your Monthly AI API Burn Rate
AI API costs are completely predictable — if you know the formula. This guide covers the exact calculation, a worked real-world agency example, budget benchmarks from MVP to enterprise, and the four mistakes that most commonly cause unexpected charges.
The Burn Rate Formula Explained
Every AI API billing event consists of three variables: how many API calls you make, how many tokens go in per call, and how many tokens come out per call. Input and output tokens are billed at different rates — output is typically 4–8x more expensive. The formula multiplies these variables and divides by 1,000,000 to convert from per-token to per-million-token pricing.
OutputRate) ÷ 1,000,000
Use this formula with any model. Swap in your chosen rates from the pricing comparison table, or run it interactively with the Burn Rate Calculator.
Step-by-Step Calculation Guide
Count your API runs per month
How many times does your application call the AI API in a typical month? Count unique API requests, not page views or sessions. A product with 200 clients each generating 25 AI outputs/month = 5,000 runs.
Estimate average input tokens per run
Add up: system prompt (200–2,000 tokens) + conversation history + injected context (0–5,000 tokens) + user message (50–500 tokens). Use 2,000 as a starting point. Log actual counts after your first 100 production calls.
Estimate average output tokens per run
Short one-liners or labels: 10–50 tokens. Structured outlines or code snippets: 300–800 tokens. Long-form outputs: 1,000–4,000 tokens. Use 500 as a baseline for mixed workloads.
Look up your model's input and output rates
Find per-1M-token pricing on your provider's pricing page. Check whether you qualify for cached input pricing — both OpenAI and DeepSeek offer automatic caching at 4–10x below standard input rates.
Plug into the formula and add a buffer
Calculate cost per run, multiply by monthly volume, then add 20–30% as a variance buffer. Real-world token counts almost always differ from estimates by 30–50%.
Worked Example: Content Agency
Imagine an agency utilising Claude 4.6 Sonnet to summarise and extract entities from 10,000 client PDFs every month.
- Avg PDF (Input): 15,000 tokens
- Avg Summary (Output): 800 tokens
- Claude Sonnet Pricing: $3.00/1M input, $15.00/1M output
Input Cost per run: (15,000 / 1,000,000) * $3.00 = $0.045
Output Cost per run: (800 / 1,000,000) * $15.00 = $0.012
Total per run: $0.057
Budget Benchmarks by Scale
If you aren't sure how many runs or tokens your app will need, here are typical monthly API budgets for SaaS products at different stages (using standard GPT-5 pricing):
4 Mistakes That Blow Your AI Budget
Mistake 1: Ignoring output token cost. On GPT-5, output costs 8x more per token than input. A 1,000-token response costs as much as 8,000 input tokens. For generation-heavy apps, output tokens dominate your bill — not input.
Mistake 2: Forgetting conversation history accumulation. In chat apps, every API call re-sends the full conversation history as input. A 10-turn conversation at 200 tokens per turn means the 10th message includes 2,000 tokens of history — multiplying your estimated input volume by 3–5x.
Mistake 3: Not counting the system prompt. A 1,000-token system prompt sent on every call at 10,000 calls/month on GPT-5 costs $12.50/month in fixed overhead alone. Use context caching to make this portion near-free.
Mistake 4: Planning for average load, not peak load. A product launch or viral moment can spike call volume 10–50x in a single week. Always build a 30–50% spike buffer into monthly estimates and set billing alerts in your provider's dashboard.
How to Reduce Your Monthly AI API Burn Rate
- Shorten system prompts — remove boilerplate, merge redundant instructions, aim for <500 tokens where possible
- Constrain output length in prompts — "Reply in under 200 words" directly cuts output token generation
- Use context caching — DeepSeek V3.2 caches at $0.028/M (10x cheaper); OpenAI GPT-5 at $0.31/M (4x cheaper)
- Model tier routing — use inexpensive models for classification; reserve premium models for complex generation
- Batch API for async workloads — OpenAI's Batch API cuts GPT-5 pricing in half for overnight processing
- Right-size context injection — only inject the most relevant chunks from your RAG system, not the entire document
- Log real usage first — every major API returns
usage.input_tokensandusage.output_tokens; log these for 100 calls before budgeting