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 Core Formula
Monthly Cost = Runs/Month × [(Avg Input Tokens × Input Rate) + (Avg Output Tokens × Output Rate)] ÷ 1,000,000
Where rates are expressed as price per 1 million tokens — the standard unit used by all major AI providers.
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.
InputRate and OutputRate are the per-1M-token prices from your model's API documentation.
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 monthHow 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 runAdd 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 runShort 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 ratesFind 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 bufferCalculate 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
You run an AI-powered SEO content brief tool. It reads client briefs and returns structured outlines. Here's how to calculate your monthly burn rate across four model options.
| Variable | Value | How We Got Here |
|---|---|---|
| Runs per month | 5,000 | 200 clients × 25 briefs/month |
| Avg. input tokens/run | 1,800 | System prompt 600 + client brief 1,200 |
| Avg. output tokens/run | 800 | Structured outline, 8–12 sections |
| Total input tokens/month | 9,000,000 | 5,000 × 1,800 |
| Total output tokens/month | 4,000,000 | 5,000 × 800 |
| Model | Input Cost | Output Cost | Monthly Total | Annual Cost |
|---|---|---|---|---|
| DeepSeek V3.2 ⭐ | $2.52 | $1.68 | $4.20 | $50.40 |
| GPT-5 Mini | $3.60 | $6.40 | $10.00 | $120.00 |
| GPT-5 | $11.25 | $40.00 | $51.25 | $615.00 |
| Claude Sonnet 4.6 | $27.00 | $60.00 | $87.00 | $1,044.00 |
DeepSeek V3.2: (9M × $0.28 + 4M × $0.42) ÷ 1,000,000 = $4.20/month. GPT-5: (9M × $1.25 + 4M × $10.00) ÷ 1,000,000 = $51.25/month.
✅ Key Takeaway
For this workload, the model choice alone creates a 12x monthly cost difference ($4.20 vs $51.25) and a $565/year gap between the cheapest and the most common default choice.
Budget Benchmarks by Scale
All estimates use the 1,800 input / 800 output token workload profile above at list pricing without caching or batch discounts.
| Stage | Runs/Month | DeepSeek V3.2 | GPT-5 Mini | GPT-5 |
|---|---|---|---|---|
| Prototype / MVP | 500 | $0.42 | $1.00 | $5.13 |
| Early-stage SaaS | 5,000 | $4.20 | $10.00 | $51.25 |
| Growing agency | 25,000 | $21.00 | $50.00 | $256.25 |
| Mid-size platform | 100,000 | $84.00 | $200.00 | $1,025.00 |
| Enterprise | 500,000 | $420.00 | $1,000.00 | $5,125.00 |
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
Frequently Asked Questions
What is AI API burn rate?
AI API burn rate is your total monthly spend on AI API calls. Formula: Runs/Month × ((Input Tokens × Input Rate) + (Output Tokens × Output Rate)) / 1,000,000, where rates are per 1 million tokens.
How do you calculate monthly AI API costs?
Monthly Cost = Runs/Month × ((AvgInputTokens × InputRate) + (AvgOutputTokens × OutputRate)) / 1,000,000. Example with GPT-5 at 10,000 runs, 2,000 input + 500 output tokens: (10,000 × (2,000 × 1.25 + 500 × 10.00)) / 1,000,000 = $75.00/month.
What is a typical AI API cost per month for a startup?
An early-stage SaaS with 5,000 API calls/month (1,800 input + 800 output tokens) pays roughly $4.20/month on DeepSeek V3.2, $10.00 on GPT-5 Mini, or $51.25 on GPT-5. At 100,000 runs/month, those scale to $84, $200, and $1,025 respectively.
What is the biggest mistake when budgeting for AI API costs?
Ignoring output token cost. On GPT-5, output is 8x more expensive per token than input. A 1,000-token response costs as much as 8,000 input tokens. The second most common mistake is not accounting for conversation history accumulation — every turn re-sends the full history as input.
What is the cheapest AI API for high-volume production use?
As of February 2026, DeepSeek V3.2 is most cost-effective at $0.28/M input and $0.42/M output, with automatic caching at $0.028/M. For US data residency requirements, GPT-5 Mini ($0.40/$1.60 per 1M) is the lowest-cost OpenAI option.
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Pricing as of 19 Feb 2026. Token estimates are approximations. Verify rates at your provider's official pricing page before budgeting.