AI/LLM API cost calculator: what will your AI feature cost to run?
Enter your users and usage in plain business terms to estimate monthly cost, cost per active user, annual cost, and the tokens you'll burn across model tiers.
What will your AI feature cost to run?
Enter your usage in business terms — we estimate the tokens and show your monthly cost and cost per user.
People using the AI feature each month
Average API calls per user each month
system prompt + retrieved context + history
Tokens the model generates in reply
Model tier
Pricing verified June 2026 — directional, re-verify against provider pages.
Fill in all fields and pick a model to estimate your cost.
Quick answer: how AI feature costs add up
LLM APIs bill per token (about 0.75 words each), and output tokens usually cost several times more than input. Monthly cost is roughly tokens per request × requests per month × the model's per-token rate. As of mid-2026, prices span roughly 150× between the cheapest and flagship models - so the biggest lever is picking the smallest model that meets your quality bar. Enter your users and usage above to estimate monthly cost and cost per user.
Key takeaways
- You pay per token, and output is pricier than input.
- Estimate all tokens - system prompts, retrieved context, chat history, and retries - not just the visible message.
- Model choice is the dominant cost lever (up to ~150× difference between tiers).
- Prompt caching and batch APIs can cut bills significantly.
- Track cost per successful task, not just cost per call.
How to estimate your AI feature's cost
- Estimate usage: monthly active users × messages per user × average input and output tokens.
- Pick a model that matches the quality you actually need.
- Compare a few models side by side to find the cheapest one that still works.
These are exactly the calculator inputs above, expressed in business terms instead of raw token counts.
How LLM API pricing works
Tokens are roughly 0.75 words each, and providers charge separately for input and output. Output is several times more expensive because each generated token requires fresh computation, while input is processed in a single efficient pass. Context windows, prompt caching, and retries all add tokens you should account for in your estimate.
Model pricing reference (verified June 2026)
| Model (example tiers) | Input / Output per 1M tokens |
|---|---|
| Budget (e.g. mini / nano / flash-lite) | ~$0.10–$0.20 / ~$0.40–$1.25 |
| Mid-tier (e.g. GPT / Claude Sonnet class) | ~$2.50–$3.00 / ~$10–$15 |
| Flagship (e.g. Opus / Pro class) | ~$5.00 / ~$25–$30 |
Directional and verified June 2026 - re-verify against provider pricing pages before committing a budget.
How to cut your AI costs
- Use a smaller model for simple tasks - often 10–16× cheaper at similar quality.
- Shorten system prompts and trim retrieved context and history.
- Enable prompt caching so repeated context isn't re-billed at full price.
- Route non-realtime jobs through the batch API for a discount.
- Cap maximum output length to control the most expensive tokens.
Frequently asked questions
- How do I calculate LLM API costs?
- Multiply tokens per request by requests per month by the model's per-token rate, keeping input and output separate, then add caching and retry overhead.
- Why are output tokens more expensive than input?
- Generating each output token requires fresh model computation, while input is processed more efficiently - so output is usually priced several times higher.
- What is a token?
- Roughly 0.75 words of English. APIs bill per token, not per word or character, counting both what you send and what the model generates.
- What does "monthly tokens" mean for a chatbot?
- It's total tokens processed per month. A bot with ~1,000 active users sending ~10 messages a day can use ~10–30M tokens/month depending on prompt size.
- Which model is cheapest?
- Budget tiers (mini / nano / flash-lite) are cheapest. Pick the smallest model that meets your quality bar and upgrade only where needed.
- How can I reduce costs?
- Smaller models, shorter prompts, prompt caching, batch APIs, and trimming context and history.
- How accurate is this estimate?
- Planning-grade. Real costs depend on prompt design, retries, and caching - verify against current provider pricing.
- Should I self-host instead?
- Only at high, steady volume. For most products under heavy usage, hosted APIs are cheaper than amortized GPUs.
Glossary
- Token
- The unit LLMs bill on - about 0.75 words of English.
- Input / output tokens
- Tokens you send (prompt, context, history) versus tokens the model generates; priced separately, with output higher.
- Context window
- The maximum tokens a model can consider at once, including everything you send plus its reply.
- Prompt caching
- Reusing already-processed prompt context at a reduced rate instead of paying full input price each call.
- Batch API
- A discounted, asynchronous mode for non-realtime workloads.
- MAU
- Monthly active users - the people using your AI feature each month.
- Cost per task
- Total cost divided by successful outcomes - a truer metric than cost per call.
- tiktoken
- A common tokenizer library used to count tokens before sending a request.
Why model choice is the cost decision that matters most
The price gap between the cheapest and most capable models can be roughly 150×. That means the single biggest cost decision for an AI feature isn't infrastructure or caching - it's which model handles each request. Many products default to a flagship model for everything, then discover that a budget tier handles 80% of requests just as well at a fraction of the cost. Route simple tasks to small models and reserve flagships for the cases that genuinely need them.
Three levers that bring an AI bill down
| Lever | How to pull it |
|---|---|
| Smaller model | Match each task to the cheapest model that meets the quality bar |
| Fewer tokens | Trim system prompts, cap output length, and limit retained history |
| Caching & batching | Cache repeated context and route non-realtime work through the batch API |
The highest-leverage savings usually come from architecture, not from haggling over per-token rates. A well-designed retrieval step that sends only the most relevant context - rather than dumping everything into the prompt - can cut input tokens dramatically. If your estimated cost looks alarming, the fix is often in how the feature is built, not in the model price list.
Last updated June 2026.
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