AI BUSINESS GUIDE
AI tokenomics: what does your AI workforce actually cost?
A plain-language guide to tokens, API credits and getting useful work from your AI budget—whatever business you run.
By Nansen Systems ·
Sources checked:
Understand what you are paying for
You hire an AI assistant to save time. Then you meet a new business expense: tokens. What are you paying for, and how do you keep the bill sensible without making your assistant less useful?
In this guide, AI tokenomics means the economics of running AI: the information it processes, the work it produces and the value you receive. We are not discussing cryptocurrency tokens or investment returns.
1. Tokens are pieces of information—not messages
For text-based AI, a token can represent a word, part of a word, punctuation or another text fragment. There is no universal one-word-to-one-token conversion. Use the provider’s recorded usage rather than counting sentences.
Input is what the model receives: your question, instructions, selected company references and any conversation history the application includes. Output is what it generates. Some reasoning models also bill for thinking tokens; check the particular model’s rules. A short question can therefore trigger more work than its length suggests.
2. API credits are a balance, not unlimited work
A top-up funds future usage. It does not necessarily remove a limit set by your application, increase a provider’s rate limit or extend the maximum size of one response. These are different controls—and your workspace should explain which one has stopped a task.
A failed or interrupted request may still incur a charge if the provider processed it. Repeatedly pressing Send can create more paid work. Check the task status before retrying.
3. A simple example—not a provider price quote
Imagine a model charging US$0.20 per million input tokens and US$1.00 per million output tokens. A task using 5,000 input tokens and 1,000 output tokens would cost US$0.001 + US$0.001 = US$0.002. One thousand identical tasks would cost US$2 in model usage.
These numbers are fictional teaching rates, not a Nansen package or a current model price. Real tasks vary. Thinking, caching, tools, taxes and other services may be charged differently. Estimate from recorded usage, then compare it with the provider’s bill.
4. The cheapest model is not always the cheapest result
Our approach: test an economical model for routine drafts, summaries and classification. Use a more capable model when the task needs it. A low-cost draft that needs five rewrites may cost more—in time and tokens—than a better first attempt.
Give each worker a clear job and the relevant approved information. Ask for a useful length, not the shortest possible answer. A sales email can be concise; a safety review must not lose essential detail just to save tokens.
5. Timing can matter—but check your provider
As checked on 17 September 2026, DeepSeek’s direct API lists off-peak rates at half its peak rates. Peak windows are Monday–Friday, 01:00–04:00 and 06:00–10:00 UTC: 9 AM–noon and 2–6 PM in the Philippines. All other hours are off-peak under that schedule.
For non-urgent work, wait for a cheaper window. For urgent work, use a clear owner-approved override. This is provider-specific, not a rule for every AI service; schedules and rates can change. A reseller may charge differently.
6. Track the cost of useful work
Start with three measures: what the task cost, whether the result was accepted, and how much human correction it needed. For sales, also track qualified replies and actual conversions—not just the number of emails generated.
Keep hosting, databases, search tools, email delivery and automation charges separate from model tokens. Moving your AI to a website does not eliminate API charges. An always-available workspace need not repeatedly call a model while it has nothing to do.
Nansen’s perspective
AI tokenomics applies to a skincare company, consultancy, retailer, manufacturer or any other business using AI. Start with one repeatable task, measure the cost and quality, and expand when it proves useful. A company AI Twin should use your approved knowledge and the right tools—not simply send every document to every model on every turn.
Keep people at the center
Keep a named person responsible for sensitive decisions and customer-facing promises. Saving tokens should never mean removing privacy protections, hiding costs, inventing product claims or sending unwanted messages. Clear approvals and spending visibility let people choose when extra AI work is worth it.
The takeaway
Spend less on repetition, not less on judgment. The goal is dependable work at a cost your business understands—not the largest team of agents or the lowest price per token.
Put the ideas to work.
Learn to build your company’s AI Twin with us, or have Nansen build it for you.