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TekStudio.ai

AI For Your Business Lesson 1 of 3

What AI Actually Costs

Two pricing models, one crossover point, and the costs that never appear on the invoice.

Applied Time-sensitive 8 min

Worth reading first: Tokens and the Context Window

By the end of this lesson you will be able to

  • Explain the difference between subscription and usage-based pricing
  • Estimate which model is cheaper for a given team and workload
  • Budget for the costs that do not appear on the vendor invoice
Prices move fastSpecific rates in this lesson are illustrative and will drift. The structure — what you are billed for and where the crossover sits — is stable. Check current rate cards before committing budget.

There are only two ways to pay for AI, and the choice between them is not about which vendor you like. It is about how many people use it and how hard they use it.

The two pricing models

Per-seat subscription

  • A flat monthly fee per person
  • Generous but capped usage
  • Completely predictable
  • Includes the polished app and interface
  • You pay for the seat whether it is used or not

Per-token API

  • Pay for text in and text out
  • No floor and no ceiling
  • Scales exactly with real usage
  • You build or buy the interface
  • A runaway process can produce a surprise bill

Output typically costs several times more than input, because generating text is the expensive part. That has a practical consequence: asking for a short answer about a long document is much cheaper than asking for a long answer about a short one.

Finding the crossover

Where does it flip?
Subscriptions$100 5 × $20/month, unlimited-ish
API (per token)$15.25 1320 tasks × ~1050 tokens Cheaper here

At this usage, the API wins. Seats become cheaper above about 79 tasks per person per day.

Illustrative Rates change often — check current pricing before you budget. What does not change: subscriptions are predictable and cap out, API billing starts near zero and scales with actual use. Light, spiky usage favours the API. Heavy daily use across a team favours seats.

Move the sliders to your real team size and usage. The crossover point moves surprisingly little when underlying rates change.

The pattern holds regardless of the exact numbers: light or spiky usage favours the API, because you pay almost nothing when nobody is using it. Heavy daily use across a team favours seats, because the subscription caps your exposure.

In practice, most small businesses land in one of three places:

  • A handful of people using AI conversationally most days — buy seats, stop thinking about it.
  • An automated process handling a few hundred jobs a month — API, and the bill will be smaller than you expect.
  • Both — seats for the people, API for the automation. This is the most common end state and there is nothing wrong with it.

The costs nobody quotes you

The vendor invoice is usually the smallest line. These are the ones that determine whether a project actually pays off.

CostTypical sizeNotes
Verification timeLarge, ongoingSomeone checks the output. This never goes to zero for work that matters.
Setup and integrationOne-off, often weeksConnecting to your actual systems is most of the work.
Prompt and process designOne-off, then smallGetting from "sometimes works" to "reliably works".
Rework when it driftsRecurringModels change under you. Things that worked can quietly stop.
Training your teamOne-off, underestimatedA tool nobody uses well costs full price and returns nothing.
The mistake that wastes the most moneyAutomating a task nobody had properly defined. If a human doing the job cannot state the rules clearly, AI will not infer them — you will just get confident, inconsistent output faster. Define the process first, then automate it.

A budgeting approach that survives contact with reality

  1. Price the current process honestly

    Hours per week times a real loaded hourly rate. If nobody can say what the task currently costs, that is the first problem to solve, and it is not an AI problem.

  2. Start with subscriptions

    They are cheap, immediate, and require no engineering. Most businesses discover the value here before building anything. Some stop here, correctly.

  3. Measure before you build

    Run the task manually through a subscription tool for a few weeks. You will learn the real volume and the real failure rate, which is what any build estimate depends on.

  4. Budget verification as a permanent line

    Not a transition cost. If the plan assumes nobody checks the output after month three, the plan is wrong.

Are the free tiers good enough?

For trying things out, yes. For real work, the limits and the older models get in the way quickly, and free tiers often carry weaker data commitments. Treat free as evaluation, not deployment.

How do I stop an API bill running away?

Set a hard spending cap at the provider — every major one supports it. Then cap the output length per request. Most runaway bills come from a loop calling the API far more often than intended, not from expensive individual calls.

Is a cheaper model worth it?

Often, yes. Smaller models handle classification, extraction, and routing well at a fraction of the cost. Reserve the expensive model for work that genuinely needs judgement. Mixing models by task is normal, not a compromise.

Key takeaways

  • Subscriptions are predictable and capped. API billing starts near zero and scales with use.
  • Output costs several times more than input — short answers about long documents are the cheap shape.
  • Light or spiky usage favours the API; heavy daily team use favours seats.
  • Verification time is a permanent cost, not a transition cost.
  • Start with subscriptions, measure real usage, and only then decide whether to build.