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Tokens are counted here as characters ÷ 4, the same approximation Collabase uses internally when it splits pages for indexing. Real tokenizers differ by 10 to 20 percent, more for German and French than for English.
What every request carries
This block is identical on every request, no matter what is asked.
Nearly two thirds of that fixed cost is the skill catalogue. It grows with every skill added to the assistant, at roughly 150 tokens per skill, and it is paid on every request whether the skill is used or not.
What gets added on top
Every step pays the fixed cost again
The assistant works in steps: it asks for something, reads what comes back, and either asks for more or answers. Each step is a fresh request carrying the full fixed block plus everything gathered so far. It takes at most 8 steps in one turn. That is why the number below is not “one question, one request”.What single questions cost
Input tokens, summed across every step of one turn. Semantic search is off in these examples.The arithmetic behind one row
“My open work items”, the most common shape of question:What a user costs per month
Assumptions, adjust them to your own organisation:- An active user asks 5 questions on a working day, over 21 working days: 105 questions a month
- Of those, 25 percent need no lookup, 55 percent need one, 20 percent run multi-step over about four steps
- Semantic search is off
What an instance with 100 users costs
Assumptions: 40 of the 100 use the assistant daily as above, the other 60 use it occasionally, around 10 questions a month.
The prices in that table are brackets, not quotes. They span roughly what small to large models cost at the time of writing. Take the current numbers from your provider’s price page and put them into the formula.
On a self-hosted provider the per-token price is zero and the cost is your hardware. The token volume is the same, so it still tells you how much work the machine has to do.
Collabase records per-request token usage itself, see Measuring instead of estimating below. Compare it against your provider’s own usage dashboard after the first month: the two count tokens with different tokenizers and will not match to the digit.
Measuring instead of estimating
Everything above is arithmetic. Admin → Settings → AI settings → Usage is the measurement: it records every model call your instance actually made, with tokens in, tokens out, and cost, broken down by area and by model over the last 7, 30 or 90 days. The figures come from your own database, not from the provider’s billing API and not from analytics, so the page works with telemetry switched off and needs no extra credentials.Cost is calculated from a price list shipped with Collabase, and the page names the date that list was last updated. A model that is not on it contributes its tokens but no cost, and the page says how many requests that affected — the total is then a lower bound, never a guess.
Setting a ceiling
The Limits card sits on the same Usage page, directly under the figures, so you set a limit next to what is actually being spent. All three are checked before a question reaches the model.0 means no limit, and that is the shipped default, so nothing changes until you set a number.
When a member reaches the daily limit, the assistant declines the next question and says the usage limit has been reached. Nothing is lost: the daily count resets at midnight and the monthly count on the first of the month, and an answer already being written is allowed to finish.
A limit is a cost control, not a permission. If Collabase cannot read the usage figures for a moment, the question goes through rather than locking everyone out of the assistant.
What moves the number
Choosing a model
The assistant asks more of a model than the writing assistant does. Three requirements decide whether a model is usable at all. Reliable tool calling. Thirty-four skill definitions go out with every request, and the model has to pick the right one and fill in its inputs correctly. A model that is merely good at prose will invent answers instead of looking them up. A context window of at least 32,000 tokens, ideally 64,000. Before a single result comes back, one request can already carry 8,200 fixed, plus up to 3,000 for the open item, plus up to 4,100 for search passages: about 15,300 tokens. Results then accumulate over as many as 8 steps. Anything under 32,000 truncates on the harder questions, and 32,000 itself is tight once semantic search is on. Instruction following. The answer style rules and the ban on inventing names are instructions, and a model that drifts from them produces confident nonsense. The approval gate in front of every change is not an instruction, so a model that ignores its prompt still cannot write anything on its own.Where to start per provider
Collabase suggests one model per provider. These are sensible starting points, not the only options.How the tiers compare
Next steps
AI Configuration
Connecting the provider, enabling semantic search, and what each provider can do.
Using CollabaseAI
What your users see once it is switched on.