What a team pays under per-seat and usage pricing once the occasional users are counted, the six ways to buy without seats, and how to test the difference in two weeks
Aug 18, 2026 · 13 min read
A team where five people use AI daily and another five need occasional access should buy a workspace plan with a member allowance, or one priced by usage. A seat for every name on the list is the expensive way to cover them. A team where nearly everyone works in the product most days can keep per-seat plans, because the seat is being used and the native tools are usually deeper. The eight workspaces compared on the same criteria below are Playgram, WorkLLM, nexos.ai, Langdock, TeamAI, Aymo, Magai and TypingMind.
Per-seat pricing charges for access rather than for work, so an occasional reviewer costs the same every month as a daily writer. That is easy to budget and hard to justify once the access list is twice the size of the group doing the work. Flexera reported in 2026 that 59 per cent of respondents had seen wasted spending on AI software rise, and that only 31 per cent had any visibility into what their AI software was doing28.
This guide sets out the four pricing shapes on the market and the six ways teams buy without paying per seat. It prices the current single-vendor plans, compares eight multi-model workspaces on how each one charges for a fifth and a tenth person, and ends with a two-week trial plan.
A small delivery core does the work and a longer list of reviewers signs it off.
You are asked why the AI invoice grew when the amount of work did not.
Ten people should have access even though three or four will do most of the asking.
One or two people use one model daily, and nobody else needs to see the work.
A seat is sold by the month and used by the hour, and four separate costs come out of that mismatch.
Every enabled person needs another paid seat on every plan they touch, so an occasional reviewer costs what a daily writer costs. As an example, four single-vendor team plans came to about $101 per person a month at July 2026 list prices. That total takes ChatGPT Business at $25, Claude Team at $25, Gemini Business at $21 and Grok Business at $301, 2, 3, 4. Ten enabled people therefore come to roughly $1,010 a month, and if five of them sign in twice a month they still account for about half of it. Read those totals as an illustration rather than a rate, because a cheaper mix is easy to assemble. Several of the plans also bundle the same capability twice, such as web search or document work the team is already paying for elsewhere.
People open a different application for each model, copy prompts and answers between them, and upload the same file more than once. They rebuild project instructions in each product, then export an answer into Slack or a document before a colleague can carry on with it. An occasional user pays the highest price for this, because they repeat the setup every time rather than keeping a rhythm.
Project context sits inside personal accounts, so prompts, decisions and outputs stay with the person who wrote them, and a teammate who picks the work up opens an empty chat. A new model or a new person cannot see what the team already tried, which answer was approved, which terminology applies or why an earlier decision was made. Shared chat history helps a little, because it keeps the old conversation without promising that anything in it is retrieved into the next one. A knowledge base built by hand covers stable documents and misses the decisions made in ordinary chats, which is exactly what a reviewer needs when they arrive.
No single view shows which models people use, which seats are idle, which projects consume the budget or which region processes a given request. So the team cannot answer whether it still needs all four plans, and cannot stop usage before a budget is passed. When someone leaves, their chat history and prompts leave with them, and access has to be removed one vendor console at a time. Flexera's 2026 report found that only 31 per cent of respondents had visibility into their AI software at all28.
Five checks to run on any candidate. The last one decides the bill, and the first four decide whether a quiet user can do anything useful when they arrive.
The workspace should carry the model families your team reaches for and add new ones as they ship. A person should also be able to change model inside a thread rather than starting again, so read the published list and test one switch during the trial.
Model access is only half the job. List what the team does beyond chat: image and video generation, web research, document and spreadsheet work, code review, and a chat that leaves nothing behind. For a light user, document work and web research usually matter more than the model count.
Files, instructions, approved answers and past decisions should belong to a project rather than to one person's account, with folders, shared prompts and clear ownership around them. Someone arriving twice a month has no recent history of their own to work from.
An admin should see usage by person, model, project and period, cap it in advance rather than explain it afterwards, and set who can reach which project and history. Adding or removing a person should be one invitation rather than an account with every provider.
A light user should not cost the same as a daily one. Four shapes are sold here: per seat, per workspace with a member cap, pooled usage credits, and your own API keys billed by the provider. Price your mix under each shape, count the quiet users separately, and check what happens at the cap.
The multi-model workspaces a team is most likely to weigh up, on the same criteria and to one standard. Where a vendor does not document something, the cell says so.
This table compares multi-model team workspaces with each other. The single-vendor plans a workspace replaces are priced further down, under 'Priced per seat', and are not rows here. Pricing is the lowest-priced paid plan that covers five users, at the monthly rate, so a product whose entry plan holds fewer than five people is shown on the plan that holds them. Each cell cites the page that documents that cell rather than one pricing page per row. Figures checked August 2026 against each provider's own pages, and cells marked 'Manual test required' could not be confirmed from public documentation.
The same products on the criteria that decide daily use: what each one does besides chat, what it connects to, what an admin can see and cap, and where your data goes.
These criteria decide daily use more than the model list does, and vendors document them very unevenly. 'Not publicly documented' means the official sources checked did not state it, and it does not mean the feature is absent, so read those cells as questions to put to the vendor. Checked August 2026.
The published per-seat price of each major single-vendor team plan, billed monthly. Every person you enable pays these figures whether they use the plan daily or twice a month.
Buying all four for one person came to about $101 a month at July 2026 list prices. Read that as one example stack rather than a going rate, because a team can assemble a cheaper mix and the four plans do not buy the same amount of use. Prices change often and vary by annual against monthly billing and by region, so confirm current pricing with each provider before purchase. The estimator further down compares this shape against usage pricing with your own numbers. Sources are listed at the foot of this page.
Two of these move the invoice directly and two are found only in an audit, which is why a team usually meets them in that order.
Six setups, from a workspace allowance through to a build of your own. The workspace options come first because they are what this guide is about.
One monthly fee covers a set number of people, so the marginal person costs nothing until the cap. TeamAI Professional is $149 a month for up to 25 users and Aymo Premium is $20 a month for up to 10 members17, 21. Both meter usage separately from members.
Best for: Teams of five to twenty five with a few heavy users.
Strengths
Trade-offs
The team buys a pool of usage and draws it down, so the bill follows the work rather than the access list. A person who logs in twice a month adds very little to it. The pool has to be watched, because heavy users and automated runs draw from the same balance.
Best for: Teams whose AI use rises and falls week to week.
Strengths
Trade-offs
Standardise on a single vendor and buy a seat for everyone who needs it. Five people on ChatGPT Business come to about $125 a month, and Claude Team has a five-member minimum, so both are simple to buy and simple to explain1, 2.
Best for: Teams whose work sits inside one model family.
Strengths
Trade-offs
Buy the plans the work needs and accept the per-seat total. As an example, all four of the plans priced above came to about $101 per person a month at July 2026 list prices, so one more occasional user adds the same $1011, 2, 3, 4.
Best for: Teams that need each vendor's own tools daily.
Strengths
Trade-offs
People expense their own accounts and the company reimburses them. It costs nothing to set up and it works while two or three people are involved. Ownership sits with the individuals, so the company holds neither the history nor the access.
Best for: One or two independent users with no shared work.
Strengths
Trade-offs
Engineers wire the models into an interface of your own, so the model cost follows consumption exactly. What replaces the subscription is build and maintenance work, plus authentication, storage, permissions, logging and retrieval that a product would have shipped.
Best for: Technical teams with one defined application.
Strengths
Trade-offs
A quarterly retention review with the project context set once. Every stage reads from it, so the reviewer who arrives at the end starts with the evidence rather than a briefing.
Every stage reads the same shared context, so the finance lead who joins twice a month opens the project rather than booking a briefing. A person approves the draft before it ships and sends unsupported claims back to the drafting stage, and the approved memo and its decisions go back into the project for whoever picks it up next.
Memory is what lets a person who was away for three weeks do something useful in ten minutes, and every product means something different by the word.
A context window is how much text a model reads in one request, and it empties when that conversation ends. Memory is context stored outside the chat and pulled back into later ones, on another day or another model. A larger window does not give a team the second thing.
Some keep chat history and nothing more. Some let you attach files and build a knowledge base by hand. Some learn automatically and keep what they learn private to one person. Some save it where the whole team can reach it, and only the last stops a group explaining the same project again.
Shared memory needs boundaries: what belongs to one person, what belongs to a project and its members, and what the organisation should see. Products draw these lines differently and some draw only one, so ask which boundaries exist rather than assuming yours are reflected.
A reviewer who arrives twice a month has no recent history, so the project has to carry the brief, the sources and the decisions already taken. Check that someone can see what was saved and correct a wrong entry, because a light user cannot tell a stale fact from a current one.
A vendor-neutral plan built around the one question a demo will not answer, which is what a quiet user costs you. It runs about two weeks.
For each plan record the monthly and annual commitment, who is enabled, and who signed in over the last 30, 60 and 90 days. Split the people into daily, weekly and occasional users, and note the native features you would lose by consolidating, plus current API and overage spending.
Use work the team already has that week, such as research and briefing, document drafting and review, spreadsheet analysis, technical review, or a customer follow-up. Prompts written for a demo make every product look good, so they prove nothing about a quiet month.
Run the pilot with two or three daily users, one manager and one genuine occasional user, across at least two model families and one shared project with real permissions. Set a budget or a hard cap before you start, so you also find out whether the cap works.
Track time to a useful first answer, manual edits, how often context had to be repeated, files uploaded more than once, and active usage per person. Then time how long the occasional user needed before they could work, and check what the month actually consumed.
Ask each vendor whether pricing is per user, per workspace, per credit or a hybrid, and whether an extra person is charged immediately. Then ask what happens when the included credits run out and whether an admin can cap spending in advance. Then cost the shortlist at your headcount, at three more people and at double, because the shapes cross over somewhere.
A team should size its AI spending on active workloads, request frequency and the model access the work needs, rather than on headcount. Per-seat plans stay reasonable while nearly everyone with access works in the product regularly and values its native tools. Workspace and usage pricing suit the common case instead, where five people do most of the work and another five need to look in.
Four limits apply. Plan names are not comparable, because a cheap workspace may arrive without analytics, permissions or a knowledge base while a per-seat plan includes model usage that a bring-your-own-key product bills separately. Stored context helps only when retrieval, scopes and correction controls are clear. Not everyone needs the same access. And prices, model lists and plan caps all move faster than any page can track.
So the choice is really about how the bill behaves, and about which people the work will actually reach next quarter. Price your own mix under each shape, count the quiet users separately, then run a short pilot on real work with a cap set in advance. That is the part a demo cannot answer for you.
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Playgram will automatically choose the most cost-efficient model suitable for the task. It will be chosen by users in approximately 80% of requests. Your models for the remaining 20%:
If you bought each separately: