Eight team workspaces compared on setting model access by team or role instead of giving everyone the same list
Sep 15, 2026 · 13 min read
A company should give each team or role a default model and an allowlist instead of one shared catalogue every employee can open. The setup pairs group-based model permissions with individual exceptions and a spending limit that acts before an overrun. It is unnecessary for a small team of two or three people running one low-risk workflow, where a single shared plan is simpler. The eight workspaces compared on the same criteria below are Playgram, WorkLLM, nexos.ai, Langdock, TeamAI, Aymo, Magai and TypingMind.
Most companies start with blanket access. Every employee sees the same model catalogue, no matter their role, their cost profile or the sensitivity of the data they handle. That setup holds up for a small team, but it breaks down once several departments use AI differently, because marketing, engineering, finance and support each need a different mix of models, connectors and spending limits. The evidence for a shift toward role-based access is mostly product direction rather than a measured market-wide statistic, and several workspaces already expose group, workspace or person-level model controls.
The approach that usually works better is a single governed workspace with model permissions set by team or role, rather than one blanket switch or a pile of separate subscriptions. It fits a company where five or more people use AI regularly and two or more model providers are already in play. It also fits a company where some roles handle source code, contracts or customer data that others should not reach. The real limitation is administrative: roles have to be reviewed as people change jobs, as models change price or region, and as temporary exceptions expire. This guide sets out what a better setup should provide and prices the direct-provider stack against it. It then shows how shared project context and memory change once permissions are set at the group level rather than the person level.
Marketing, engineering, finance and support each reach for different models, so one shared catalogue no longer expresses the real policy.
Roles that touch source code, contracts, personal data or financial records need scoped access, while the rest of the company does not.
IT or finance needs to see which team or model is driving cost, and cap it before an exception becomes the new normal.
Two or three people on one low-risk workflow with one provider do not need a permission matrix. One shared plan and a written policy cover that case well.
Four layers explain why one shared model catalogue looks fine until cost, workflow, context and management all break at once.
Separate subscriptions to GPT, Claude, Gemini and Grok duplicate access for a person who only needs part of each plan, and the company ends up paying for overlapping capability across four consoles. Without a role limit, routine rewriting and summarization can draw on the same expensive model a company reserves for hard analysis. Nothing stops a routine account from opening the priciest option in the catalogue. An occasional user, someone who needs one model for one project, usually has no way to get scoped access without a full permanent seat.
A single task can move across three or four separate tools before it is done, as an employee copies a prompt, reformats an output and repeats the same system instructions in each one. Comparing two models under identical conditions is hard when each one lives in its own account with its own history. A written policy that says a model is off limits does nothing if that model still sits in the menu and a person can select it anyway.
A prompt refined inside one employee's personal account stays there, so project history, approved terminology and successful examples never become a team asset. Documents and instructions get uploaded again in every new chat and every new provider, because nothing carries them forward automatically. Changing providers usually means copying that context by hand, and even inside one multi-model product, whether a switch keeps the current thread or forces a new one varies by product.
Separate accounts give a manager no single place to see which model a team used, for which task, and whether a restricted model was already disabled by the time it mattered. A model allowlist is only part of the picture, since governance also has to cover connected data, tools, retained context and spend. When someone changes roles or leaves the company, the chats and knowledge tied to their personal account rarely have a clear owner, and access has to be found and revoked account by account.
Five groups covering what a workspace needs so access, tools and memory match each role rather than one shared list.
Model access should be set by team or role, not open to everyone in one catalogue. A routine user opens on an approved default model, and a manager or specialist gets an individual exception without changing the whole group's setting.
Check each product for image generation, web research, document and spreadsheet work, code review, and chats that leave no trace. A role that needs one of these buys a second tool if the workspace does not cover it, and video generation is still absent in several products.
Team members on the same project should reach the same approved files, instructions and decisions. Switching to another permitted model should not mean rebuilding the prompt by hand, and memory needs its own scope, separate from which models a role can reach.
Usage limits should pair with model access, measured in credits, tokens or dollars, with a hard cap that stops spend before an overrun. Admins need usage by person, model and project, plus a separate permission for the connectors and knowledge bases a model does not automatically grant.
Onboarding should be automatic within an identity group, so a new hire in an existing role inherits its allowed models, tools and limits. Pricing should flex with how much a role actually uses instead of charging every seat the same, and every access change should name the administrator.
The multi-model workspaces a team is most likely to weigh up for role-based model access, judged on the same criteria and to one standard.
This table compares multi-model team workspaces on model-access and permission criteria. Pricing is the lowest-priced paid plan that covers five users, at the monthly rate. Each cell cites the page that documents that cell rather than one pricing page per row. Figures checked September 15 2026, and cells marked 'Manual test required' could not be confirmed from public documentation.
The same products again, on the criteria that decide whether a role's permissions actually hold: tools, integrations, visibility, controls, training terms and hosting.
'Not publicly documented' means the official sources checked did not state it, and 'Manual test required' means the behaviour cannot be confirmed without trying it. Neither means the feature is absent, so read them as questions to put to the vendor. Checked September 15 2026.
The published per-seat price of each major single-vendor team plan, billed monthly, before any role or model restriction is set inside it.
Buying all four for one person came to about $101 a month at July 2026 list prices, so five fully provisioned people cost roughly $505. Read the total as one example stack rather than a going rate, since a cheaper mix is easy to assemble. Figures checked July 2026.
These drivers change the real cost of role-based access more than any plan's list price does.
Six setups, led by the one this guide is about, ordered by how much administration each one adds.
One workspace can hold every model behind role-based permissions, so a team sets a default per group instead of policing an open catalogue by hand.
Best for: Teams with at least two roles that need genuinely different model access, tools or budgets.
Strengths
Trade-offs
Each person keeps whatever provider account they already use, and access control depends on which accounts get created in the first place.
Best for: A very small team where every member already has a preferred, low-risk tool.
Strengths
Trade-offs
The company standardises on one vendor and controls access through that vendor's own seat types and admin console.
Best for: A company whose workflows fit one model family well enough that a second one is rarely needed.
Strengths
Trade-offs
The company keeps more than one vendor's team plan active, and access control is whatever each vendor's own console happens to offer.
Best for: Organisations with one or two workflows a single workspace genuinely cannot cover.
Strengths
Trade-offs
Engineers build a policy layer that checks role, model, data class and cost before a request reaches any provider.
Best for: Organisations with engineering capacity and an existing identity system the policy layer can plug into.
Strengths
Trade-offs
The same governed workspace, plus a saved record of approved context that a permitted teammate can pick up without asking around.
Best for: Teams whose workflows hand off between roles, such as a draft stage and a review or escalation stage.
Strengths
Trade-offs
A controlled workflow makes the permitted path easier than asking someone to rebuild it from memory.
A campaign manager reviews the routine draft before it escalates to the privileged model, and that review gate is what keeps the specialist model behind a person rather than a button. A regional teammate then opens the same project and continues the work using only the models their role allows, without explaining the brief again.
Products mean different things by the word memory, and for role-based access what is saved matters as much as which models a role can reach.
A context window is how much text a model reads in one request, and it empties when the chat ends. Memory is context stored outside the chat and pulled back into later ones. A bigger window does not give a team the second thing.
Some keep chat history and projects only. Some let a person attach files and build a knowledge base by hand. Some learn automatically but keep it private to one account. Some save it at a level the whole team can reach, which is the one worth relying on.
Once memory is shared it needs a boundary: what belongs to one project, what belongs to a team, and what the whole company should see. A model permission and a memory permission are not the same setting, so a person barred from a model should not reach it through a shared agent fixed to that model.
Before real work goes in, check four controls: who can see what was saved and why, who can correct a wrong entry, who can reach it, and who can stop a session from becoming permanent. Allowing a model to a role does not mean granting every memory source that model can reach.
Five steps take a company from an unmanaged model catalogue to a permission matrix it can actually enforce.
Record every AI subscription and API account, who is assigned and active, which models each department actually uses, and which accounts have no clear owner.
List each role or team as a row, and set its default model, its approved exceptions, and the data or tools it must not reach.
Fix a duration and a named administrator, and test group inheritance, individual exceptions and what happens when a person belongs to two groups.
Track time to a useful output, exception volume, attempts to use a restricted model, and how long it takes to revoke access when a role changes.
Confirm restrictions are enforced on the server, budgets stop spend before an overrun, shared agents cannot bypass a model rule, and departing employees lose access immediately.
The practical answer sits between two extremes: unrestricted access for everyone, and one model allowed across the whole company. A permission matrix that gives each role a useful default, a few approved specialist models and a spending limit that acts before an overrun covers the middle ground most companies actually need.
Pricing and plan limits change quickly, and two plans called Business rarely mean the same thing, so names alone cannot be compared. Memory needs a clear scope of its own, not every teammate needs the same model access, and the only reliable test is a pilot run on the company's own workflows and its own identity groups.
What a company is really choosing between is a policy written down somewhere nobody checks, and a system that enforces the same policy on its own. The setup a role's model list sits inside decides as much as the list itself, since a permission that a shared agent or an unmanaged export can bypass is not really a permission. Test the setup on a real permission matrix, a real exception case and a real departure before deciding which one the company needs.
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