Eight team workspaces compared on what an admin can limit for one contractor: models, projects, memory and credits, with the cost of each setup
Oct 20, 2026 · 17 min read
Give every freelancer or contractor a named account that an admin can remove, then limit it to the models, projects and usage the assignment needs. A shared login is the weakest option because nobody can tell who did what, and a normal full seat often costs the same as an employee seat while exposing more than the job needs. Per-person controls vary a lot by product, and below about five people one or two single-vendor seats often cost less. The eight workspaces compared on the same criteria below are Playgram, WorkLLM, nexos.ai, Langdock, TeamAI, Aymo, Magai and TypingMind.
Cost, access and context usually follow the account and not the assignment. A contractor with three separate subscriptions triggers three monthly seats even when use is occasional, and the same contractor in a broad company workspace may be able to read memory and files from unrelated clients. Both are common, so the controls matter as much as the model list.
This guide lists what a contractor-ready workspace needs, prices the single-vendor stack for five people and compares eight workspaces on controls, memory and cost. It also shows a nine-stage workflow from scoped project to revoked access and a pilot that tests both what a contractor can open and what they cannot. No product removes the need for confidentiality terms, because a copied file stays copied after the account is gone.
Writers, designers, researchers and agencies of roughly five people or more work with outside people who need brand instructions, research and image tools. Each client's material should stay apart from the others.
Developers and fractional leaders need technical documents or strategy papers. They should not reach finance, customer or HR material, and a connector should not hand them more than the assignment calls for.
A team that already uses two or more model providers, shared prompt libraries or company knowledge brings in several short-term workers a year. Each one adds seats, invitations and a removal step.
One long-term contractor uses one provider and one narrow tool, or no confidential material enters the AI system. The work can also run in a contractor-owned environment, or the team cannot yet keep permissions and removal in order.
Four layers explain why cost, access and context spread out when a contractor joins through whatever account is nearest.
Separate subscriptions multiply the fixed cost by both people and providers, so a contractor who needs GPT, Claude and Grok models for six weeks can trigger several monthly seats even when use is occasional. ChatGPT Business is a fixed named-user subscription, and Langdock and Magai also charge by seat or user on their comparable plans1, 15, 28, while WorkLLM prices by workspace and credit level. Nobody is told when a short-term seat sits idle.
Every added tool needs another account and invitation, another interface to learn, another upload of the brief and another copy of the system instructions. A conversation in one product cannot be continued by a colleague in another, and each vendor has its own console to visit when the contract ends. Short engagements feel this overhead most, because onboarding takes a larger share of the assignment.
A contractor's useful work can stay in a private chat or a personal account, and sharing one conversation does not move the files, instructions, model settings or decisions behind it. The reverse is also risky, because a broad company workspace can show too much. WorkLLM, for example, says organization memory is available across every project and can make imported information reachable even where a user lacked access to the original file8.
A shared login gives an admin almost no control over one person, because actions cannot be attributed, model access cannot differ by person, usage and cost cannot be separated and one password change affects everyone. Langdock requires registered user accounts and prohibits sharing credentials16. A full named seat fixes identity and removal, though the contractor may still inherit every workspace-wide model, agent, prompt and knowledge source unless the product supports groups or project membership.
Five groups covering what a workspace needs so a contractor can do the job without reaching the rest of the team's work.
The contractor sees only approved models, set for one person or one group. They can change model mid task without uploading the brief again.
Check each product for image generation, video generation, web research, document and spreadsheet work, code review and chats that leave no history. A product with many models does not automatically include any of them.
The account reaches only the assigned project and its knowledge. Chats, files and approved outputs stay with the team after the contractor leaves, and connectors keep the source system's own permissions.
Each contractor has a separate named login. An admin can see use by person, model and period, set a limit that applies before the bill and remove the identity and its project access in one action.
A contractor may use AI daily for two weeks and then not at all, so a full seat for the whole term is a poor fit. Compare per seat, per workspace and credit pricing using the real contract length, active use and model mix, and keep model use and outside API charges apart from the licence fee.
The multi-model workspaces a team is most likely to weigh up when one outside person needs limited access to its AI tools.
This table compares multi-model team workspaces with each other on what contractor access needs. Pricing is the lowest-priced paid plan that could be verified for five users, at the monthly rate. Each cell cites the page that documents that cell. Figures checked October 20 2026, and cells marked 'Manual test required' could not be confirmed from public documentation.
The same products again, on the settings that decide how far a contractor can reach: tools, connectors, visibility, limits, training and processing location.
'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 October 20 2026.
The published per-seat price of each major single-vendor team plan, billed monthly, which a team pays for each person who needs all four.
Example only: ChatGPT Business, Claude Team, Gemini Enterprise Business and Grok Business at July 2026 list prices come to about $101 a person a month, or roughly $505 for five people. A cheaper mix is easy to assemble and the plans do not buy the same amount of use, so run the estimator below with your own numbers. Figures checked July 2026.
These drivers change the real cost of contractor access more than any single plan's list price does.
Seven setups, led by the one this guide is about, with what each does when one outside person needs a limited slice of the team's AI.
Each contractor gets a named identity behind one model and policy layer, so an admin sets access in one place.
Best for: Teams that regularly bring in several short-term workers who need more than one model.
Strengths
Trade-offs
One account and password are passed to several people, which looks like a way to avoid another seat.
Best for: No one who handles confidential material.
Strengths
Trade-offs
The contractor pays for their own account and the work stays in it.
Best for: One contractor doing low-risk work outside company systems.
Strengths
Trade-offs
The contractor takes a named seat on the plan the team already uses, with one console and one bill.
Best for: A long-running contractor whose work fits one vendor's models and tools.
Strengths
Trade-offs
Each vendor's own application is bought for the work that needs it.
Best for: Work that needs several vendors' native applications.
Strengths
Trade-offs
The team builds its own interface with its own logins, budgets and permission checks.
Best for: Teams with engineers, repeatable contractor workflows and unusual security needs.
Strengths
Trade-offs
The same workspace plus saved context, so a contractor can pick up a brief quickly and leave reusable work behind.
Best for: Contractors who inherit a project brief and hand work back to a permanent teammate.
Strengths
Trade-offs
Nine stages, read left to right and top to bottom, with only approved context in the project and every stage limited to the contractor's own named account.
A permanent employee checks sources, confidential-data handling and whether the contractor understood the brief before the second model takes over, and the same thread moves to it without another upload. The admin saves the final report, draft or spreadsheet in the team-owned project and not in the contractor's private history, so a teammate continues without asking for the background again. Offboarding then removes the named identity, revokes connector tokens and checks that the project-owned work is still readable.
Products in this category mean different things by the word memory, and for contractors the scope of saved context matters more than its size.
A context window is the material a model can read in one request, and it empties when the chat ends. Memory is stored outside the chat and brought back into later ones. Plain chat history is a saved transcript and does not apply facts to future chats.
WorkLLM has a hierarchy that reaches organization level, and nexos.ai keeps project memory across models. Langdock and TypingMind rely on knowledge bases and project files made by hand, and TeamAI keeps automatic memory personal.
A project is the natural contractor boundary when its member list is limited. Organization-wide memory is broad, so an outside user should not join a workspace whose memory holds unrestricted internal material, and Langdock's company search keeps source permissions instead of copying them.
Ask who can inspect a saved fact, who can correct or delete it, which projects can retrieve it and whether a temporary chat can skip memory and history. Ask whether removing the contractor removes only access or also team-owned work.
Five steps test what a contractor can open and what they cannot, using real work but starting with low-sensitivity documents.
Record every AI subscription and its owner, monthly or annual billing, active employees and contractors, last active date, models actually used, native tools that cannot be replaced, connected repositories and applications, and renewal dates.
Use a mix such as cited web research, drafting from a company brief, contract or policy analysis, spreadsheet analysis and image generation where relevant. Keep the same files and instructions across the products you test.
Create one contractor group and one isolated project, then test the negatives as well as the positives. Can the contractor open the assigned project, see another client's project, pick a prohibited premium model, exceed the assigned limit or connect a personal data source, and what remains after the account is removed.
Track time from invitation to first useful output, how often context is repeated, manual edits needed, subscriptions still required, active use by person and by model, cost attributable to contractor work, time to onboard and time to revoke, and whether project work stays after removal.
Ask whether model access can be set for one contractor, whether a limit is a hard cap before spend or only a report, and whether it is measured in dollars, credits, tokens, messages or characters. Ask whether a contractor can see company-wide memory, whether connected systems keep their original permissions and whether projects have separate member lists. Ask whether context carries when models change, who owns chats and files after removal and whether admins can inspect and correct saved memory. Ask whether a temporary chat that leaves no history exists, where each model processes data and whether the contract bars provider training on customer content. Ask whether access and connector tokens can be revoked at once and whether logs show who used which model and when.
The safest practical setup is a named contractor account inside a project-scoped workspace, with only the models, connectors and usage allowance the job needs. It is safer than a shared login and usually narrower than a full seat. Public documentation shows the clearest person-level budget and limit controls on nexos.ai and TypingMind, project roles on Aymo, source-permission search on Langdock, and credits with a per-person limit on Playgram.
Pricing and limits change often, and plan names alone are not comparable. A credit limit is not the same as a dollar budget, shared memory helps only when its scope is clear and removing access cannot pull back anything a contractor already copied. Several feature and processing claims remain undocumented and need a written answer or a manual test. The final choice should rest on a pilot with the team's own contractor workflow and not on the size of a model list.
What the team is choosing between is a seat that gives a contractor the whole workspace and a named account that gives them one project. The setup around the models decides that as much as the model list, since model access, limits and memory scope either follow the person or follow the whole team. Test it first with one real contractor on low-sensitivity files, and try to open what they should not be able to see.
Upgrade as needed, and only pay for what you actually use
Save ~17% with the annual plan
Pro
Perfect for small and medium teams
Unlimited users & infinite memory
Multi-LLM chats
Granular access control to models
EU data residency
Ultra
Best for large, growing teams
Unlimited users & infinite memory
Multi-LLM chats
Unlimited use of DeepSeek V4 Flash
Granular access control to models
Choose US or EU data residency
Enterprise
Get in touch
For organizations with advanced needs
Unlimited users & SSO
Priority Support
Unlimited use of DeepSeek V4 Flash
Granular access control to models
Choose US or EU data residency
30-days money back guarantee
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: