What no training does and does not cover, which products document retention periods and sub-processors, and the evidence to ask for before sensitive work moves
Aug 18, 2026 · 13 min read
A team that needs AI which does not train on its content should buy a business workspace with an explicit no-training commitment, documented retention controls, a current sub-processor list and evidence an administrator can see in the product. The commitment has to cover the workspace vendor and every model provider that receives a prompt. The eight workspaces compared on the same criteria below are Playgram, WorkLLM, nexos.ai, Langdock, TeamAI, Aymo, Magai and TypingMind.
The phrase itself is easy to publish and hard to verify, and it answers only one of five questions. The others are who receives the data, where it is processed, how long each copy remains, what memory derives from the original content, and what an administrator can restrict, inspect, export and delete.
This guide sets out what the eight products publish on each of those, and where a marketing claim outruns the documentation. It ends with the evidence to collect before sensitive work moves in, and a controlled test that checks deletion rather than trusting it.
You are replacing unapproved consumer accounts and need evidence rather than assurances.
You read the terms and the privacy page and have to reconcile the two before signing.
Roadmaps, interview notes, client files and HR documents pass through prompts every week.
One or two people working only with public information on a single approved product.
Each subscription creates its own privacy boundary, retention rule and administrative surface, and nobody is looking at all of them together.
Separate business plans duplicate model access and charge for provisioned people whether or not they use them. As an example, four single-vendor team plans came to about $101 per person a month at July 2026 list prices, taking ChatGPT Business at $25, Claude Team at $25, Gemini Business at $21 and Grok Business at $301, 2, 3, 4. Read it as an illustration rather than a rate, because a cheaper mix is easy to assemble. On this topic the review effort matters as much as the fee, since each product adds a retention policy, a sub-processor register and a set of terms for somebody to read. Idle seats are also hardest to find when departments buy their own, and each provider defines usage and minimum members differently.
People switch tabs, copy prompts and download then upload the same file again, so one sensitive document passes through several vendors in an afternoon. Two risks follow. Nobody can reliably remember which account carries the approved business terms, and moving a task between models leaves several stored copies of the same material behind.
Chat history is usually private to one account, so instructions, decisions and corrected facts never become team knowledge. The team then repeats the same client brief, policy or project background in each tool, which costs time and creates more copies of material that may be sensitive. Memory makes that worse rather than better unless its scope is clear, because a stored summary or extracted fact is another copy under another rule.
Consumer accounts leave a manager unable to verify which model received a prompt, whether the work happened in a business workspace, or whether history and training controls were on. Nobody can say which integrations reached the conversation either, or whether a person's content was deleted after they left. A business workspace helps only when it shows those facts to an administrator, because a policy saying your data is protected is not operational evidence.
Five checks that turn a policy statement into something an administrator can show an auditor. The fourth is where most products in this group stop.
Approved OpenAI, Anthropic, Google, xAI and open-model endpoints should sit behind one controlled interface, and the workspace has to use business or API terms that stop providers training on prompts and outputs. An admin should be able to disable a model whose region or retention fails policy.
Check each product for web search and cited research, document drafting, spreadsheet analysis, image generation, video generation, code review or execution, chats that leave no history, and MCP or API connections. Every one of those is another path your content can take out of the workspace.
Workspace history, backups, memory databases, application logs and model-provider retention are five different periods, and most vendors publish one. Ask for all five, and look for a retention control an admin can set rather than a paragraph describing a general practice.
Logs should identify person, model, period and ideally the project, and deletion should appear as an event rather than as a promise. Add a current sub-processor register with change notices, permissions that follow user and group, and workspace ownership that survives somebody leaving.
Hard caps or automatic blocking should act before unapproved spending happens rather than appear in a report afterwards. Teams handling sensitive one-off work should also look for a documented mode that leaves no history or long-term memory, which is the clean answer for a task nobody should retain.
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, and cells marked 'Manual test required' could not be confirmed from public documentation.
The same products on the criteria this topic turns on: what each one does besides chat, what it connects to, what an admin can see and cap, and where your content is processed and kept.
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. Public documentation is also not a substitute for the signed processing agreement and provider schedule. Checked August 2026.
The published per-seat price of each major single-vendor team plan, billed monthly. Each one also brings its own retention policy and its own sub-processor list to review.
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. On this topic the review effort matters as much as the price, since four products mean four retention policies, four sub-processor registers and four sets of terms for a legal team to read. Figures checked July 2026.
Cost is the secondary question on this topic, and two of these are paid in legal and security hours rather than in subscription fees.
Six setups, ordered by how much an administrator can verify rather than by how strongly each one is described.
One vendor routes requests to several providers under one policy, one set of logs and one contract. That vendor becomes an additional processor, so it has to prove what it sends onward, what it stores and what it logs.
Best for: Teams with non-public work across more than one model.
Strengths
Trade-offs
The same setup where an admin chooses how long content is kept and which models may be used. Langdock documents inactive-chat retention of seven days, one month, three months, twelve months or permanent, and flags models whose provider keeps data longer33.
Best for: Teams with a data-classification policy to enforce.
Strengths
Trade-offs
Standardise on a single vendor's business plan and review one set of terms. Governance is simpler because there is one policy, one console and one processor to name in a register.
Best for: Teams whose work fits one approved ecosystem.
Strengths
Trade-offs
Buy each vendor's business product for the native controls. The capability is strong and the review effort multiplies, because four products mean four retention policies, four registers and four consoles.
Best for: Regulated teams that need each vendor's own terms.
Strengths
Trade-offs
Run the product on your own infrastructure so the workspace data stays in a database you control. TypingMind documents self-hosted team deployments where workspace data sits in the customer's own MySQL database, while cloud-hosted plans store it on the vendor's servers37.
Best for: Teams with infrastructure requirements written into contracts.
Strengths
Trade-offs
Engineers select the endpoints, databases, logs and deletion jobs directly. It gives the most control over where content goes, and the team then owns authentication, the interface, routing, monitoring, retrieval security and incident response.
Best for: Regulated or technical teams with engineering capacity.
Strengths
Trade-offs
An unreleased launch plan in a restricted project, where the members are named, the classification is recorded and the last step is a deletion check rather than a handover.
A person removes personal data and marks unsupported assumptions before the work moves on, and sends anything thin back to the analysis stage. The approved output is stored at project level rather than in an unrestricted company store, and closing the project means exporting the result, deleting the drafts and confirming that the retention job covers chats, files, indexes and backups.
Memory is a storage system with a friendlier name, so on this topic it belongs in the retention review rather than in the convenience column.
A context window is the information placed into one request, and it creates no durable knowledge by itself. Chat history is a stored transcript that nothing retrieves for you. Memory is stored outside the window and pulled into later work, which makes it a data store with its own retention rule.
WorkLLM documents organisation-level memory and nexos.ai keeps shared project context. Langdock and TeamAI both keep automatic memory personal to one account. Magai and TypingMind rely on knowledge bases somebody builds, and Aymo advertises team memory without its controls35, 37, 38.
Find out whether an entry is a verbatim message, a generated summary, an embedding or an extracted fact, and which model providers receive it when it is retrieved. Then ask whether project memory is isolated from other projects and whether a sensitive session can bypass memory and history entirely.
Check that users can inspect and correct an entry, that an admin can delete one, and that deleting a chat also removes anything derived from it. TeamAI notes that turning memory off can take up to 24 hours to stop influencing responses, which is the kind of delay to find before it matters38.
A vendor-neutral plan that collects evidence rather than assurances, and ends by checking that deleted content is really gone.
Ask each candidate for the processing agreement naming it as processor, the current sub-processor register with a way to be notified of changes, and a model inventory showing provider, region and retention class. A vendor that cannot produce those three for the plan you would buy has answered the question already.
Write down four periods for each product: workspace history, backups, application logs and model-provider retention. They are different numbers and vendors often publish only one. Note which models carry longer provider retention, since some are flagged for abuse review rather than for training.
Set up a project with real but classified material, invite only the people who should see it, and run the work across two models with a review stage between them. Check that the second model received the approved project context rather than a fresh upload, and that nothing reached an unrestricted store.
Delete a chat, a file and a stored memory entry, then search for them in history, in project search and in the memory interface. Ask whether deleting a source chat also removes anything derived from it, and record how long each disappearance took rather than accepting a policy statement about it.
Confirm usage exports by person, model and period, deletion events in an audit log, model restrictions, and budgets that block before an overage. Then reconcile the terms against the privacy page, because a no-training statement and a broad content licence can sit in the same contract.
A team can answer the no-training question by moving approved work into a governed business workspace, and that phrase is only the first control. A credible setup also shows who receives the data, where it is processed, how long each copy remains, what memory derives from the original content, and what an administrator can restrict, inspect, export and delete.
The public evidence is uneven, and it is worth saying which way. nexos.ai publishes a sub-processor register with locations. Langdock documents practical retention periods and labels models whose provider keeps data longer. TypingMind offers the strongest architectural control through self-hosting while its cloud plan adds vendor-side storage. WorkLLM states zero retention at the model layer and publishes less about its own. TeamAI's broad content licence needs legal review, Aymo's provider commitment is narrower than its headline, and Magai gives a purge window while its logs have no stated end.
None of that is a ranking, and none of it replaces a signed agreement you have read yourself. Plans cannot be compared by name, public pages are not the processing agreement, and retention policies change. So run the work through a controlled test with your own material, then take every unsupported or contradictory point to the contract.
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