What four direct vendor plans cost per person, what a multi-model workspace gives up in return, and how to tell which of the two your team is actually buying
Aug 18, 2026 · 13 min read
A team that genuinely needs several vendors is usually better served by one multi-model workspace than by four direct plans per person, because the operational load falls rather than the price necessarily does. A team whose work sits inside one provider should keep the plan it has. The eight workspaces compared on the same criteria below are Playgram, WorkLLM, nexos.ai, Langdock, TeamAI, Aymo, Magai and TypingMind.
The direct route is easy to price and hard to run. As an example, four single-vendor team plans came to about $101 per person a month at July 2026 list prices, so five fully provisioned people cost roughly $5051, 2, 3, 4. That figure buys availability rather than use, and OpenAI states that an unused Business seat is not refundable for the period already billed32.
This guide sets out which model categories a team actually needs, what a workspace gives up against buying direct, and how the eight products differ on included usage, memory and controls. It ends with a pilot that measures the whole working setup rather than the plan price.
Separate accounts for two or more providers have appeared without anyone deciding to buy them.
One project moves through research, analysis, drafting, code and visuals in a week.
You need one place to see model spending and to decide who can reach which model.
One provider reliably covers the work, or one person wants an occasional second opinion.
Separate subscriptions divide the budget, the daily workflow, the project context and the administrative control, and each division has its own price.
Every person who needs all four direct plans adds the whole stack again. As an example, those four 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. Five fully provisioned people came to roughly $505 and ten to about $1,010, and it is an illustration rather than a rate because a cheaper mix is easy to assemble. Per-seat plans charge for availability rather than use, and OpenAI states that unused Business seats are not refundable for the period already billed32. Expensive frontier access also lands on occasional users, because direct plans cannot pool activity across vendors.
People switch tabs, sign-ins and interfaces, then adapt the same prompt for each product when they want to compare two answers. Files often have to be uploaded again when the work moves to another provider. Native tools differ as well, so a workflow that runs smoothly in one product does not transfer cleanly to the next one.
Chat history stays inside the provider that created it, so project instructions and uploaded files do not follow a person to another model. A decision taken in one product is invisible to a teammate working in another, which turns a model change into a small re-onboarding exercise unless the workspace keeps the thread and its attachments. That is the part teams underestimate when they buy on the model list, because access to every model is worth little when every model begins from a blank prompt.
Each vendor keeps its own roles, billing, analytics and retention settings, so no manager can easily see total model spending by person or by project. Offboarding means removing access from several systems on the same day, and useful prompts and outputs stay inside personal chat histories with no clear owner. Nobody can answer whether the team still needs all four plans, which is the question that started the review.
Five checks on any product sold as access to everything. The first one is about coverage and the last one is where the advertised price stops being the real one.
Look for current models from OpenAI, Anthropic, Google and xAI, plus the specialist or lower-cost options a role needs. Then check that switching models inside a thread does not mean copying the previous conversation, because access to everything is worth little if each model starts empty.
Model access and tools are separate purchases, so check every product for web research with citations, document work, spreadsheet analysis, code review, image generation, video generation and chats that leave no history. A creative team may need one text model and real image tools rather than four.
Files, instructions, decisions and approved outputs should belong to a project that authorised teammates can open, with groups, roles and a named owner around it. That is what stops each new model and each new person starting from a blank prompt.
An admin should see activity and cost by person, model and period, cap spending before a charge arrives, restrict expensive models, and control who reaches which projects, integrations and stored knowledge. A new starter should get one account with a small default model set rather than four logins.
Five shapes are sold here: per seat, per workspace with a member cap, included credits, credits plus overage, and a platform fee with model calls on your own keys. Price your mix under each and ask what a credit buys on the strongest model, because a low platform fee can still produce a high total.
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 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. This is the stack a one-plan workspace is bought to replace.
Buying all four for one person came to about $101 a month at July 2026 list prices, so five fully provisioned people came to roughly $505. 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. A workspace fee is not directly comparable either, since included usage, credit multipliers on frontier models and separate provider charges all sit on top of it. The estimator further down runs the comparison on your own numbers.
Two of these are on the invoice every month and two are found only when somebody compares provisioned access against actual use.
Six routes to the same model families, from one workspace to a set of direct plans. The workspace options come first because they are the subject here.
Several vendors sit behind one interface, one invoice and one set of roles. The commercial shape varies widely, so WorkLLM and Langdock sell seats, TeamAI and Aymo sell workspace plans with member caps, and TypingMind charges for the workspace while model calls go on your own provider keys6, 13, 17, 21, 25.
Best for: Teams whose work crosses two or more model families.
Strengths
Trade-offs
The same setup, plus project context stored where the next model and the next teammate can retrieve it. Access to every model matters less when each one starts from an empty prompt, which is what this fixes.
Best for: Teams whose projects run across models and people.
Strengths
Trade-offs
Assign each provider to the roles that need it rather than giving everyone everything. It reduces the bill against a full stack and keeps the billing and the context split across vendors.
Best for: Teams with a few specialists loyal to one provider each.
Strengths
Trade-offs
Standardise on a single vendor and accept its model list. There is one identity system, one invoice and one set of policies, which is the lowest administrative load of any option here.
Best for: Teams whose work fits one ecosystem.
Strengths
Trade-offs
Buy each vendor's enterprise offering for the native security, support and integrations. Procurement, security review, provisioning and analytics then repeat once per vendor, and prices are usually custom rather than published.
Best for: Regulated organisations that need each vendor's own terms.
Strengths
Trade-offs
Engineers embed the models in your own product or internal tools, metering usage closely. API access is not a finished workspace though, so authentication, history, file handling, monitoring, budgets and provider failover all have to be built.
Best for: Engineering-led teams embedding models in a product.
Strengths
Trade-offs
A product launch where each stage picks the model that suits it, with the brief and the decisions held by the project rather than by whoever is at the keyboard.
Each stage uses the model that suits it while the project holds the brief, so nothing is pasted forward between them. A person approves the positioning before it ships and sends unsupported claims back to the challenge stage, and image or video work only joins the flow when the campaign genuinely needs it.
Reaching every model matters less than what each one is given when it arrives, which is the part the four shapes of memory decide.
A context window is the material temporarily available to the current request. Chat history is a record of earlier messages. Memory is stored context retrieved in a later interaction, and only the third one reaches the next model or the next person.
Some keep history alone. Some retrieve from files and instructions somebody configured. Some store preferences automatically for one person, which Langdock documents with a 50-entry personal store. Only the last shape, retrieved by authorised teammates across chats, is what WorkLLM documents33, 7.
Project permissions should be separate from workspace-wide ones, so one project's context does not surface in unrelated work. Ask which boundaries exist before you move anything in, and whether a sensitive session can be kept out of history and memory entirely.
Does the same project context reach the next model and the next authorised teammate without leaking into unrelated projects? Run that once during the trial, then check that somebody can inspect a stored entry and correct it, because access to every model is worth little if each one starts blank.
A vendor-neutral plan that compares the whole working setup rather than the advertised prices. It runs about two weeks.
List every AI subscription with its owner, billing cycle and active users, then record which models and native tools each person actually opens. Mark the duplicated seats and the personal accounts being used for company work, and separate chat-model needs from image, video, coding and office-suite needs.
Use real tasks that cross model strengths, such as research into a report, proposal drafting, spreadsheet analysis, code review or campaign production. At least one should move between models partway through, and at least one should be picked up by a second person.
Check that the workspace covers a strong general model, a second for hard reasoning, a web-connected research option and the specialist tools your roles need. Ask which models are included today, which need your own provider keys, and whether frontier access is full or heavily multiplied against credits.
Record time to a useful first answer, repeated context, manual edits, uploads and tool changes, which model produced the best result, and whether a teammate could continue unaided. Then add the actual credit consumption and the subscription overlap you could remove.
Ask whether an admin can restrict expensive models, cap spending before an overage and read usage by person, model and period. Then check whether project permissions are separate from workspace-wide ones, where each model processes data, and what happens when a provider renames or retires a model.
A team that regularly needs GPT, Claude, Gemini and Grok should compare the direct per-seat stack with the full working cost of a workspace. That means its credits, any provider charges, the native tools it does not replace and the administration it removes. A workspace usually improves access and continuity, and the lowest advertised plan is not automatically the lowest working total.
Most teams should also stop sending every task to every frontier model. A capable default for daily work, expensive models kept for difficult work and explicit access for the specialists who need research, coding or creative tools all cost less than giving everyone everything. That pattern is also easier to explain than a routing rule.
Four limits apply. Plan names are not comparable, because included usage, tools and controls differ behind them. Stored context helps only when its scopes and correction controls are clear. Model line-ups and prices move, so a decision taken on today's list needs revisiting. And the reliable answer comes from a pilot on your own files rather than from a feature count.
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: