Pricing comparison of AI assistants for small teams 2026

Small teams today face the same problem: how to achieve real time savings while keeping costs under control. In practice, the most commonly compared tools are writing assistants, meeting notes, internal knowledge bases, and day-to-day work with documents. Below is an overview of specific services that are actually available on the market, including indicative pricing and links to official websites.

Important: prices vary by region, billing model (monthly/annual), VAT, and account type. Treat them as an indicative overview as of March 11, 2026.

Real AI assistants for small teams

If you want a quick overview, focus on these 5 services:

ChatGPT Team – a universal assistant for content creation, source analysis, summaries, and working with data.
Claude Team – strong for longer texts, internal documentation, and working with context.
Gemini for Google Workspace – suitable for teams already living in Gmail, Docs, and Sheets.
Microsoft Copilot for Microsoft 365 – the best fit for companies on Microsoft 365 (Outlook, Teams, Word, Excel, PowerPoint).
Make AI – good for smaller teams that want AI directly in their internal wiki and project documentation.

Indicative price comparison (as of March 11, 2026)

ChatGPT Team: usually from ~USD 25 / user / month with annual billing (the monthly option is usually higher, typically around USD 30).
Claude Team: usually in a similar range to competitors’ team plans (often from ~USD 25–30 / user / month, depending on billing).
Gemini for Workspace: pricing consists of the Google Workspace plan + the AI component (varies by tier and region).
Microsoft Copilot for Microsoft 365: usually from ~USD 30 / user / month (typically with an annual commitment).
Make AI: the AI add-on is usually approximately from ~USD 10 / user / month on top of the base Make plan.

If you want the lowest possible entry cost, the combination of Make + Make AI or a smaller number of ChatGPT Team licenses often works well. However, if the team is strongly tied to the Microsoft or Google ecosystem, it is usually operationally more efficient to stay within it and pay extra for the native assistant.

What to consider when comparing price

The seat price itself is only the first layer. For small teams, the overall impact on the process is more important in practice:

Onboarding time: how quickly the team learns to use the tool in its regular workflow.
Integrations: whether the tool can work directly with your documents, emails, and meetings.
Output quality in Czech: for marketing, support, and internal texts, this is a crucial factor.
Governance and security: audit logs, roles, and handling sensitive data.
Actual ROI: how many hours per month the tool saves compared to manual work.

Practical scenario for a team of up to 10 people

A typical setup that works:

Content and research: ChatGPT Team or Claude Team.
Documentation and know-how: Make AI.
Operations in the office suite: Copilot or Gemini depending on the company’s main ecosystem.

This gives you a “best-of-breed” model without unnecessary lock-in: one tool for text work, another for internal knowledge, and a third for daily operations in emails and documents.

When one assistant makes sense vs. a combination

One assistant makes sense when the team is small, processes are simple, and you want minimal administration.
A combination of 2–3 assistants makes sense when you have different departments (marketing, sales, support) and each needs a different type of AI work.

For teams of up to 10 people, it is usually reasonable to start with one service for 4–6 weeks, measure the impact (time, quality, number of iterations), and only then add another tool.

FAQ

Which assistant has the best price/performance ratio for a small team?

Without company context, this cannot be determined in one sentence. The best price/performance ratio is often delivered by the tool that creates the least friction with your existing stack (Google/Microsoft/Make) and that the team actually uses every day.

Does it make sense to pay for multiple assistants at once?

Yes, but only after a pilot. First validate one tool on real use cases. If you run into limits (e.g. weaker documentation work or poor integrations), only then add a second one.

Is monthly or annual billing better?

Annual billing is usually more cost-effective, but the monthly option is safer for a pilot deployment. For a small team, the ideal approach is usually a 1–2 month test first, then a switch to an annual plan.

Conclusion

If you are comparing AI assistants for a small team, do not start only with the seat price. Track total operating costs, output quality in Czech, integrations, and real time savings. For most smaller teams today, the most practical approach is a pilot deployment of 1 service, followed by impact measurement, and only then a possible expansion to a combination of multiple tools.

Sources of illustrative images

Stock photo: source
Stock photo: source

The custom illustrative image was created using the OpenAI Images API.

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