A founder I spoke to recently had bought ChatGPT Team licences for her 12-person consultancy. Six months in, two contract reviewers had quietly switched to Claude on personal accounts, while the marketing team was perfectly happy with ChatGPT. She was effectively running two platforms with no central policy and no clear sense of which to keep. The seat price was manageable. The drift was the problem.
That is a recognisable place to land. Both platforms cost almost the same, share similar privacy commitments, and do genuinely useful work. The question is which one fits your workflows, and what it costs when you guess wrong.
What is the choice you’re actually facing?
Both ChatGPT Team and Claude Team give your business a private AI workspace, protected from model training by default, with basic admin controls and team management included. Per-seat cost is nearly identical, around £24 to £25 per user per month on an annual plan. The meaningful differences are in what each tool does well, who on your team will use it, and what your workflows actually demand.
Both vendors confirm that data from paid business plans is not used to train their models unless customers opt in. Both support single sign-on and role-based access at team tier, with finer controls available at enterprise level. Data residency is where the two diverge: ChatGPT Enterprise and API now offer explicit EU and UK processing options, introduced in late 2025. Claude Team’s protections work best when accessed via AWS Bedrock in the AWS UK South London region, rather than through the standard claude.ai web interface, which stores data in US-controlled environments.
If you are weighing the two for the first time, a sharper question than “which is better?” is simply: which platform fits the work my team actually does?
When does ChatGPT Team make sense?
ChatGPT Team is the better starting point when you want one general-purpose AI workspace across multiple roles and departments. Independent small-business testing consistently finds it stronger for mixed teams covering sales, marketing, operations and leadership, largely because of its broader feature set, easier onboarding for occasional users, and a custom GPT ecosystem that lets non-technical staff build lightweight automations without needing a developer.
Built-in image generation via DALL-E and real-time voice give small teams producing multi-format content a practical advantage, without additional tools. If marketing, customer communications, or social content is central to your workload, ChatGPT tends to produce fluent first-draft copy with less prompt-tuning required.
ChatGPT is also the cleaner choice on data residency if you are not already committed to AWS or Google Cloud infrastructure. Explicit EU and UK processing options at enterprise tier give a straightforward answer to ICO questions about where data sits, without requiring changes to your existing cloud setup.
This platform tends to suit broadly distributed teams with varied roles, a marketing or customer-comms workload, and no deep dependency on AWS or Google Cloud.
When does Claude Team make sense?
Claude Team is the stronger fit when documents are at the centre of your work. Its context window supports up to 200,000 tokens, roughly 300 pages of text, which is nearly double the 128,000-token limit on standard ChatGPT business plans. For contract review, policy analysis, compliance documentation, or any workflow where your team regularly works with long, dense documents, that difference is immediately practical.
In legal, healthcare, and regulated financial services firms, Claude tends to be the preferred choice, partly because of that context window and partly because its outputs lean towards precise, careful analysis rather than fluent conversational prose. FCA-regulated firms considering AI for drafting compliance notes or financial promotions will need human oversight regardless, but many find Claude’s more considered style easier to review and audit.
If your team is already standardised on AWS or Google Cloud, deploying Claude via AWS Bedrock in the London region simplifies data-residency compliance and keeps your IAM, logging and security controls in one place. Independent testing also finds Claude’s Microsoft 365 integration performs well for SharePoint and OneDrive content at scale.
This platform tends to suit document-heavy or regulated operations, teams already on AWS or Google Cloud, and businesses with significant legal, compliance, or technical workloads.
What does it cost to choose the wrong one?
The seat-price difference between the two platforms amounts to under £150 a year for a 10-seat team, and that number is almost beside the point. The real expense when you land on the wrong tool is the time and management overhead of onboarding your team to workflows that do not fit, then either migrating or running both in parallel.
Training a team of 20 to 50 people to use an AI platform consistently, including prompting habits, governance rules, and what not to paste in, takes real management time. SME buyer guides suggest that a poorly planned platform switch can double that overhead, with teams running both tools in parallel for 12 to 18 months while they settle.
A secondary cost is shadow IT. When the central platform does not fit all major workflows, departments make their own choices. Your legal team migrates to Claude. Marketing stays on ChatGPT. Central policy breaks down, and the data risk that pushed you towards a paid plan leaks back in. The March 2023 Samsung incident, in which engineers pasted confidential source code into the public ChatGPT interface, is the extreme version of what happens when staff fall back on consumer accounts because the company-provided tool does not do what they need.
Integration debt is a further consideration. Building automations around one vendor’s custom GPT ecosystem or API structure, then deciding to switch, means rewriting those integrations. The CMA’s 2023 review of AI foundation models flagged this risk directly, warning that deep dependence on proprietary tooling creates switching costs that go well beyond the licence fee.
What should you ask before you decide?
Before committing to either platform, the most useful exercise is to list the three to five workflows your team will use AI for in the first six months, then ask honestly whether those workflows are breadth-led or depth-led. Breadth-led work, meaning many people doing varied everyday tasks, favours ChatGPT. Depth-led work, meaning a few people going deep into long documents or complex analysis, favours Claude.
Two data-protection questions matter before you sign anything. Does any proposed workflow involve personal data? If so, the ICO’s guidance on AI and data protection sets out when a DPIA is required, and using either tool with customer or employee data will typically trigger that threshold. Does the vendor’s data-residency arrangement match what you have told clients and staff about where their data goes? For ChatGPT that means checking which plan tier includes EU or UK processing options. For Claude it means understanding whether your team will access it through the web interface or via AWS Bedrock.
If you are in a regulated sector, check whether each vendor’s documentation is sufficient for your regulator. FCA-regulated firms need to evidence governance and oversight of AI-assisted workflows. The SRA has published guidance on AI in legal practice. These are not reasons to avoid the tools, but both require more than a standard terms-of-service review.
Finally, plan for running both at some point. Research from Andreessen Horowitz in 2024 found that 81% of large enterprises use three or more AI model families concurrently. Many firms that start with one platform add the other within 18 months for specialist work. If you design your initial rollout around two or three core use cases rather than trying to cover everything at once, the choice between these two platforms becomes easier, and adding the second later is straightforward.
