Read this first
Specific capability claims about either platform go stale within months. The durable advice is: evaluate both against your own tasks, with your own data, judged by your own experts. That evaluation is worth more than any published comparison.
We build on both platforms and have no incentive toward either. This is a framework for deciding, not a verdict.
What does not differentiate them
- Whether they can do your work. Both handle mainstream business tasks — drafting, summarising, analysis, coding assistance — competently.
- Whether output needs review. Both produce confident errors. Both need a human in the loop for anything consequential.
- Whether adoption is automatic. Neither succeeds without named workflows and support.
- Whether integration is trivial. Connecting either to internal systems is an engineering project with security implications.
The variables that actually determine your outcome — workflow selection, implementation quality, governance, adoption support — are identical on both platforms and entirely within your control.
What legitimately differs
| Dimension | How to assess it for yourself |
|---|---|
| Enterprise controls | Compare current admin, audit and retention features against your requirements |
| Extension model | Custom GPTs and Actions vs skills, plugins and MCP — which fits your architecture |
| Ecosystem and tooling | What already integrates with your stack |
| Commercial terms | Pricing at your seat count, and contractual data commitments |
| Task performance | Test on your work — general benchmarks predict this poorly |
| Existing usage | What your teams already use informally |
Verify the current state of each dimension directly with the vendor. Features, limits and terms change often enough that a comparison written today is unreliable by next quarter. Treat published comparisons — this one included — as a list of questions to ask, not as answers.
An evaluation that produces a defensible decision
- Choose five real tasks your teams do repeatedly, with known-good historical outputs.
- Run both platforms on the same inputs, with equivalent effort spent on each.
- Have the domain experts judge blind — not the person who ran the test, and without knowing which output came from where.
- Score against your criteria: accuracy, format, how much editing was needed.
- Separately assess governance against your security and compliance requirements.
- Price at realistic seat counts, including support and any development.
- Decide, and write down why. You will revisit this.
Blind judging is the part most evaluations skip and most need. Whoever ran the pilot has a preference by the end of it, and unblinded scoring reliably confirms that preference. Stripping the labels costs nothing and changes results more often than people expect.
Reasonable tie-breakers
| If this is true | It reasonably points to |
|---|---|
| Teams already use one informally | That one — adoption is half the battle |
| Your stack integrates with one more readily | That one |
| One meets a hard compliance requirement | That one, decisively |
| Your evaluation showed a clear task-quality gap | The better performer on your work |
| Neither differentiated | Commercial terms; decide and move on |
Avoiding lock-in without avoiding a decision
- Keep workflow logic in your own systems where the value justifies it, rather than entirely in platform configuration.
- Prefer standard integration approaches over deeply proprietary ones where both are available.
- Document your prompts and processes outside the platform — this is the asset, and it is portable.
- Accept some lock-in. Architecting for total portability costs more than switching would, and produces a worse implementation in the meantime.
The honest summary
Organisations that get value from AI assistants are not distinguished by which vendor they chose. They are distinguished by having picked specific workflows, supported adoption properly, set clear governance, and measured against a baseline. Spending three months on vendor comparison and three weeks on implementation is the wrong ratio.
Evaluating platforms, or ready to build on one? Tell us the workflows you have in mind — we build on both. See our Claude and ChatGPT services, and why AI pilots fail.