Copilot, ChatGPT and Claude: Differences and How to Choose

Copilot, ChatGPT and Claude are three generative AI assistants offered by different vendors, and choosing among them at work is not settled by a feature ranking. It is settled by a framework of criteria: the use case to serve, integration with the existing ecosystem, data and security requirements, real cost per seat, and the degree of vendor independence the company wants to keep.

Why is a feature comparison not enough?

Generative AI products change quickly. A table of features written today is out of date within a few months, and anyone deciding on that basis is buying a snapshot. A comparison that lasts starts from what is stable: the organization's needs.

There is also a second limit: stated features say what the tool can do, not what it does in your context. The same license can produce value in one department and sit unused in another.

So the starting point is a different question: what job are we choosing for, and under what constraints? The sections below propose five criteria to answer it.

Criterion 1: the use case

The first criterion is what the tool must do, and for whom. Different use cases have different needs: drafting and rephrasing text, summarizing long documents, assisting software development, analyzing data, supporting customer service, searching internal documentation.

List the use cases by department and assess three aspects for each:

• frequency: how many people perform it and how often;
• criticality: what happens if the output is wrong;
• expected value: how much time or quality is recovered.

An AI use case register makes this exercise systematic. Without one, you end up choosing the tool before understanding the problem, which is the most common source of overlapping licenses.

Criterion 2: integration with the company ecosystem

The second criterion is how well the tool fits the environment where people already work. An assistant that lives inside the productivity, email or development tools used every day has a lower barrier to entry than one that requires changing habits.

Assess concretely:

• which applications and company data sources it can work with;
• how identity, access and permissions are managed (for example with single sign-on);
• what technical integration options exist for automated workflows;
• how much effort configuration and maintenance require.

Caution is advisable here: integration capabilities evolve and should be verified with the vendor and through tests in your own environment, not assumed from marketing materials. Deep integration promotes adoption, but it also increases dependence on the vendor, a topic we return to under criterion 5.

Criterion 3: data and security requirements

The third criterion concerns what happens to the data that enters the tool. This is the territory of the DPO and the CISO, and often the one that rules out an option regardless of its other qualities.

The questions to put to each vendor, and to document:

1. Which data is processed and where, and whether there are commitments on location.
2. Whether the content entered can be used to train the models, and under what contract terms.
3. What role the vendor takes under the GDPR (article 28 on the processor) and what safeguards it offers for any transfers to third countries (Chapter V).
4. What access controls, activity logging and data retention are available.
5. How the vendor fits into your supply chain risk analysis, in particular if you are in scope of NIS2.

For processing that presents high risks, an impact assessment may be needed (GDPR article 35). These references are informational and are not legal advice: the final assessment belongs to your DPO and legal counsel. For more detail, see GDPR and generative AI at work.

Criterion 4: the real cost per seat

The list price per seat is only the beginning. The real cost per seat is what the company actually bears, divided by those who use the tool.

A practical formula: add up licenses, any integration costs, training, administration and support, and divide by the number of active users, not by the seats purchased. If you buy 500 seats (a hypothetical number) and 300 use them regularly, the cost per active user is two thirds higher than what appears on the invoice.

Two practical consequences. First: a tool that looks more expensive per seat can cost less per active user if adoption is higher. Second: the cost changes over time with usage, so it must be measured periodically. The method for finding inactive seats and overlaps is described in how to reduce AI license spend.

Criterion 5: the need for vendor independence

The fifth criterion is how far the company wants, or can afford, to depend on a single vendor. The market moves fast: terms, performance and licensing models can change, and the best vendor today may not be the best in twelve months.

Independence has a cost, because managing several tools takes more coordination. Dependence has another cost, less visible: weaker negotiating power and difficulty switching when needed.

To weigh the criterion, ask yourself:

• how hard it is to migrate workflows, prompts and accumulated knowledge to another tool;
• whether your data and configurations are portable;
• whether a choice made today leaves options open or closes them.

There is no answer that fits everyone. Those with uniform use cases can concentrate on one vendor; those with very different needs across departments may prefer a portfolio, provided it is governed.

Why do you need a multi-vendor view?

Applying the five criteria requires comparable data. The problem is that each vendor exposes its usage and cost data in its own console, with different metrics and granularity. Without a common layer, the comparison comes down to impressions and what the vendors themselves say.

A multi-vendor view serves three purposes:

• Compare on facts. Usage and cost per seat, by department and by use case, in the same unit of measure.
• Spot overlaps. Where several tools serve the same users for the same task.
• Negotiate with knowledge. Knowing how much is really used strengthens your position at renewal.

This view makes sense only if whoever provides it is not tied to the vendors being compared.

How do you apply the framework in practice?

A simple path for applying the criteria:

1. List the use cases by department and assign them a priority.
2. For each case, define the minimum data and security requirements together with the DPO and the CISO.
3. Discard options that do not meet the requirements, with no compromise.
4. Test the remaining options with a pilot group and collect usage data.
5. Calculate the cost per active user and assess vendor dependence.
6. Decide, document the reason and set a review date.

Review the comparison at every renewal or when products or terms change significantly.

Where Delphis fits

Delphis is a vendor-independent platform that connects spend, adoption, impact and regulatory obligations. It gathers the usage and cost data of different tools into a single model, so they can be compared by department, role and use case and you can see where they overlap. It does not sell models or licenses, which allows the data to be read without any stake in a vendor. For details on cost analysis, see the Spend page.