Comparison — updated 14 July 2026
Four questions, one decision
Use cases, data, budget, sovereignty: a four-step decision framework for choosing your enterprise AI, with typical scenarios and platforms.
In brief
Choosing an enterprise AI comes down to four questions, in order: which use cases do you want to equip, where can your data legally and reasonably go, what budget and cost model can you sustain, what level of sovereignty are you aiming for. The answers sketch a typical scenario — SaaS assistant, API platform, private AI or hybrid — and only then the choice of a vendor. Starting from the product datasheet is the most common mistake.
Which enterprise AI to choose? A decision guide
'Which AI should we choose?' is the question we hear most in a first meeting — and it is almost always the wrong first question. ChatGPT or Claude, OpenAI or Anthropic, cloud or on-premise: these trade-offs only make sense once the foundations are set — your use cases, your data constraints, your budget, your sovereignty requirement.
This guide walks through the decision framework we apply on assignments, laid out as a four-step tree. At each step, a question, the criteria to answer it, and what each answer implies. At the end, a table of typical scenarios connects company profiles to the architectures and platforms that suit them. The aim is not to sell you a platform — we are partners of both OpenAI and Anthropic, and we also deploy open models — but to spare you the months lost to a poorly framed choice that then has to be unwound.
Which enterprise AI to choose: the four questions that decide
Every organisation that chooses well follows, knowingly or not, the same path: use cases, data, budget, sovereignty — in that order. Use cases first, because an AI with no named use cases is a cost centre: the disappointing deployments we audit almost always share one trait — the tool was bought before anyone knew what it was for. Data next, because it sets the non-negotiable constraints: what may travel to a US cloud, what must stay within your perimeter, what the law — GDPR in Europe, Law 09-08 in Morocco — permits or forbids.
Budget third, because it arbitrates between very different cost models: per-user licence, API consumption per token, or infrastructure investment. Sovereignty last, because it is a strategic slider — from 'SaaS with contractual guarantees' to 'air-gapped in my own datacentre' — that commits far beyond the IT department.
These four questions build on one another: each answer narrows the space of the choices that follow. A company that skips a step pays for it later — usually at the compliance audit or the first surprise API bill. The four sections that follow detail them one by one.
Step 1 — which use cases do you want to equip?
Map before you choose. We distinguish four families of use cases, with very different implications. Augmented office work — drafting, summarising, translation, meeting preparation — potentially concerns every employee: it calls for a SaaS assistant such as ChatGPT Enterprise or Claude Enterprise, with a per-user licence. Intensive line-of-business use cases — contract analysis in legal, credit-file review, customer support — call for specialised assistants: often the same SaaS foundation, enriched with internal corpora via Projects, GPTs or RAG.
Application use cases — AI embedded in your products and processes, customer chatbot, document extraction, scoring — go not through licences but through the API, billed per token, with engineering work involved. Software engineering use cases, finally — coding assistants, development agents — deserve their own line: dedicated tools such as Claude Code or Codex, whose value is measured as much in the human time to review as in generation speed.
The practical exercise: list your ten most promising use cases, sort them by family, estimate the population concerned and the expected value. This one-page document steers everything else — and it will help you measure, six months on, whether the promise has been kept.
Step 2 — where can your data go?
Classify your data into three circles. The first: public or low-sensitivity data — product documentation, marketing content, non-proprietary code. It can travel through any serious SaaS offering with no particular precaution beyond the standard contract. The second: internal and personal data — working documents, customer data, HR. It requires the guarantees of enterprise offerings — no training on your data, encryption, configurable retention — and, depending on your jurisdiction, certain formalities: in Morocco, processing falls under Law 09-08 and cross-border transfers must be framed with the CNDP; in Europe, the GDPR imposes its transfer clauses.
The third circle: critical data — trade secrets, defence data, data subject to strict sector requirements (banking, health, operators of vital importance under DGSSI directives in Morocco). For these, the question is no longer contractual but architectural: private on-premise AI, a trusted cloud, or plain exclusion from the AI perimeter altogether.
This classification decides the architecture: if almost all of your use cases fall into the first two circles, a well-governed enterprise SaaS is enough, possibly complemented by a private enclave for the third circle. Our private AI versus cloud AI comparison details this trade-off.
Step 3 — what budget and what cost model?
Three cost models coexist, and many companies will combine all three. Licences: on the order of 25 to 30 dollars per user per month for mid-tier offerings according to public price lists, on quotation for Enterprise plans — several tens of dollars per seat. A predictable cost, proportional to the headcount equipped: this is the model for augmented office work. Sizing rule: start with the populations with proven daily usage, then widen in waves.
The API: billed per million tokens — on the order of 1 to 5 dollars for input and 10 to 25 dollars for output for frontier models according to the public price lists of July 2026, five to ten times less for the lightweight variants. A variable cost that tracks your application volumes: unpredictable if no one models it, highly optimisable — caching, model routing, batch processing — if someone takes charge of it.
Infrastructure, finally, for private AI: purchase or rental of GPU servers — on the order of a few thousand to a few tens of thousands of dollars per month — plus the skills to operate them. It is justified only above a certain volume or where sovereignty is required. To put figures on all of this in dollars and dirhams, our AI cost calculator cross-references the three items with editable prices.
Step 4 — what level of sovereignty are you aiming for?
Sovereignty is not a switch but a slider with four positions. Position one: SaaS with contractual guarantees — the enterprise offerings of OpenAI and Anthropic, data outside your perimeter but protected by contract and certifications. This is the majority choice, and it is legitimate for the first two circles of data. Position two: API through a cloud you already have under contract — Azure, AWS Bedrock, Google Vertex AI — which brings regional data residency and integration with your cloud agreements, without fundamentally changing the nature of the risk.
Position three: private AI on dedicated infrastructure — open models (Llama, Mistral, Qwen…) served from your own datacentre or a national host. Data no longer leaves; in return, you operate the platform and accept a capability gap against the frontier models. Position four: air-gapped — physical disconnection, for sovereign or critical industrial perimeters.
The classic trap is over-specifying: demanding position three for second-circle data costs dearly in lost capability and delay. The reverse — under-specifying — is paid for at audit. Set the slider perimeter by perimeter, not globally: that is the whole point of hybrid architectures, which reserve the sovereign enclave for the data that justifies it.
The typical scenarios and their recommended platforms
Let us cross the four answers into typical scenarios — the table below summarises them. The services SME with no critical data will go for a single SaaS assistant, chosen on the basis of the existing ecosystem and rolled out with a simple usage policy: it is the shortest path to a first tangible benefit. The mid-market firm with document-heavy functions — law firm, engineering, finance — will combine a SaaS assistant for everyone with enriched spaces (Projects, GPTs, RAG) for the core functions; the ChatGPT-or-Claude choice comes down to the dominant use cases, and our dedicated comparison helps you settle it.
The product or platform company will reason API-first: the choice is made on value for money per task, with a multi-model abstraction layer to stay reversible. The bank, the insurer or the regulated operator will build a hybrid: governed SaaS for office work, a private enclave or trusted cloud for third-circle data, unified governance on top. The inherently sovereign organisation — defence, strategic sectors — will start from open models on-premise, accepting the engineering investment.
Across every scenario, two constants: governance written down from day one, and an evaluation on your own data before any multi-year commitment. If your situation fits none of the boxes — which is common — that is precisely the work of a scoping diagnostic.
| Critère | Profile | Recommended architecture | Platforms to evaluate |
|---|---|---|---|
| Services SME, low-sensitivity data | 50-300 employees, augmented office work | Single SaaS assistant + usage policy | ChatGPT Business/Enterprise or Claude Team/Enterprise |
| Mid-market firm, document-heavy functions | Legal, engineering, finance, audit | SaaS for all + enriched spaces (RAG, Projects, GPTs) | Claude Enterprise and ChatGPT Enterprise, possibly both |
| Product / platform company | AI embedded in products, API volumes | API platform + multi-model abstraction layer | OpenAI API, Anthropic API, via Azure / Bedrock / Vertex AI |
| Bank, insurer, regulated operator | Three-circle data, strong compliance | Hybrid: governed SaaS + private enclave for the critical | Enterprise offerings + open models (Llama, Mistral) on-premise |
| Sovereign organisation (defence, strategic) | Requirement that data never leaves | Private on-premise AI, or even air-gapped | Open models: Llama, Mistral, Qwen, per evaluation |
When to choose a SaaS platform (ChatGPT / Claude Enterprise) or an API or private architecture?
Choose a SaaS platform (ChatGPT / Claude Enterprise) if:
- Your dominant use cases are augmented office work and line-of-business assistants
- Your sensitive data stays within the first two classification circles
- You want time-to-value in weeks, not quarters
Choose an API or private architecture if:
- AI is embedded in your products and processes: significant API volumes
- Part of your data falls into the critical circle or under sovereignty requirements
- You have — or want to build — in-house AI engineering capabilities
In summary
Our verdict
There is no 'best enterprise AI' in the abstract: there is an architecture matched to your use cases, your data, your budget and your sovereignty slider — in that order. The healthy path fits into six weeks: mapping the use cases, classifying the data, a costed budget scenario built with our calculator, choosing the architecture, then a comparative evaluation of the candidate platforms on your real cases before any multi-year commitment. Beware the two symmetrical shortcuts: buying the fashionable platform without any scoping, and over-specifying sovereignty out of blanket caution. If you want an independent view on your situation, that is exactly what our platform advisory is for.
Frequently asked
What is the best AI for a company in 2026?
The one that fits your scenario, not the ranking of the moment. For augmented office work, ChatGPT Enterprise and Claude Enterprise lead; for application AI, the choice comes down to the API with the best cost per task; for critical data, on-premise open models are the natural fit. The use-cases-data-budget-sovereignty scoping precedes the choice of vendor.
Should you choose between ChatGPT and Claude, or take both?
Below 200 users, choose one: governance simplicity wins. Above that, the dual-vendor scenario becomes relevant — each population on the tool where it excels, reduced dependency, stronger negotiation. A six-week cross pilot on two contrasting functions gives a factual answer at little cost.
What budget should you plan to get started with AI in the enterprise?
Start from the public price lists rather than a ready-made figure: mid-tier offerings are listed at around 25 to 30 dollars per user per month, while Enterprise plans are on quotation, at higher orders of magnitude that taper with volume and commitment length. Add a provision for support in the first year, cost the API use cases separately, by volume, and set it all against the vendors' quotations. Our AI cost calculator lets you simulate your configuration in dollars and dirhams.
How do you know whether your data can go to the cloud?
Classify it into three circles: public; internal and personal; critical. The first two are compatible with enterprise SaaS offerings, subject to the applicable formalities — Law 09-08 and the CNDP in Morocco, the GDPR in Europe — and a usage policy. The critical circle calls for a private architecture or exclusion from the AI perimeter.
How long does a well-run platform selection take?
Six to eight weeks for a full journey: mapping the use cases, classifying the data, budget scenarios, then a comparative evaluation of the candidates on 50 to 200 real cases. It is a modest investment against the cost of a poorly framed choice that then has to be unwound — migration, renegotiation, adoption to restart.
Sources
- Claude pricing — anthropic.com — accessed July 2026
- OpenAI API pricing — openai.com — accessed July 2026
- CNDP — personal data protection in Morocco (Law 09-08) — accessed July 2026
- Amazon Bedrock pricing — aws.amazon.com — accessed July 2026
Need an independent view?
We deploy both platforms and open models. An hour of discussion is often enough to settle a trade-off that has dragged on for months.
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