Choose Claude if…
- Your work is writing-, analysis-, and judgment-heavy
- You want strong controls and careful, on-brand output
- You want a capable default that's fast to roll out
AI platform guide
Claude, OpenAI, or a private model — which AI platform should your business run on?
Most teams pick an AI tool before they've decided what actually matters: who owns the data, how it connects to your work, and what it costs to run at scale. Here's a practical, vendor-neutral way to choose — and how we set up whichever one fits.
The short answer
Start with the question, not the tool.
For many established businesses, an official Claude or OpenAI commercial offering is the practical starting point: it is fast to deploy, and business inputs and outputs are not used for model training by default. That default is subject to the selected product, settings, feedback or opt-in choices, and contract. A private deployment can fit stricter residency needs, but only when every data path is scoped. The decision comes down to four things: data, customization, connections, and cost.
Side by side
Claude vs. OpenAI vs. a private model.
| What matters | Claude (Anthropic) | OpenAI (ChatGPT) | Private / in-house |
|---|---|---|---|
| Best for | Reasoning, writing, and careful work with business controls | Broad adoption, custom GPTs, and building on a deep API | Strict privacy, data residency, and high-volume use |
| Data & privacy | Official commercial products: excluded from training by default; verify settings and contract | Official commercial products: excluded from training by default; verify settings and contract | Boundary depends on the full deployment and every connected data path |
| Customization | Projects and custom assistants per team | Custom GPTs and fine-tuning options | Full control — your models, prompts, and data |
| Connects to your tools | Connectors to your systems and data | Custom GPTs + a broad API ecosystem | Built to your stack — more setup, fewer limits |
| Cost model | Per-seat / usage-based | Per-seat / usage-based | You pay for infrastructure, not per seat |
| Setup effort | Fast to stand up for a team | Fast to stand up for a team | Higher — infrastructure and security work |
| Who controls it | Vendor-hosted; the client controls the configured workspace | Vendor-hosted; the client controls the configured workspace | The client controls the deployment; licences and cloud terms still apply |
Training defaults: see the official OpenAI business-data policy and Anthropic model-training policy. Provider policies last reviewed August 1, 2026. Features, settings, feedback programs, and contracts change, so treat this as a decision framework and verify the selected product.
How to choose
Pick by what your business actually needs.
Choose OpenAI if…
- Your team already uses ChatGPT and likes it
- You want custom GPTs and a deep API to build on
- You value the largest ecosystem of integrations
Choose a private model if…
- Data residency or privacy rules require it
- You want to control the deployment and avoid per-seat pricing
- You run high volumes where hosted costs add up
The honest take
The platform matters less than the setup around it.
A blank chat box doesn't change how a team works. The value comes from tuning the platform to your industry, connecting it to your tools, and training your people to use it. That's the same whichever platform you pick — and it's exactly what we do.
Common questions
Choosing an AI platform — FAQs.
Is our data safe with Claude or ChatGPT for business?
OpenAI and Anthropic state that official commercial, business, enterprise, and API inputs and outputs are not used for model training by default. Product settings, feedback or opt-in programs, and the governing contract can change that treatment, so confirm the exact product and configuration. A private deployment does not create a boundary by itself; connectors, logging, telemetry, and backups must also be scoped. Provider policies last reviewed August 1, 2026.
What is a private or in-house AI model?
It is an open-weight model, such as a Llama- or Mistral-family model, deployed on infrastructure the client controls. Data can remain within the configured environment only when the model, connectors, logging, telemetry, and backups are all scoped that way. The client controls the deployment; software licences and cloud terms still govern ownership.
Do we have to pick just one?
No. Many teams run a hybrid: a hosted platform for everyday work and a private model for sensitive data or high-volume tasks. The right mix depends on your data sensitivity, budget, and existing tools.
How long does setup take?
A hosted platform like Claude or OpenAI can be configured for a team quickly. A private in-house deployment takes longer because of infrastructure and security setup. We scope timing on a call based on the platform, connections, and customization you need.
Not sure which fits?