Many UAE companies now ask the same question: should we give people a hosted AI assistant, or build a private one inside our own environment? The honest answer is that it depends, and the factors that decide it are mostly about your data and your rules, not about which model is more impressive.
This article gives you a framework for the decision. It is a comparison of approaches, not a verdict on any product. Where we mention third-party details, we mark them for confirmation, because they change.
Two approaches in plain terms
A hosted enterprise assistant is a service run by a vendor. Your people sign in, ask questions and sometimes connect it to company sources. The vendor operates the model and the infrastructure, and you agree terms on how your data is handled.
A private LLM deployment runs inside infrastructure you control. It is connected to your own knowledge and systems. You decide where it runs, which models it uses and how it is governed. You, or a partner on your behalf, operate it.
Both can be valuable. They differ in control, effort and fit.
The seven questions that decide
1. Where does your data live?
This is often the first question. Ask where prompts, documents and outputs are stored and processed, for how long and who can access them.
With a private deployment, the answer is whatever you decide: your cloud, your VPC or your own data center. With a hosted service, the answer is whatever the vendor offers.
Residency rules vary by sector and change over time. Your legal and compliance teams should decide which ones apply.
2. How are permissions handled?
An assistant connected to company knowledge must respect who is allowed to see what. If a document is restricted in the source system, the assistant should not use it to answer someone without access.
Ask how the product applies source permissions, whether it keeps them in sync and how you can test it. A good test is simple: connect a restricted document and check that a user without access gets nothing drawn from it.
3. Does every answer cite its source?
Citations let people verify answers, and they make the difference between a helpful tool and a risky one. Ask whether answers show their sources, whether those sources link to the original and what happens when the knowledge does not cover a question. A good assistant says so instead of guessing.
4. What is logged for audit?
If an AI can answer questions and take actions, you need a record. Ask what is logged: queries, answers, sources, actions and approvals. Ask who can see the logs and how long they are kept.
5. Which systems does it connect to?
Value comes from connecting to where your knowledge lives. List your systems and ask for each one: is there a connector, what does it cover and how are permissions handled? Check read and write access separately.
6. Can you choose and change the model?
Models improve quickly, and your policies may limit which ones you can use. Ask whether you can choose the model, whether you can switch, and where the model is hosted. A private deployment can support commercial or open models, hosted where you require.
7. How is it deployed and operated?
Consider the effort on your side. A hosted service is generally quicker to start. A private deployment needs setup, security review and ongoing governance. Ask who will run it, who monitors it and who handles incidents.
Where a private deployment fits
A private LLM deployment tends to suit organizations that:
- Handle sensitive or regulated data
- Want data to stay in their own environment or country
- Need answers filtered by existing permissions across many systems
- Need a full audit trail
- Want to choose or change models
- Plan to run agents that take actions under approval gates
Where a hosted assistant may be enough
A hosted assistant can suit organizations that:
- Mainly want a general-purpose assistant for individuals
- Work with low-sensitivity data
- Prefer the simplest possible adoption path
- Have limited capacity to operate a platform
The trade-off is less control over data location and governance. Check that against your policies.
Effort and cost to consider
Compare more than the license. Include the effort to connect your sources, to configure permissions, to govern content over time and to satisfy your security team. A cheaper option that takes months to approve can cost more than a pricier one that clears review quickly. We do not quote prices here, and you should ask every vendor for a scoped estimate against your own requirements.
How to run a fair evaluation
- Write your requirements first, including data location, permissions and audit.
- Pick ten real questions from your teams, including some that touch restricted documents.
- Run the same questions through each option and record the answers and their sources.
- Test what happens when the answer is not in the knowledge.
- Check that a user without access to a document gets nothing drawn from it.
- Ask each vendor to show you the audit trail for the test.
- Compare the total effort to deploy, govern and maintain each option.
We offer a structured comparison of the two approaches on these questions.
What your security team will ask
Whichever route you take, expect your security and compliance teams to ask a consistent set of questions. Prepare answers early, because they often decide the timeline more than the technology does.
- What data goes into the system, and what comes out?
- Who can access it, and how is that access reviewed?
- Where is it stored and processed, and for how long?
- Is it used to train any model outside our control?
- How are incidents detected and handled?
- What happens to our data if we stop using the service?
A private deployment lets you answer many of these with your own policies. A hosted service lets you answer them through the vendor's documentation and contract. In both cases, get the answers in writing.
Think about adoption, not only architecture
A platform nobody uses has no value, however well it is governed. Plan how people will actually adopt it: which team goes first, which questions it should answer well, who owns the knowledge it draws on and how you will measure usefulness. A small pilot with a clear owner usually teaches more than a broad launch.
A note on governance
Whatever you choose, the quality of answers depends on the quality of your knowledge. If policies are outdated, duplicated or contradictory, any assistant will reflect that. Give each important document an owner and a review step, and look for gaps in what people ask. This is as important as the choice of model.
Where to go next
Our Enterprise AI security page explains how private deployment, access control and audit work on our platform. You can also explore the platform overview and the four modules.
