Your data never leaves your building.
Private AI means running AI models on servers you own, so prompts and documents never go to a public cloud service. Synapse Horizon, a Dubai-based B2B wholesaler, supplies the right-sized on-premise AI hardware; our partners deploy open-weight LLMs on it and support it.
On-premise GPU nodes · Isolated
The problem
Public AI tools are useful, but every request carries your data to a third party. For sensitive information, that is a risk many companies cannot accept.
Every prompt, pasted paragraph and uploaded file sent to a public AI tool is processed on someone else’s servers. That includes contracts, patient notes, source code and client records.
Risk: confidential data outside your control
When staff have no approved tool, they use personal accounts on public services. You cannot see what was shared, how long it is kept, or whether it is used to improve someone else’s model.
Risk: unmanaged use you cannot audit
Cloud AI services may process data in other countries. Data protection rules in the UAE and abroad set conditions on cross-border transfers, and some sectors restrict them further.
Risk: transfers you must justify and document
Compliance context
Keeping AI processing on your own hardware helps support compliance with data protection rules, because fewer third parties and fewer cross-border transfers are involved.
Federal
UAE
Federal Decree-Law No. 45 of 2021
Applies to personal data processing in the UAE outside the financial free zones and sectors governed by their own data laws, including cross-border transfer rules.
Financial free zones
DIFC · ADGM
DIFC Law No. 5 of 2020 · ADGM Data Protection Regulations 2021
Both financial centres have their own data protection regimes, with conditions on transferring personal data to other jurisdictions.
International
EU
Regulation (EU) 2016/679
Can apply to organisations outside the EU that offer goods or services to people in the EU, with rules on transfers outside the EEA.
Sector rules also apply, for example health-data localisation and central bank outsourcing rules. See the industry pages for details, and our guide to UAE data privacy rules for AI.
This is general information, not legal advice. Private AI helps support compliance; it does not guarantee it. Ask your legal or compliance adviser which rules apply to your organisation.
How it works
Step 01
We and our partners review your users, use cases, documents and server room: power, cooling, rack space, network and security.
Step 02
We supply a right-sized Team, Department or Enterprise bundle from Dubai, with the GPUs, storage and networking the workload needs.
Step 03
Our partners install the hardware, deploy open-weight LLMs, connect your documents for RAG and set up single sign-on and access control.
Step 04
Monitoring, updates, spares and on-site help under an agreed support SLA, so the platform keeps running as usage grows.
What runs on it
Open-weight LLMs and vision models cover the use cases most teams ask for first.
A private assistant for drafting, summarising and answering questions, with no calls to external AI services.
Answers grounded in your policies, contracts and manuals, with citations back to the source file.
Code completion and review for your developers, without sharing source code outside the company.
Classify, extract and summarise forms, letters, invoices and reports in bulk.
Image and video models for inspection, safety and document scanning, kept on your own network.
Hardware bundles
Three right-sized starting points. Every bundle is quoted to your users, models and site.
Typically 5–25 users
A first private AI deployment for one team, practice group or pilot project.
Most requested
Typically 25–200 users
Shared AI for a whole department, with several use cases on one platform.
Typically 200+ users
Organisation-wide private AI on a rack-scale cluster you own and control.
Industries
Sectors where sensitive data and sector rules make on-premise AI the practical choice.
Client, credit and trading data kept off public AI tools and inside your data centre.
Banks · Insurers
Patient records processed in your own facility, inside the UAE.
Hospitals · Clinics
Privileged client files searched and summarised on your own servers.
Firms · In-house
Citizen and policy data on government-controlled, isolatable infrastructure.
Public sector
Engineering and operations data kept at site, even on isolated networks.
Utilities · Industrial
Compare
For the numbers, see our worked comparison of on-premise AI vs cloud cost, with a breakeven calculator.
| Factor | Public cloud AI | Private on-premise AI |
|---|---|---|
| Data residency | Data is processed in the provider’s chosen regions, which may be outside the UAE. | Data stays on hardware in your building, under your own controls. |
| Cost predictability | Usage-based fees can rise with users, tokens and new use cases. | An upfront hardware purchase plus power, cooling and a support agreement, with no per-token fees. |
| Latency | Requests typically travel over the internet to the provider’s shared service. | Requests stay on your local network, close to your users and data. |
| Customization | Limited to the provider’s models, settings and fine-tuning options. | Choose open-weight models, connect any internal source and fine-tune on your own data. |
| Vendor lock-in | Prompts, workflows and integrations tie you to one provider’s APIs. | Open-weight models and standard interfaces let you switch models when better ones appear. |
Guides
Private AI and UAE data privacy: what the PDPL, DIFC, ADGM, health, banking and GDPR rules mean for AI use, and how on-premise AI helps support compliance.
On-premise AI vs cloud cost, worked through: GPU-hour rates, hardware, power, PUE and UAE tariffs, and why utilisation sets the breakeven. With a calculator.
Private LLM hardware requirements explained: size GPU memory for model weights and KV cache, pick a precision, and match users to the right AI hardware bundle.
FAQ
Request a quote
Share your users, use cases, documents and server room details. No obligation, and we reply within one business day.