Cloud use case · Innovate
AI workloads on cloud(Azure OpenAI, Vertex AI, Bedrock)
Run generative AI on your own documents and systems, with a clear answer to the question legal will ask first: where is the data processed?
Why companies ask us to plan their AI workloads
Microsoft Solutions Partner for Modern Work
and Google Cloud Premier Partner. We are also an AWS partner and a Huawei Cloud partner.
Since 2007
with 1,500+ active cloud customers across the Middle East.
Offices in Dubai, Muscat (head office) and Doha
serving all six GCC countries.
We train teams too
generative AI training, online or in person.
Clouds we architect and deploy AI workloads on
Microsoft Azure
Google Cloud
AI workloads on cloud means running large language models and AI services, such as Azure OpenAI in Microsoft Foundry, Google Vertex AI and Amazon Bedrock, against your own data.
Gulf Infotech, working in the Middle East since 2007, helps companies choose the platform, the region and the deployment type that keeps processing where their rules require.
A closer look
What problem does this solve?
What buyers tell us:
The hard part is rarely the model. It is choosing where the model runs, which of your data it may see, and how you will pay for it at scale. The GCC adds one more layer: in-region AI options are narrower than the provider's headline model list suggests.
- "We want an assistant that answers from our policies and contracts, not from the internet."
- "Our customer service team copies data into public chatbots and we have no control."
- "Compliance says customer data cannot leave the country. Can we still use GPT or Gemini?"
- "We tried a pilot and the monthly token bill surprised everyone."
Who is it for?
Teams that only need AI in email and documents usually start with Microsoft 365 Copilot or Gemini in Google Workspace, not a cloud AI build.
| Profile | Typical first workload |
|---|---|
| 50+ staff with large document libraries (legal, engineering, real estate) | Typical first workloadSearch and Q&A over internal documents |
| Customer-facing teams (retail, travel, telecom resellers) | Typical first workloadArabic and English assistant on website or WhatsApp. See WhatsApp chatbot |
| Finance and operations | Typical first workloadInvoice, contract and form extraction |
| Banks, insurers, health and government | Typical first workloadSame uses, but only on deployments that keep processing in-country |
| Software companies | Typical first workloadAI features inside their own product |
Which AI options run in GCC cloud regions?
Checked against each provider's official model and region tables in October 2026. These tables change often; we re-check before every design.
| Provider | AI service | GCC regions | What runs in-region | What runs only through global routing |
|---|---|---|---|---|
| Microsoft Azure | AI serviceAzure OpenAI in Microsoft Foundry | GCC regionsUAE North (Dubai). Qatar Central does not appear in Foundry's model tables | What runs in-regionRegional Provisioned deployments in UAE North for models including gpt-4.1, gpt-4o, gpt-5-mini, gpt-5.1, o3-mini and o4-mini; standard regional deployment only for embeddings and Whisper | What runs only through global routingGlobal Standard deployments, which may process prompts in any Azure region. Data stored at rest stays in the Azure geography |
| Huawei Cloud | AI serviceModelArts, Pangu models | GCC regionsRiyadh; Abu Dhabi (UAE) upcoming | What runs in-regionAsk us | What runs only through global routingAsk us |
| Google Cloud | AI serviceVertex AI | GCC regionsme-central1 (Doha), me-central2 (Dammam) | What runs in-regionGemini embedding and text and multimodal embedding models | What runs only through global routingGemini models are not offered on the Doha or Dammam regional endpoints. Google states endpoints do not guarantee in-region ML processing |
| AWS | AI serviceAmazon Bedrock | GCC regionsme-central-1 (UAE), me-south-1 (Bahrain) | What runs in-regionAmazon Nova Pro and Nova Lite in me-central-1 | What runs only through global routingAnthropic Claude and most other models, called from the GCC regions through global cross-region inference |
| Any provider | AI serviceOpen-weight models on your own GPU servers | GCC regionsGPU machines listed in Dammam (G4 and G2) and AWS UAE (G5, G6, G6e, P5en). Azure UAE North: | What runs in-regionEverything, because you host the model | What runs only through global routingNot applicable; you run and patch it |
Takeaway: if processing must stay in the UAE today, Azure Provisioned deployments in UAE North and self-hosted models on GPUs are the realistic routes. In Qatar and Saudi Arabia, self-hosting is the main in-country route for generative models.
Which AI option should you pick?
| Option | Best when | Trade-off |
|---|---|---|
| Azure OpenAI, Global Standard | Best whenData rules allow processing outside the GCC and you want the newest models at the lowest entry cost | Trade-offNo in-region processing guarantee |
| Azure OpenAI, Regional Provisioned in UAE North | Best whenUAE processing is required and usage is steady | Trade-offYou pay for reserved capacity whether you use it or not |
| Amazon Bedrock from me-central-1 | Best whenYou build on AWS and want Claude and other models through one API | Trade-offMost models route globally; only Amazon Nova runs in the UAE region |
| Vertex AI with Gemini | Best whenYour data is in Google Cloud or BigQuery and global processing is acceptable | Trade-offGemini is not on the Doha or Dammam regional endpoints today |
| Open-weight model on GPU servers in-region | Best whenProcessing must stay in Qatar, Saudi Arabia or the UAE, and volumes justify a server | Trade-offYou run, secure and update the model yourselves |
A closer look
How is AI on cloud priced?
There are three pricing models, and the choice changes the bill more than the model does.
When answers can wait, batch processing helps: Bedrock lists batch inference as a lower-cost option, and Azure's batch API returns results within 24 hours. We estimate the monthly cost from your real document and user volumes before the pilot ends.
- Pay per token: Azure OpenAI Standard and Amazon Bedrock on-demand charge for input and output tokens, priced separately. Cheap to start, variable at scale.
- Reserved capacity: Azure OpenAI Provisioned (with monthly or annual reservations) and Bedrock Provisioned Throughput (hourly per model unit, with no, one-month or six-month commitment). Predictable, but paid whether used or not.
- GPU servers: self-hosted models pay for the GPU virtual machines by the hour, plus storage, with the usual reservation discounts for steady use.
What have we built?
Our delivered work is workflow automation on Power Automate, Copilot Studio, Gemini Workflow Builder and Zoho Flow. GPU servers for self-hosted models can run on Azure, Huawei Cloud or Google Cloud; we help you choose and set them up. See our case studies.
What does Gulf Infotech do?
Assess
we pick one or two use cases with a measurable payoff, classify the data involved and check which rules apply to where it may be processed.
Design
we choose platform, region and deployment type, design retrieval over your documents, sign-in and permissions, and estimate token or GPU cost at real volumes.
Build and migrate
we build the pilot, connect it to your data sources and test answers in English and Arabic with your own staff.
Run
we monitor usage, cost and answer quality, update models when providers retire versions, and train users. See AI training for teams.
Readiness assessment
What are the typical steps?
Tap a step to see what you get
01Pick the use case
Frequently asked questions
The best cloud for AI is the one that offers your chosen model in a deployment your data rules allow. For UAE in-region processing, Azure OpenAI Provisioned in UAE North is the most direct option. For Claude through one API, Amazon Bedrock fits, with global routing. For data already in BigQuery, Vertex AI fits. Where nothing else qualifies, a self-hosted model on GPUs does.
Partly, and it depends on the deployment type. Azure stores data at rest in the UAE geography for UAE North deployments, and Regional Provisioned deployments also process prompts there. Global deployments on Azure, Bedrock and Vertex AI may process prompts outside the GCC. For Qatar and Saudi Arabia, self-hosting an open model on in-country GPU servers is the clearest route today.
It depends on volume and pricing model, so there is no flat figure. Pay-per-token deployments cost little in a pilot and grow with usage. Reserved capacity costs the same every month whether used or not. Self-hosted models cost the GPU servers by the hour. We estimate monthly cost from your real users and documents during the pilot, before you commit.
A first pilot on one use case is mostly limited by how quickly your documents can be connected and how soon real users can test it, not by the cloud. We agree a pilot plan with dates after the assessment and keep the scope to one process so results come quickly.
Arabic quality differs by model and by task, such as Gulf dialect versus Modern Standard Arabic, so we test rather than assume. During the pilot we run your real questions in Arabic and English against two or three candidate models and pick the one with the best measured answers. Your staff score the answers, not us and not the provider.
Read the provider's data-use terms for the exact service before you connect company data, because the answer is set by contract, not by the model. We review those terms with you for the service and deployment type you choose, record the answer in the design document, and only then connect real data.
Start with one use case and a clear data answer
Tell us the process you want to improve and where your data must stay. We will come back with a platform, a region and a pilot cost.