AI Agents & Automation
Private AI that runs on your infrastructure and keeps your data in region.
For organizations whose data can't go to a public AI API, whether because of your industry's rules, your contracts or your own policy. We deploy open-weight models such as Llama, Mistral and Qwen on your cloud or hardware, with access control, logging and an honest cost comparison against hosted APIs.
- A person replies within one business day
- A plan you can act on
- You own every asset
- Timeline
- Working deployment in 4 to 10 weeks
- Engagement
- Setup, then optional managed operation
- Hosting
- Your cloud region or your own hardware
- Languages
- Arabic and English where needed
Reviewed by Noor Khan
Private AI· Your cloud · in region
SampleAn open-weight model runs on a server inside the company's own cloud, a team member signs in and gets an answer, the link to public AI APIs stays locked so no data is sent out, and every request is kept in the company's own log.
- Model runs in your cloud
- Your team signs in
- No data leaves
- Every request logged
Illustration with sample data. Your version uses your accounts and numbers.
Brands Noor has worked with
Overview
What is private AI?
Private AI is running a large language model on infrastructure you control, such as your own cloud account or on-premise servers, so prompts, documents and outputs never pass through a third-party AI provider. It usually means an open-weight model, such as Llama, Mistral or Qwen, served with in-region hosting, your own access control and your own logs. You decide where the data lives, who can reach the model and how long anything is kept. Noorscale selects and tests the model, fine-tunes it where that pays off, deploys the serving stack and compares its cost with hosted APIs before you commit.
See it in action
What does a private LLM deployment look like when it's working?
It looks like the live sample below, built with illustrative data. Your version runs on your own accounts and your numbers.
AI chat agent · website widgetNorthwind Accounting · pricing page
Chat ready
CRM record · created by the agent
Filling in- Source
- Industry
- Locations
- Transactions
- Owner
Routing rules
Waiting- Good fit: offer a call with a specialist
- Later stage: send a resource, add to nurture
- Discount, complaint or 'a person, please': a teammate joins
Illustrative chat. Your agent answers only from your approved content, books into your team's calendars and brings in a person by the rules you set.
Sound familiar?
What problems does a private LLM deployment solve?
It solves the three problems teams raise most often on a first call, and each one has a fix.
The problem
- “Our compliance team won't approve a public AI API”Customer, patient or financial data can't leave your region or your control, so every AI idea stalls at the security review.
- “We don't know if self-hosting is worth it”GPU prices, model choices and staffing questions pile up, and nobody has compared the real cost against a hosted API at your volume.
- “Open-weight models stumble on our language and terms”Out of the box, many models struggle with Arabic text, sector vocabulary and your document formats, and staff stop trusting the pilot.
What changes
- AI your security team can approveData stays in your region and your accounts, so AI projects stop stalling at the compliance review.
- Costs you can plan aroundYou pay for capacity you control, compared up front with hosted API pricing at your real volume.
- Models that handle your language and termsArabic and English, sector vocabulary and your document formats, tested and tuned on your own data.
- Free to switch AI providersOpen-weight models behind a standard API mean you can swap in a better model when one ships.
Compare your options
Hosted AI API, in-region API or a private LLM?
Each option suits a different stage. Here's how they compare on the points buyers ask about most, including where the others do well.
| Compared on | Hosted AI API | In-region hosted API | Private LLM by Noorscale |
|---|---|---|---|
| Where your data goes | Hosted AI APIProvider's servers, business terms | In-region hosted APIProvider's servers in your region | Private LLM by NoorscaleYour cloud account or hardware |
| Model quality | Hosted AI APIStrongest frontier models | In-region hosted APIStrong models, where offered in region | Private LLM by NoorscaleClose on focused tasks, benchmarked |
| Cost at low volume | Hosted AI APICheapest to start | In-region hosted APICheap to start | Private LLM by NoorscaleHigher, you pay for capacity |
| Cost at high, steady volume | Hosted AI APIPer-token fees add up | In-region hosted APIPer-token fees add up | Private LLM by NoorscaleCan be lower, compared up front |
| Ongoing effort for your team | Hosted AI APIVery little | In-region hosted APIVery little | Private LLM by NoorscaleA platform to run, or we run it |
| Best when | Hosted AI APIRules allow a third-party AI provider | In-region hosted APIRules need in-region data, volume is low | Private LLM by NoorscaleRules, contracts or volume demand control |
On the free call, Noor will tell you which option suits your stage, even when it's one of the others.
What's included
What does a private LLM deployment include?
A private LLM deployment includes six deliverables, each one working material your team can use, not a slide deck.
Scope · Private & In-Region AI6 deliverables
- 01
Model selection and benchmark
Shortlisted open-weight models tested on your own tasks and languages, compared on quality, speed, hardware needs and license terms.
- 02
Inference serving on your infrastructure
A production serving stack in your cloud region or data center, with autoscaling, quantization where quality holds, and an OpenAI-compatible API your apps can call.
- 03
Access control and audit logging
Single sign-on, role-based access, per-team keys, rate limits and logs of who sent what, kept as long as your policy says and no longer.
- 04
Fine-tuning where it pays
Lightweight fine-tuning, such as LoRA, on your approved examples when testing shows it beats better prompts and retrieval, with a held-out set to prove the gain.
- 05
Arabic and English support
Models and retrieval tested on Arabic and English inputs, including mixed-language documents, with tuning where the base model falls short.
- 06
Cost comparison and runbook
Monthly self-hosting cost set against hosted APIs at your real volume, plus a runbook for updates, scaling, model swaps and incidents.
Tools and platforms we work in
- LLlama
- MMistral
- QQwen
- HHugging Face
- VvLLM
- TText Generation Inference
- DDocker
- KKubernetes
AWS
- AAzure
Google Cloud
Langfuse
Process
How long does a private LLM deployment take?
The work runs in five steps, starting in weeks 1 to 2 and then running as ongoing work. Every step ends with something you can use, so you always know what's landed and what comes next.
Timeline: Working deployment in 4 to 10 weeks
Step 01Weeks 1 to 2
Requirements and data rules
We document where data may live, who may access it, which use cases come first and what your security team needs to approve.
You get: Data rules and first use cases in writing
Step 02Weeks 2 to 4
Model shortlist and benchmark
We test shortlisted open-weight models on your tasks and languages, then recommend hardware sizing with a cost comparison.
You get: Benchmark report, hardware sizing and cost comparison
Step 03Weeks 4 to 7
Deploy and secure
We stand up serving, access control, logging and monitoring in your environment and take it through your security review.
You get: Secured serving stack in your environment
Step 04Weeks 6 to 10
Fine-tune and connect apps
Where testing shows a gain we fine-tune, then connect the first apps, such as a knowledge assistant or a document workflow.
You get: First apps connected, tuned where it pays
Step 05Ongoing
Operate and update
We monitor quality, latency, GPU use and cost, apply model and security updates, and re-run the benchmark when better models ship.
You get: Monitored platform, updates and fresh benchmarks
Reporting
How will you know a private LLM deployment is working?
You'll see it in four numbers on a live dashboard, with a written review every month. Leading indicators come first, then the numbers your finance team cares about.
Monthly reportSample
- Quality against a hosted baselineHigher is better
Scores on your own evaluation set next to a hosted API, so you know what the private model gives up or gains.
- Latency and throughputHigher is better
Response time and requests per second under real load, against the targets your apps need.
- Cost per million tokensLower is better
Full serving cost, including hardware and operations, compared with hosted API prices at your volume.
- Data boundary checksHigher is better
Audit logs and network tests confirming that prompts and outputs stay inside your environment.

15 minutes with Noor
Noor Khan, Founder & CEO
You'll leave with
- Your biggest growth opportunity, in plain terms
- The first two or three moves worth making
- A clear plan for your first milestone
Noor listens first, then shows you the fastest route to the result you want.
Useful to have with you
- Your website address
- The goal or problem you want to solve
- Any numbers you already track
Booking opens on Cal.com. Or send a call request
Who it's for
Who is a private LLM deployment for?
A private LLM deployment works best for teams in one or more of these situations.
- Banks, insurers and fintechs under data-residency rules
- Healthcare providers handling patient records
- Public-sector teams and their suppliers with in-region hosting rules
- Companies with steady, high AI volume where per-token fees add up
Engagement
How is a private LLM deployment scoped and priced?
Pricing is shared after the free call, in a written scope with deliverables, timeline and fee. Never a package, never a surprise.
- Typical for this service
Monthly retainer
Ongoing work against a 90-day plan, for channels that compound month over month.
Usually: Fixed-price setup and benchmark, then a monthly retainer if we operate the platform for you
- Monthly report and strategy call
- Priorities reset every quarter
- No long lock-in
Fixed-scope project
A defined scope, timeline and fee for builds, audits, migrations and launches.
- Deliverables agreed in writing
- Milestones you can check
- Optional care plan after launch
Focused sprint
A short, time-boxed piece of work on one priority, to prove the approach first.
- One clear goal
- Working output, not a deck
- Roll into a retainer or stop
Every engagement includes
- Model benchmark on your tasks and languages
- Serving stack deployed in your environment
- Access control, audit logging and monitoring
- Fine-tuning where testing shows a gain
- Cost comparison and operations runbook
What shapes the scope
- Model size and response-time targets
- How many requests you serve at once
- Cloud GPUs or your own hardware
- Fine-tuning and Arabic support needs
- Depth of your security review
You'll see how each one applies to you in the written scope.

Who leads the work?
Founder-led by Noor Khan
A software engineer turned AI and growth strategist. Noor sets the strategy and architecture on every engagement, reviews every plan before it ships and built the method the whole team works by.
FAQ
Questions about Private & In-Region AI
Something else on your mind? Email grow@noorscale.com and a person will reply within one business day.
How much does a private LLM cost to run?
Cost depends on model size, how many requests you serve at once, your response-time targets and whether you rent cloud GPUs or buy hardware. A smaller model that passes your tests can need far less hardware than a large one. Before you commit, we model the monthly cost at your volume against hosted APIs, and the setup comes with a fixed written scope.
Is an open-weight model as good as GPT or Claude?
It depends on the task, and we'll show you where the gaps are. For focused jobs, such as answering from your documents, classifying requests or extracting data, a well-chosen open-weight model can score close enough on your own test set. We benchmark against a hosted baseline so you decide with evidence.
Does our data ever leave our environment?
In a private deployment, the model, prompts, documents and logs all stay in your cloud account or data center. We enforce the boundary with network rules, verify it with audit logs and document it for your security team. Outbound traffic is blocked except for updates you approve.
Should we self-host or use a hosted API with in-region hosting?
If a hosted API with in-region hosting and business terms meets your rules, it's usually cheaper and simpler at low volume. Self-hosting makes sense when rules or contracts demand full control, when volume is high and steady, or when you need a fine-tuned model. We compare both on cost and risk before you choose.
Do private models work in Arabic?
Yes, we test models on Arabic and English inputs from your own documents, including mixed-language text, and choose or tune the model to match. Quality varies a lot between models and dialects, so we measure it on your data instead of trusting general benchmarks.
When is fine-tuning worth it?
Fine-tuning pays when a model must follow a consistent format, use your terminology or handle a narrow task better than prompting and retrieval can. It's a poor fix for missing knowledge, which retrieval handles better. We try prompts and retrieval first and fine-tune only when a held-out test shows a clear gain.
Can a private deployment meet our industry's rules?
Yes, it's designed around them, with in-region hosting, access control, audit logs, retention limits and documentation your compliance team can review. We support your readiness work, while certification itself stays with your auditors.
Who runs it after launch?
Your team can, with the runbook and training we hand over, or we run it for a monthly fee covering monitoring, updates, scaling and fresh benchmarks. Either way, it runs in accounts you own.
Related services
What pairs well with Private & In-Region AI?
- AI AgentsKnowledge Assistants (RAG)Answers from your own documents, with citations and permissionsExplore
- AI AgentsDocument AIExtract, check and route data from invoices, claims, IDs and contractsExplore
- AI AgentsLLM Fine-TuningOpen-weight models trained on your examples and tested against a baselineExplore
- AI AgentsMLOps & LLMOpsPipelines, evaluation gates and monitoring that ship models and LLM apps safelyExplore
We build Private & In-Region AI for teams in
Free 15-minute call
Tell us what you want to build.
In fifteen minutes you'll have a clear first step for Private & In-Region AI and a plan to reach it. Pick a time below, or write if that's easier.
Prefer email? grow@noorscale.com

15 minutes with Noor
Noor Khan, Founder & CEO
You'll leave with
- Your biggest growth opportunity, in plain terms
- The first two or three moves worth making
- A clear plan for your first milestone
Noor listens first, then shows you the fastest route to the result you want.
Useful to have with you
- Your website address
- The goal or problem you want to solve
- Any numbers you already track
Booking opens on Cal.com. Or send a call request