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Noorscale

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

Sample

An 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.

  1. Model runs in your cloud
  2. Your team signs in
  3. No data leaves
  4. Every request logged

Illustration with sample data. Your version uses your accounts and numbers.

Brands Noor has worked with

  • Brand North
  • Olympic Behavioral Health
  • White Light Behavioral Health
  • Rubicon Recovery Center
  • Quest 2 Recovery
  • The Nestled

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

Sample

Chat ready

northwind.example/pricing
NNorthwind assistantAI · replies in seconds · a person on request
Sample conversation, plays automatically

CRM record · created by the agent

Filling in
Source
Industry
Locations
Transactions
Email
Owner
Fit score86
StatusCall booked · Wed 10:00

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.

Hosted AI API, in-region API or a private LLM?
Where your data goesHosted AI APIProvider's servers, business termsIn-region hosted APIProvider's servers in your regionPrivate LLM by NoorscaleYour cloud account or hardware
Model qualityHosted AI APIStrongest frontier modelsIn-region hosted APIStrong models, where offered in regionPrivate LLM by NoorscaleClose on focused tasks, benchmarked
Cost at low volumeHosted AI APICheapest to startIn-region hosted APICheap to startPrivate LLM by NoorscaleHigher, you pay for capacity
Cost at high, steady volumeHosted AI APIPer-token fees add upIn-region hosted APIPer-token fees add upPrivate LLM by NoorscaleCan be lower, compared up front
Ongoing effort for your teamHosted AI APIVery littleIn-region hosted APIVery littlePrivate LLM by NoorscaleA platform to run, or we run it
Best whenHosted AI APIRules allow a third-party AI providerIn-region hosted APIRules need in-region data, volume is lowPrivate 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

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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.

Noor Khan

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
See available times

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.

Book a free private AI call
  • 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.

Noor Khan, founder of Noorscale

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.

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

Noor Khan

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
See available times

Booking opens on Cal.com. Or send a call request

Book a free 15-minute call