Azure Migration Services
Azure migration services for the AI systems we build — deployed into your tenant, not ours.
A compliance-regulated client’s leadership asked the only question that matters: “Whose cloud does this live in?” We had built their reporting app — AI models included — in our Azure tenant. Then we migrated it into theirs, with documented deployment options, a security review, and a monthly cost estimate up front.
Built with the leading AI platforms
NVIDIA
Anthropic
The hard question
Application migration to Azure is the step nobody scoped.
From a deployment-planning call with a multi-site US organization under a compliance-certification regime: the AI reporting app was built and tested — in our tenant. Now leadership wanted it in theirs, and the security team wanted evidence of everything.
“Whose cloud does this live in?”
The app works, the demo lands, and then leadership asks the question. While the AI system runs in a vendor’s tenant, your data sits outside your security perimeter — and under a compliance certification, that answer doesn’t hold.
The move was never scoped
Building the AI system had a plan; moving it into your tenant didn’t. The database, the AI models, the keys, the logs — each one needs a documented landing spot, and “we’ll figure it out at deployment” is where go-lives stall.
The security review starts too late
Your IT and security team meets the system for the first time at deployment and asks for evidence of everything inside the environment — what’s being installed, what it talks to, which ports it opens. Every undocumented answer is another week of review.
Anything that leaves the environment is a finding
Every external call the AI makes — a model endpoint, a logging service, a package feed — has to be named, firewall rules included. Undocumented traffic is how a deployment fails review.
The question-mark price tag
Nobody approves “it depends.” Without a monthly recurring cost estimate for everything deployed, finance and security both hold the pen — and the system sits in someone else’s tenant meanwhile.
Month six is unstaffed
Vulnerability scanners keep finding things after go-live. Who patches, who remediates, and how fast — if that isn’t written down before launch, it becomes an argument after it.
What the undocumented way costs
Without an Azure migration strategy, go-live is a moving date.
What it looks like when a purpose-built AI system has to cross into a client’s tenant without a plan. Your numbers will differ — the step-by-step document we write first puts figures on yours before anything ships.
From an anonymized engagement — a multi-site US organization under a compliance-certification regime; a reporting application with AI models inside
How it works
The Azure security assessment passes before anything ships.
We build the AI system, prove it in our own Azure environment, then migrate it into yours — through your security review, not around it. Three steps, fixed order.
- 01
Write the deployment down, steps 1 through 20
Every resource the system needs — the database, the containerized app, the AI models, Key Vault, Log Analytics, storage — plus what each piece talks to inside and outside your tenant, the firewall rules, the ports, and how patching works. Your IT and security team reads it before we touch anything.
- 02
Pass your security review
Your team runs its review — vulnerability scans, traffic inspection, the evidence your compliance certification requires. Whatever the scanner finds, we remediate within an agreed window; the findings and the fixes are documented either way.
- 03
Deploy into your tenant, one of three ways
A single container image your team pulls from a container registry and deploys; a managed build inside your tenant with credentials you issue; or a scripted provisioning run — an Azure CLI script your engineer executes, reviews, and keeps. You pick; the monthly cost estimate comes with all three.
Azure managed services, only if you actually want them.
Most Azure managed services pitches are 24/7 operations contracts for your whole estate — that is not what this is. We are an AI operating partner: we advise, build, and run the machine. After go-live we can stay on — monitoring through Azure Monitor logs or a dashboard your team reads, patching, and remediating whatever the vulnerability scanners find — on a plain monthly arrangement.
It is optional by design: the handover is complete — documentation, provisioning scripts, a walkthrough with your engineer — so ongoing support is a choice you make, not a dependency we engineered. Teams that find us searching for an Azure AI implementation partner or Azure OpenAI consulting usually discover the deployment question decides everything; the support question is the one you get to answer freely.
Led by Peter Enestrom.
Questions
Azure migration checklist questions, answered straight.
What IT, security, and finance ask before anything ships.
What’s holding your business back?
A workflow ready for automation. An AI product you want to build. A problem that has sat on the roadmap for years. Let’s talk about what it would take to solve it.
Talk to Zaigo