For IT and platform teams about to build custom AI apps and agents on Azure, or already running them in production.
We design and build the Azure foundation custom AI runs on: landing zones, private networking, identity, and a Microsoft Foundry structure. Then we set up the routine that keeps it working in production: release gates, monitoring, model retirement tracking, cost alerts, and tested recovery. We run it with you, or for you under Managed Azure and Microsoft 365.
The Azure landing zone for AI is where workloads deploy, and the Microsoft Foundry platform on top of it is where teams build. The operating model, release and lifecycle, and cost and continuity routines keep each model and agent working once it is live. You can take any of them, or only what the Assess phase finds missing.
Azure landing zone for AI
A place for AI workloads to land that already carries the right policies, private connectivity, and the region and capacity decisions made up front.
What you get
Management groups separating internet-facing and internal AI
AI-specific Azure Policy for Foundry, AI Search, and virtual machines
Private endpoints, Bastion, and DDoS Protection for internet-facing AI
ExpressRoute or VPN Gateway sized to the data AI needs
Quota, capacity, and region checks for the models you plan to use
A second region where the risk justifies it
Best for
Experiments already landing in personal subscriptions, and the data AI needs still on-premises.
Microsoft Foundry platform
A Foundry structure ready for teams to build in, with identity, secrets, evaluation, and cost controls already in place.
What you get
Foundry resource and project hierarchy
Managed identities and Key Vault for secrets
Evaluation, tracing, and observability from the start
Cost controls and an approved model catalog
Best for
Each AI project is about to set up its own identity, secrets, and monitoring from scratch.
AI operating model
Clear ownership for deciding, building, and running AI, with shared standards instead of each team starting over.
What you get
An AI center of excellence for standards and guidance
GenAIOps for generative AI, MLOps for machine learning
One set of SDKs and tools shared across teams
A sandbox separate from development, test, and production
Best for
Three teams build AI three ways, and nobody owns the standards.
Release and lifecycle
Teams that ship inside guardrails with one approval before production, and a view of how each model and agent performs and when it must change.
What you get
Workload teams deploy within Azure Policy boundaries
Release pipelines with quality checks and one manual approval
Performance baselines, including responsible AI measures
Versioning, promotion gates, and rollback
Model retirement dates tracked in Microsoft Foundry
Agent inventory kept current, to the rules set in AI Governance
Best for
Releases that wait on a central queue, or go straight to production, and a model that will retire before anyone notices.
Do we need a landing zone before we build AI on Azure?
Yes, though not a new one if you already have landing zones. AI workloads then go into application landing zones like any other workload. If you don’t have them, we set up a baseline hierarchy first, so AI resources inherit identity, network, and policy controls instead of each project inventing its own.
Which Azure region should our AI run in?
The one where the services, features, and quota you need are available, close to the data the AI retrieves, and allowed by your residency rules. Production workloads that justify it get a second region for failover.
Copilot Studio or Microsoft Foundry?
Copilot Studio when low-code agents, Power Platform connectors, and managed channels are enough. Microsoft Foundry when you need more control over models, retrieval, evaluation, networking, or the user experience. We use the simplest path that meets the requirement.
Can our AI apps use data that’s still on-premises?
Yes. We size the connection to the data: Azure ExpressRoute for high volumes and steady performance, VPN Gateway for moderate or occasional transfer, with private endpoints so the traffic stays off the public internet.
What is GenAIOps?
GenAIOps is the operating discipline for generative AI: versioned prompts, models, and data, evaluation before release, monitoring for quality, safety, and cost, and controlled promotion between environments. MLOps is the equivalent for traditional machine learning.
What happens when a model we use is retired?
We move you to a tested replacement before the retirement date. Pretrained models have retirement dates, and missing one means degraded or broken service. We track the dates for your deployments in Microsoft Foundry, test the replacement against your golden dataset, and promote it through the same gates as any other release.
How do we keep AI costs predictable?
Track tokens and requests per minute, and set budgets with alerts at several thresholds in Microsoft Cost Management. Then adjust models and architecture to the usage pattern, and move to commitment-based billing once usage is steady.
How long does it take?
It depends on what the Assess phase finds missing and on your first workload. The foundation is sized from what Assess finds: landing zones, private networking, identity, or a Foundry structure. The operating model is built and proven on the first workload, then extended. The proposal sets the length of each phase.
What changes the size of an AI platform and operations setup?
Mostly whether landing zones exist, how many AI workloads, models, and agents are coming or already in production, and whether any of them face the internet. Where the data lives, whether release pipelines and monitoring already exist, your recovery objectives, and who runs the platform afterwards decide the rest.
It starts with a 60-minute call. If we need more to scope it, a short questionnaire follows. Then a written proposal and statement of work. Once you approve, we begin with the Assess phase.
