Engagement 10 · AI Foundation
A production-ready foundation for Azure OpenAI and AI Foundry workloads — so AI runs inside your platform, not around it. Private networking, identity, content safety, logging, and cost guardrails, all built right.
Why this exists
Teams stand up Azure OpenAI to ship a chatbot, then leave it on the public network with default keys, no logging, no cost guardrails, and no content safety policy. Six months later, when AI is mission-critical, they realise they've built it on sand. The Landing Zone gives you the foundation first: private endpoints, managed identity, observability, content safety, and the cost controls you'll wish you'd set up before someone forgot to disable a long-running agent.
What's included
Azure OpenAI and AI Foundry behind private endpoints. Egress controlled, public access disabled, DNS configured for private resolution. No surprise public exposure.
Managed identity for all integrations — no keys in app config. RBAC for who can deploy models, who can call them, and who can change content safety policies.
Azure AI Content Safety configured with the right thresholds, custom block lists, and prompt shielding for input and output. Logged and reviewable.
Token usage, latency, errors, and content safety triggers shipped to Log Analytics. Starter dashboards and alerts for cost and abuse.
Per-model quota allocation, budget alerts, and the controls to throttle a runaway integration before it costs five figures by Tuesday.
Everything deployed via Terraform. Reproducible, auditable, and easy to extend as you add models, regions, and projects.
Deliverables
Timeline
Workload requirements, model access, region choices, integration patterns, and compliance considerations.
Terraform deployment, content safety configuration, observability and quota guardrails — built and tested.
Reference integration deployed, walkthrough with your team, playbook agreed.
FAQ
Do we need this if we're not building AI products yet?
If AI is on the roadmap and you want to be ready, yes. If you're a year out and the priority is the rest of the platform, the Azure Launchpad covers general foundations and you can come back here when AI is real.
Does this include building the AI app itself?
No — that's AI System Build. This engagement gets the foundation right; AI System Build delivers the application layer (RAG, agents, integrations) on top.
What about AI Foundry vs Azure OpenAI?
AI Foundry sits on top of Azure OpenAI for orchestration and evaluation. We configure both as part of the foundation — your application teams choose which surface to use.
How does cost scale?
Token usage drives most of the cost. The guardrails (quota allocation, budgets, throttle controls) keep you in known limits while you experiment, and the dashboards make it visible. Compute and storage are minor by comparison.
Do you cover regulated industries / data residency?
Yes — region selection, data residency, abuse monitoring opt-out, and customer-managed keys all get covered when relevant. We'll flag what's possible and what isn't on the discovery call.
Next step
Book a 30-minute discovery call. We'll talk through your AI plans, current Azure footprint, and any compliance constraints before agreeing scope.
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