Azure for AI: An SRE's Guide to Provisioning for High Availability

When teams ask whether Azure is "ready" for serious AI workloads, my answer as an SRE is usually: yes—but only if you treat it like critical infrastructure, not a demo platform.

I'm writing this as someone with deep experience operating high-traffic cloud systems, now specializing in Azure's AI and MLOps ecosystem. This is not a tutorial. It's an evaluation of what actually holds up under load, failure, and budget scrutiny—as of late 2025.

If your goal is to run LLM-backed systems with uptime expectations comparable to Tier-1 services, Azure can do it. But the path runs through quotas, networking, and observability—not prompts and SDKs.

Development vs Production: Draw the Line Early

Before diving into components, one hard rule:

Azure AI Studio / Foundry is a development and control plane. High availability lives elsewhere.

Studio makes it easy to experiment. Production reliability depends on:

• VM quotas and regional capacity
• Network topology (Private Link, VNet injection)
• Multi-region deployment patterns
• Infrastructure-as-Code (IaC)
• Observability beyond "model accuracy"

Conflating these layers is the fastest way to ship something fragile.

Compute & Silicon: Why "Standard" VMs Don't Survive AI Load

ND-Series and Why It Matters

For serious AI workloads—training or high-throughput inference—ND-series GPUs are not optional. Azure's ND families (H100 today, Blackwell-class accelerators emerging) exist for one reason: predictable performance at scale.

From an SRE standpoint, the key advantages are:

• Dedicated GPUs with predictable scheduling
• High memory bandwidth, which dominates LLM latency
• InfiniBand networking, which fundamentally changes distributed workloads

Trying to run LLMs on general-purpose VM SKUs usually fails quietly:

• Tail latency explodes under concurrency
• CPU bottlenecks mask GPU underutilization
• Autoscaling reacts too late to be useful

InfiniBand Is Not a Luxury

InfiniBand isn't about peak performance benchmarks. It's about variance control.

For distributed inference or training:

• Cross-node synchronization becomes deterministic
• Tail latency narrows
• GPU utilization becomes predictable

From an SRE lens, InfiniBand reduces unknown unknowns. That alone justifies the cost.

Azure Cobalt CPUs: The Supporting Cast

Azure's Cobalt CPUs matter less for raw AI compute and more for:

• Control plane services
• Pre/post-processing
• Vector search
• Inference gateways

They offer better performance-per-watt and tighter integration with Azure's SDN, which shows up as lower jitter and more stable networking under load.

Azure AI Foundry: The Control Plane, Not the Runtime

Azure AI Foundry (the evolution of AI Studio) should be viewed as a control plane for MLOps, not the place where reliability is enforced.

Prompt Flow for CI/CD (Not Experimentation)

Prompt Flow's real value is not visual authoring—it's versioned, testable prompt pipelines.

Used correctly, it enables:

• Prompt versioning tied to releases
• Canary deployments of prompt logic
• Regression testing against fixed evaluation sets

From an SRE perspective, this turns prompts from "tribal knowledge" into deployable artifacts.

Registry: Treat Models Like Binaries

Foundry's Registry becomes useful when you treat models the same way you treat container images:

• Immutable versions
• Explicit promotion between environments
• Rollback as a first-class operation

This matters operationally. When something degrades, you want to answer: what changed—and "the model" should never be a mystery.

Observability: Azure Monitor + Managed Grafana (or You're Flying Blind)

Most AI failures don't show up as crashes. They show up as variance.

Azure's observability stack works—but only if you wire it correctly.

What to Measure (That Actually Predicts Failure)

From an SRE standpoint, these signals matter more than accuracy charts:

Time to First Token (TTFT) User-perceived responsiveness; sensitive to queuing and cold starts.

Total Inference Time Capacity and cost driver; correlates with GPU saturation.

GPU Health Metrics Memory pressure, throttling, ECC errors—these fail silently until they don't.

Queue Depth & Wait Time The earliest indicator of overload.

Azure Monitor can collect these; Managed Grafana makes them usable. The mistake I see is stopping at platform metrics and ignoring application-level instrumentation.

Integrating Foundry Signals

Foundry provides metadata—model versions, deployments, prompt flows. Correlating that with latency and cost metrics is where observability becomes actionable.

If latency regresses after a prompt or model change, you should see it immediately—not in a weekly review.

Data & Vector Storage: Treat Retrieval as Tier-0

Retrieval-augmented systems fail when their data layer degrades. This is not theoretical.

Azure AI Search

Strengths:

• Fully managed
• Tight Azure integration
• Fast to get started

SRE Concerns:

• Index rebuild costs
• Scaling limits under write-heavy workloads
• Less control over memory behavior

Good for moderate-scale RAG. Less forgiving at high throughput.

Azure Database for PostgreSQL (Hyperscale/Citus)

When teams use PostgreSQL-based approaches (often with extensions or external vector layers), they gain:

• Explicit control over indexing
• Predictable memory usage
• Clear scaling boundaries

The trade-off is operational responsibility—but for Tier-1 systems, that's often acceptable.

Zero-Copy with Fabric (Where It Helps)

Fabric-style zero-copy integration reduces data duplication and pipeline latency, but from an SRE view:

• It simplifies data movement
• It does not eliminate runtime dependencies
• It still requires capacity planning

Zero-copy reduces failure surfaces—it doesn't remove them.

Networking: Private Links, or Don't Bother

If your AI system is production-critical:

• Private Endpoints are mandatory
• Public networking is a risk multiplier
• SDN isolation is part of your threat model and reliability story

Azure's Software Defined Networking is one of its strongest assets. Used correctly, it provides:

• Predictable latency
• Blast-radius containment
• Cleaner multi-region failover patterns

Ignoring this layer is how "secure demos" become operational liabilities.

Quotas: The Real Availability Constraint

Azure outages are rarely what take AI systems down. Quota exhaustion does.

GPU quotas, regional capacity, and SKU availability must be treated as:

• Design inputs
• Not afterthoughts

High availability requires:

• Pre-approved quotas
• Multi-region capacity planning
• IaC that can fail over without human intervention

If your runbook includes "open a support ticket," you don't have HA.

Agentic Operations: Early, Promising, Not Autonomous (Yet)

Azure's emerging SRE and Copilot agents are interesting—but they are assistive, not autonomous.

Where they help today:

• Triage assistance
• Suggested remediation
• Faster diagnostics

Where they don't replace humans:

• Capacity planning
• Architectural decisions
• Incident command

As an SRE, I see them as force multipliers—not replacements. Used well, they reduce cognitive load during incidents. Used blindly, they add noise.

Closing Thought

Azure's biggest strength for AI is not its models.

It's the Software Defined Networking, global footprint, and enterprise-grade control plane that let LLM-based systems behave like traditional Tier-1 services.

When provisioned with:

• The right silicon
• Proper networking
• Explicit quotas
• Real observability

LLMs stop feeling "experimental" and start behaving like infrastructure.

And from an SRE's perspective, that's the real milestone: not intelligence—but predictability at scale.