Deploy Anthropic’s Claude Models in Azure AI Foundry

The Problem: You want to use Anthropic’s Claude models for coding, agents, or enterprise AI—but you’re already invested in the Azure ecosystem and don’t want another vendor contract.

The Nugget: Claude models are now available in Microsoft Foundry via serverless deployment. You can deploy Claude Sonnet 4.5, Haiku 4.5, Opus 4.5, or Opus 4.5 directly from the Foundry model catalog—no separate Anthropic account needed.

Quick Setup:

• Open Azure Portal → Search for “AI Foundry”

• Create or select a Foundry project (supported regions: East US2 or Sweden Central)

• Go to Model catalog → Search for “Claude”

• Select your model (e.g., claude-sonnet-4-5) → Click Deploy

• Choose Global Standard deployment → Confirm

Which Model to Pick:

ModelBest ForSonnet 4.5Coding, complex agents, production workflowsHaiku 4.5High-volume, cost-sensitive, fast responsesOpus 4.5Most intelligent — enterprise workflows, computer useOpus 4.1Long-running tasks, sustained reasoning

Call Your Deployment (Python):

from anthropic import AnthropicFoundry

client = AnthropicFoundry(
    api_key="YOUR_API_KEY",
    base_url="https://<resource-name>.services.ai.azure.com/anthropic"
)

message = client.messages.create(
    model="claude-sonnet-4-5",
    messages=[{"role": "user", "content": "Explain Azure Arc in one sentence."}],
    max_tokens=1024
)
print(message.content)

Why This Matters:

• MACC eligible — counts against your Azure commitment

• Same billing — no separate Anthropic invoices

• Enterprise ready — governance, observability, Entra ID auth built-in

• Multi-model flexibility — use Claude alongside GPT models in one platform

Pro Tip: Use the Foundry Playground to test prompts before writing code. It’s perfect for experimenting with different Claude models side-by-side.

Admin context

For Microsoft admins, the practical point in Deploy Anthropic’s Claude Models in Azure AI Foundry is to treat the change as something that should be validated before it becomes tenant-wide behavior. Check the affected users, devices, assignments and support process so the Nugget turns into a controlled operational improvement instead of another undocumented setting.

Runbook notes for Deploy Anthropic’s Claude Models in Azure AI Foundry

Deploy Anthropic’s Claude Models in Azure AI Foundry deserves a little more operational context because the decision usually affects model access planning. The related items are Claude models, Azure AI Foundry, quota, deployment controls, enterprise AI governance. Treat this Nugget as a starting point for a concrete tenant decision: who is in scope, which Microsoft portal or policy is touched, and what visible result should confirm that the configuration worked.

When validating Deploy Anthropic’s Claude Models in Azure AI Foundry, keep the test narrow enough to understand the result. Select one representative user, device, workload or subscription, capture the current state, then apply the change and compare the outcome. This avoids guessing later when support sees a different enrollment state, access result, model response, update status or admin center signal.

The most useful documentation for Deploy Anthropic’s Claude Models in Azure AI Foundry is practical rather than theoretical. Record the assignment logic, the owner, the expected monitoring view and the exception path. If the change affects users, include the wording support teams should use when they explain the behavior. If it affects devices or services, include the exact place where administrators can verify health.

For search consistency, keep the phrase Deploy Anthropic’s Claude Models in Azure AI Foundry connected to the body text, the internal links and the category context. That helps readers understand why this Microsoft admin topic belongs with the surrounding Intune, Entra, Azure, Copilot, Security or automation Nuggets, and it gives AI search systems clearer signals about the real subject of the page.

Revisit Deploy Anthropic’s Claude Models in Azure AI Foundry after the next rollout wave or Microsoft service update. Cloud behavior, licensing boundaries and portal labels can move quickly, so a short review prevents stale instructions. Confirm that the original assumption is still true, remove obsolete exceptions, and update the runbook if the operating model changed.

A clean handover for Deploy Anthropic’s Claude Models in Azure AI Foundry should also include a fallback. Write down how the team pauses the change, narrows the scope, or returns to the previous configuration if the result creates noise. This makes the Nugget safer to use in production because the implementation path includes both the happy path and the recovery path.