BlogAzure AI

Claude Opus 5 Is Now Available in Microsoft Foundry

Anthropic's Claude Opus 5 — the first Opus model in the Claude 5 generation — is now available directly in Microsoft Foundry.

Author

Artan Ajredini

Artan Ajredini

Founder/CEO AI Architect

3 min read
26 July 2026

Claude Opus 5 Arrives in Microsoft Foundry

Claude Opus 5 is now available in Microsoft Foundry

Anthropic's Claude Opus 5 — the first Opus model in the Claude 5 generation — is now available directly in Microsoft Foundry. It joins Claude Sonnet and Haiku as part of the growing Claude model family on Foundry, giving Azure customers a frontier-grade option for the reasoning-heavy, long-running work that lighter models struggle to finish reliably.

Combined with Foundry's enterprise platform — evaluation tools, native security controls, governance, and scalable deployment — teams can build and operate the next generation of AI applications without leaving the Azure ecosystem they already manage identity, networking, and compliance through.

“Claude Opus 5 is designed to work for hours, find paths around obstacles, and navigate codebases like a senior engineer. In Foundry, that reasoning power comes wrapped in the enterprise controls production workloads actually require.”

Built for Complex Workflows

Claude Opus 5 is built for complex workflows

Most models are optimised for short, single-turn exchanges. Opus 5 is built for the opposite: multi-step work that unfolds over minutes or hours, where the model has to keep track of what it has already tried, what failed, and what to do next.

  • Advanced planning and reasoning — Opus 5 breaks a large task into an ordered sequence of steps before acting, rather than reacting one prompt at a time.
  • Long-running tasks — it sustains context and objective across extended sessions, instead of losing the thread after a few exchanges.
  • Adaptive execution — when a planned step fails or an obstacle appears, it revises the plan and keeps going rather than stalling out.

Agentic AI, Natively

Claude Opus 5 agentic AI capabilities

The headline capability behind Opus 5 is agentic reliability — recovering from errors, coordinating multiple tools, and reaching an objective without a human re-prompting it at every turn.

  • Reason across multiple steps — Opus 5 holds a chain of intermediate decisions in context rather than treating each step in isolation.
  • Use tools and functions — it calls external tools, APIs, and functions as part of executing a plan, not just as a one-off action.
  • Coordinate workflows — it can orchestrate work across multiple tools and subagents to complete a larger objective.
  • Adapt as tasks evolve — when requirements shift mid-task, it updates its plan instead of continuing down a now-invalid path.

Deploying Claude Opus 5 in Foundry

Enterprise AI in Microsoft Foundry

Claude Opus 5 deploys through the Foundry portal like any other model in the catalog: open Discover → Models, select Claude Opus 5, choose Deploy, accept the Azure Marketplace terms, and pick a region scope (Global or Data Zone, where available). You need Contributor or Owner on the resource group, and a paid Azure subscription with a supported billing region.

Calling the model

Once deployed, you call Opus 5 through the Claude Messages API using the Anthropic SDK, authenticating with either Microsoft Entra ID (keyless, via DefaultAzureCredential) or a deployment API key. The base URL follows the pattern https://<resource-name>.services.ai.azure.com/anthropic.

python
from anthropic import AnthropicFoundry
from azure.identity import DefaultAzureCredential, get_bearer_token_provider

baseURL = "https://<resource-name>.services.ai.azure.com/anthropic"
deploymentName = "claude-opus-5"  # the name you chose during deployment

# Keyless auth via Microsoft Entra ID
tokenProvider = get_bearer_token_provider(
    DefaultAzureCredential(), "https://ai.azure.com/.default"
)

client = AnthropicFoundry(
    azure_ad_token_provider=tokenProvider,
    base_url=baseURL
)

message = client.messages.create(
    model=deploymentName,
    messages=[
        { "role": "user", "content": "Plan and execute a 3-step migration checklist for moving a service from VMs to AKS." }
    ],
    max_tokens=2048,
    temperature=1,
    thinking={"type": "adaptive"},
    stream=False
)

print(message.content)

Prefer API key authentication for quick prototyping — swap the `azure_ad_token_provider` for `api_key=<your-key>`. In production, Entra ID is the safer default: no long-lived secret to rotate or leak, and access is governed through the same RBAC roles you already use for other Azure resources.

Want help integrating Claude Opus 5 into your Azure stack?

We help teams deploy and govern Claude models in Microsoft Foundry — from first deployment to production-grade agentic workflows.

Schedule a call

What This Means for Developers

What Claude Opus 5 in Microsoft Foundry means for developers
  • More choice in Foundry — access Claude Opus 5 alongside other leading models in the same catalog, without standing up a separate vendor integration.
  • Stronger agentic workloads — handle complex, multi-step tasks with greater reliability than lighter models allow.
  • Higher performance — advanced reasoning and long-running execution for the demanding use cases that used to require a human in the loop at every step.
  • Seamless ecosystem — native integration with the Microsoft tools, data, and governance your organisation already runs on.

For teams already standardised on Azure and Foundry, the practical effect is simple: the same identity, networking, monitoring, and compliance controls now extend to Anthropic's most capable model, with no new vendor relationship to manage.

Closing Thoughts

Claude Opus 5 in Microsoft Foundry is aimed squarely at the workloads that make or break an agentic AI strategy: long-running, multi-step tasks where the model has to plan, adapt, and recover without constant supervision. Combined with Foundry's governance and deployment tooling, it gives Azure-native teams a frontier reasoning model without leaving their existing security and compliance boundary.

If you are already running workloads in Foundry, deploying Opus 5 for a pilot agentic use case — a migration assistant, a multi-step data pipeline, a codebase-navigation agent — is a low-friction way to see whether the extra reasoning depth is worth the move up from lighter models.

More articles

View all
Building RAG Pipelines with Azure AI Search and GPT-4o
over 1 year ago1 min read

Building RAG Pipelines with Azure AI Search and GPT-4o

Retrieval-Augmented Generation (RAG) is the architecture that turns a general-purpose language model into a domain expert grounded in your own data. Instead of fine-tuning — which is expensive and produces models that go stale — RAG retrieves the most relevant documents at query time and passes them as context to the model. In this article, we build a complete RAG pipeline on Azure: documents are uploaded to Azure Blob Storage, indexed by Azure AI Search using vector embeddings from Azure OpenAI, and retrieved at query time using hybrid search (vector + keyword). The retrieved chunks are then assembled into a prompt sent to GPT-4o, which generates a grounded answer with source citations. We cover chunking strategies, embedding model selection, index schema design, semantic ranking, and how to evaluate retrieval quality. Full code examples in Python using the Azure SDK are included.

Read article
CI/CD Pipelines with Azure DevOps and GitHub Actions
over 1 year ago1 min read

CI/CD Pipelines with Azure DevOps and GitHub Actions

A well-designed CI/CD pipeline is the backbone of a high-performing engineering team. In this article, we compare Azure DevOps Pipelines and GitHub Actions and explain how to combine both tools to get the best of each ecosystem. We build a complete pipeline from scratch: code commit triggers a GitHub Actions workflow that runs unit tests and builds a Docker image, pushes it to Azure Container Registry, and then hands off to an Azure DevOps release pipeline for staged deployment to AKS — with approval gates between environments. We also cover secrets management with Azure Key Vault, environment-specific configuration using variable groups, and how to set up rollback strategies using deployment slots and blue-green releases. Practical YAML examples are included throughout.

Read article
Azure Security Fundamentals: Zero Trust for Cloud Workloads
over 1 year ago1 min read

Azure Security Fundamentals: Zero Trust for Cloud Workloads

The traditional perimeter-based security model does not work in the cloud. With resources spread across regions, teams accessing systems from anywhere, and workloads communicating over public networks, Zero Trust — never trust, always verify — is the only viable approach. In this article, we implement Zero Trust across an Azure workload step by step: enabling Microsoft Defender for Cloud and addressing its security score recommendations, configuring Entra ID Conditional Access policies to require MFA and compliant devices, applying Just-in-Time VM access to eliminate persistent inbound ports, using Private Endpoints to remove public exposure from storage accounts and databases, and setting up Microsoft Sentinel for centralised security monitoring and automated incident response. This article is a practical starting point for any team that wants to harden their Azure environment against modern threats.

Read article