Microsoft AI Foundry & Azure OpenAI
Set up Microsoft AI Foundry and Azure OpenAI for production with the right model deployments, access controls, monitoring, and cost guardrails.
The problem
Many teams get to an AI demo quickly. The harder part starts after that. Someone still needs to decide where Foundry projects live, how Azure OpenAI is exposed to applications, which models are approved, how traffic is isolated, who can deploy, how content filtering is tuned, and how usage is monitored.
Without that foundation, every new use case turns into a separate setup. Delivery slows down. Governance stays unclear. Production readiness keeps moving to the next quarter.
What we build
We deliver a concrete Microsoft AI Foundry and Azure OpenAI setup service for teams that need a working platform, not another pilot. The outcome is an Azure-based foundation your product and engineering teams can start using for copilots, chat experiences, RAG solutions, agents, and internal AI services.
From platform setup to production-ready use
The service establishes the Azure foundation first, then adds the controls and operating model needed for real workloads.
A typical engagement includes:
- Foundry resource, project, and environment setup aligned with your Azure structure
- Azure OpenAI deployment strategy, region choices, and quota or PTU sizing
- Network and access design with private endpoints, managed identities, and RBAC
- Guardrails with Content Safety, API policies, and approved model and prompt patterns
- Monitoring and cost visibility for tokens, latency, failures, and filter events
- Infrastructure-as-code, deployment workflow, and runbooks for your team
We can stop at platform setup, or stay through the first production use case so the platform is proven under real traffic.
How we work
We start with the use cases you actually plan to ship, the data boundaries you need to respect, and the operating model your team can own. From there, we design the Foundry and Azure OpenAI setup that fits your landing zone, security model, and delivery process.
Then we build the platform in short iterations. We stand up the resources, lock down access, deploy the first approved models, wire diagnostics to Azure Monitor, and validate the path from development to production. Knowledge transfer is part of the delivery. By the end, your team has a platform, a working reference implementation, and clear operating practices.
Key Technologies
- Microsoft AI Foundry resources and projects
- Azure OpenAI Service deployments
- Microsoft Entra ID and Azure RBAC
- Private endpoints and private DNS
- Azure AI Content Safety
- Azure API Management
- Azure Monitor and Log Analytics
- Bicep / Terraform and GitHub Actions or Azure DevOps
Delivery Foundations
- Model approval, deployment, and versioning rules
- Region, quota, and PTU sizing for production traffic
- Managed identity-first authentication
- Network isolation and secret handling
- Diagnostic settings, alerts, and usage dashboards
- Retry, fallback, and 429 handling patterns
- Runbooks for access changes, rollout, rollback, and incident response