Azure AI Foundry Development
Build production AI on Microsoft's unified platform
Azure AI Foundry gives you access to the best models from OpenAI, Anthropic, Meta, Mistral, and Microsoft, all within your Azure tenancy. We help you select, evaluate, deploy, and integrate them into real enterprise applications.
AI Foundry capabilities
From model selection and prompt engineering through to production deployment, evaluation, and ongoing optimisation.
Model catalogue and deployment
Access and deploy models from the Azure AI Foundry catalogue: OpenAI, Anthropic (Claude Haiku for low-latency tasks up to Sonnet and Opus for complex reasoning), Microsoft Phi, Meta Llama, Mistral, and more. Choose the right model for each task based on capability, cost, and latency, not vendor default.
Prompt flow and evaluation
Design, test, and iterate on prompt architectures using Azure AI Foundry prompt flow. We build evaluation pipelines that measure grounding accuracy, relevance, and safety before anything reaches production.
Model fine-tuning
When prompt engineering is not enough, we fine-tune models on your domain data within Azure AI Foundry. The fine-tuned model stays in your tenancy with full data sovereignty.
Responsible AI dashboard
Every model deployment includes responsible AI evaluation: fairness, content safety, grounding quality, and bias detection. Azure AI Foundry provides the tooling; we configure and interpret it for your use case.
RAG and retrieval integration
We connect Foundry-hosted models to Azure AI Search for retrieval-augmented generation. Your AI answers questions grounded in your own documents, databases, and knowledge bases, not general model training data.
Multi-model architectures
Not every task needs the most capable model. We design architectures that route requests based on complexity: Phi or Claude Haiku for fast, low-latency tasks, Claude Sonnet or Opus where deep reasoning matters, and deterministic logic where AI is not needed at all.
What Azure AI Foundry provides
The unified Azure platform for your entire AI lifecycle. We configure and operate it so you get value from it, not just access to it.
Azure AI Foundry portal
The unified interface for model management, prompt engineering, evaluation, and deployment. One place to manage your entire AI lifecycle.
Model-as-a-Service (MaaS)
Deploy models without managing infrastructure. Pay per token for models you use, scale automatically, and switch models without redeployment.
Managed compute
For models that need dedicated infrastructure (fine-tuned models, high-throughput workloads), Azure AI Foundry provides managed GPU compute within your tenancy.
Prompt flow
Visual and code-based prompt engineering with built-in evaluation. Test prompts against datasets, compare model outputs, and version your prompt architecture.
Content safety
Built-in content filtering, jailbreak detection, and grounding evaluation. Configure safety thresholds per deployment to match your risk profile.
Enterprise security
Private endpoints, managed identity, RBAC, and Azure Policy. Models and data stay within your Azure tenancy and compliance boundary.
How Foundry fits into your solution
Azure AI Foundry is not a standalone product. It is the AI layer that plugs into your existing systems or powers new ones. Here is how we use it.
Add AI to an existing application
The most common pattern. Your web app, mobile app, or internal system stays as it is. We deploy models in Foundry, build the integration layer (APIs, RAG pipeline, MCP connectors), and your application calls the AI service when it needs intelligence. No rebuild required.
Best for: You have a working application and want to add smart search, document processing, summarisation, or recommendations.
Build a new AI-first application
When the AI capability is the product, we build the entire solution on Azure with Foundry at the core. The application architecture is designed around AI workflows from the start: prompt pipelines, retrieval layers, feedback loops, and human-in-the-loop controls.
Best for: You are building a new product where AI is the primary value (e.g. an internal knowledge assistant, an AI-powered assessment tool, an automated analysis platform).
Replace a component with AI
Some existing features work but are expensive to maintain or limited in capability: manual classification, rules-based search, template-driven content. We replace the component with a Foundry-hosted model and keep the rest of the system unchanged.
Best for: A specific feature in your system would benefit from AI but you do not want to rebuild the whole application.
Extend Microsoft 365 and Copilot
Foundry-hosted models power custom Copilot Studio solutions, Microsoft 365 Copilot extensions, and Teams integrations. Your users interact through familiar Microsoft interfaces while Foundry handles the AI backend with your choice of model.
Best for: Your workforce already uses Microsoft 365 and you want AI grounded in your enterprise data within the tools they use daily.
From assessment to production AI
A structured approach that validates model fit and cost before committing to a full build.
AI Opportunity Assessment
We analyse your use case, data landscape, and requirements to determine which Azure AI Foundry capabilities apply. You receive a recommendation covering model selection, deployment approach (MaaS vs managed compute), and estimated costs.
Model selection and proof of concept
We deploy candidate models in Foundry, build evaluation pipelines, and test against your real data. You see measurable results (accuracy, latency, cost per request) before committing to a full build.
Production build and integration
We build the production AI solution: prompt flow, RAG pipelines, API integration, monitoring, and responsible AI controls. Everything integrates with your existing applications via APIs. AI-augmented development tools accelerate our delivery.
Launch, monitor, and evolve
We deploy with comprehensive monitoring: token usage, response quality, latency, and cost dashboards. As new models ship to the Foundry catalogue, we evaluate and migrate where it makes sense. Managed support keeps your AI running reliably.
Go deeper
AI Development & Implementation
Our full AI service: generative AI, agents, Copilot extensions, and RAG architectures.
Copilot Studio Rescue
Rescue stuck Copilot Studio deployments with Foundry model integration.
AI Integration
Custom MCPs, RAG pipelines, and blending AI with deterministic logic.
Custom Generative AI
Deep-dive on Azure OpenAI, Copilot customisation, and RAG patterns.
RAG AI Search Accelerator
Our technical guide to building RAG on Azure AI Search.
Frequently asked questions
What is Azure AI Foundry?
Azure AI Foundry (formerly Azure AI Studio) is Microsoft's unified platform for building, evaluating, and deploying AI applications. It provides access to a catalogue of models (OpenAI, Meta, Mistral, Microsoft), prompt engineering tools, evaluation pipelines, and responsible AI dashboards, all within your Azure tenancy.
What is the difference between Azure AI Foundry and Azure OpenAI Service?
Azure OpenAI Service gives you access to OpenAI models within Azure. Azure AI Foundry is the broader platform that includes Azure OpenAI alongside models from Anthropic, Meta, Mistral, Microsoft, and others. Foundry also adds prompt flow, model evaluation, fine-tuning, and responsible AI tooling. Think of Azure OpenAI as one model provider within the Foundry ecosystem.
How much does Azure AI Foundry cost?
Azure AI Foundry itself is free to use. You pay for the models and compute you consume: Model-as-a-Service pricing is per token (varying by model), and managed compute is billed by GPU-hour. We design architectures that optimise cost by routing requests to the most cost-effective model for each task. See our pricing guide for project cost ranges.
Can we use models other than OpenAI?
Yes. Azure AI Foundry provides access to models from Meta (Llama), Mistral, Microsoft (Phi), Cohere, and others, alongside OpenAI models. We help you choose the right model for each task based on capability, cost, latency, and licensing requirements.
Is fine-tuning worth it?
For most enterprise use cases, prompt engineering with RAG is sufficient and more cost-effective. Fine-tuning makes sense when you have a high-volume, specialised task where prompt engineering hits accuracy or latency limits, and you have quality training data. We evaluate both approaches during the proof of concept and recommend the one that delivers better results for your budget.
Does our data stay secure?
Yes. All models deployed through Azure AI Foundry run within your Azure tenancy. Your data is never used to train public models, never leaves your chosen region, and is subject to the same Azure security controls (RBAC, private endpoints, encryption) as any other Azure resource. Our ISO 27001 certification and Cyber Essentials Plus accreditation apply to all delivery.
Can we add Foundry models to our existing application?
Yes, and this is the most common pattern. Your existing web app, mobile app, or internal system stays as it is. We deploy models in Foundry, build an API integration layer (often with a RAG pipeline behind it), and your application calls the AI service when it needs intelligence. No rebuild is required. We can also connect Foundry models to Copilot Studio, Microsoft 365, and Teams so your users interact through tools they already use.
Ready to build on Azure AI Foundry?
Start with a free AI Opportunity Assessment. We identify the right models, the right architecture, and the real cost before you commit.
Book a free consultationor call 01202 375647