RAG vs Fine-Tuning: Choosing the Right LLM Approach for Your Product
Both RAG and fine-tuning improve LLM performance on your specific use case — but they solve different problems. Here's how to choose.
Cloud
Microsoft Azure
Azure AI services and enterprise Microsoft ecosystem integration
60+Global regions
1000+Compliance certifications
99.99%SLA for enterprise
M365Native integration
HOW WE USE IT
Microsoft Azure in our stack
We deploy on Azure for enterprise clients already in the Microsoft ecosystem. Azure OpenAI Service, Cognitive Services, Azure ML, and Teams integration make Azure the natural choice for enterprises running on M365, Dynamics, or SharePoint.
CAPABILITIES
What we deliver
- Azure OpenAI Service (private GPT-4 deployment)
- Azure Cognitive Services (Vision, Speech, Language)
- Azure Machine Learning Studio
- AKS (Azure Kubernetes Service) deployments
- Azure DevOps CI/CD pipelines
- Power Platform and Microsoft 365 integrations
USE CASES
How we apply Azure
Enterprise AI on Azure OpenAI
Private GPT-4 deployment on Azure with data residency guarantees and Active Directory authentication.
M365 AI Integration
AI features embedded in SharePoint, Teams, and Outlook via Microsoft Graph API and Azure Functions.
AKS ML Platform
Kubernetes-based ML platform on Azure with MLflow, model registry, and automated retraining pipelines.
Engineering Stack
Built with the tools that matter
38 production-grade technologies — every one battle-tested in shipped products.
Frequently Asked Questions
Didn't find what you were searching for? Reach out to us at [email protected] and we'll assist you promptly.
Azure has the strongest enterprise integration story: Azure OpenAI Service provides GPT-4 with enterprise security, data residency, and compliance guarantees not available on OpenAI directly; Azure AD integrates with existing enterprise identity; and Azure ML aligns with Microsoft-stack organizations already using Office 365, Dynamics, and Power BI. We recommend Azure for enterprises with existing Microsoft infrastructure, for regulated industries needing Azure-specific compliance certifications, or for teams building on Azure OpenAI.
A production Azure AI architecture typically includes: Azure OpenAI or Azure ML for model serving, Azure Blob Storage and Data Lake for data, Azure Service Bus for async processing, Azure Monitor and Application Insights for observability, Azure Key Vault for secrets, and Azure AD for identity. We use Bicep or Terraform for infrastructure-as-code and Azure DevOps pipelines for CI/CD with MLOps integration.
Azure OpenAI integrations with enterprise security and compliance controls typically take 4-8 weeks including networking, authentication, and governance setup. Full Azure ML MLOps infrastructure projects run 6-10 weeks. For organizations migrating from another cloud to Azure, add 2-4 weeks for migration planning and data transfer.
FROM OUR CLIENTS
Built with teams who ship
The team took our AI concept from whiteboard to production in 10 weeks. The architecture they designed handles 10x our expected load with no issues.
Insights
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SERVICES THAT USE AZURE
Related service offerings
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