AI-Ready Infrastructure Design – A pattern for Enterprise Scale

I. Introduction

As artificial intelligence (AI) permeates industries, businesses increasingly seek to integrate AI capabilities into their operations. However, deploying AI at enterprise scale presents unique challenges. Organizations often struggle to manage multiple AI services, ensure consistent governance, and maintain security across business units.

II. The Problem: Challenges of Enterprise AI Adoption

Before we look at solutions, let’s look at the key challenges enterprises face when adopting AI at scale.

1. Distributed AI Service Management: As different departments independently adopt AI solutions, organizations often find that AI services are fragmented, making it difficult to maintain consistency and control.
2. Inconsistent governance and security practices: The introduction of decentralized AI has raised the challenge of ensuring consistent governance policies and security measures across all AI implementations.
3. Difficulties in implementing usage tracking and refund mechanisms: Without a centralized system, it is difficult to monitor AI service usage across the organization and implement a fair billing process for different business units.
4. Concerns about scalability and resiliency: As the use of AI increases within organizations, it becomes increasingly important to scale your infrastructure to keep up and remain resilient.
5. Cost management for AI services: Unmonitored and inefficient use of API usage can lead to budget overruns, spending discrepancies, and difficulties in fairly allocating costs across departments. As a result, it is difficult to ensure that API costs contribute directly to business goals.

III. Solution: AI Hub Gateway Landing Zone

that AI Hub Gateway Landing Zone is a solution accelerator. We address these challenges by providing a centralized architecture for managing AI services within your organization. It acts as a single point of entry for AI services, enabling consistent management and governance.

Key Features and Benefits:

1. Centralized AI API Gateway: It acts as a hub for all AI services, providing a unified entry point that can be shared across multiple use cases in a secure and managed manner.
2. Seamless integration with Azure AI services: You can easily migrate to AI Hub Gateway by updating the endpoints and keys of your existing applications.
3. AI Routing and Orchestration: Provides a mechanism to route and orchestrate AI services based on priorities and target models, ensuring consistent management and governance.
4. Fine-grained access control: Strengthen security by allowing consumers to use gateway keys while accessing AI services using managed identities instead of master keys.
5. Personal connection: Designed to be deployed on private networks and securely access AI services by leveraging private endpoints.
6. Capacity Management: Provides a mechanism to manage capacity based on requests and tokens to ensure optimal resource utilization.
7. Use and Refund: Implement usage tracking and billing for each business unit with flexible integration options to existing billing and data platforms.
8. Elasticity and Scalability: Leverage Azure API Management with zone redundancy and regional gateways to deliver a scalable and resilient solution.
9. Total Observability: Integrates with Azure Monitor, Application Insights, and Log Analytics to provide detailed insights into performance, usage, and errors.
10. Hybrid Support: Supports deployment of backends and gateways in Azure, on-premises, or other clouds, providing flexibility in infrastructure choice.

IV. How it works: Architecture and components

AI Hub Gateway Landing Zone leverages multiple Azure components to create a robust and scalable solution.

1. Azure API Management: Acts as a central hub that powers most of the GenAI Gateway functionality and manages API requests.
2. Application Insights: Provides critical insights into the operational performance of your gateway, including dashboards for key metrics.
3. Event Hub: Used for streaming usage and billing data for target data and billing platforms.
4. Azure OpenAI: Deploy instances across multiple regions to provide access to cutting-edge generative models.
5. Cosmos DB: Store usage and billing data in a fully managed NoSQL database.
6. Azure Function App: Supports real-time event processing for usage and billing data.
7. User Management ID: Securely access various Azure services without exposing your credentials.
8. Virtual Network: Host Azure API Management and other Azure resources in a secure network environment.
9. Private endpoints and private DNS zones: Enhance security by enabling private connectivity to various Azure services.

AI Hub Gateway Key Components:

• Hub Performance Monitoring:
  • Monitor the performance and status of AI services and infrastructure.
  • Connect to other components via personal links, ensuring secure and professional communication.
• AI Usage Metrics (Chargeback) Event Hub:
  • We collect and process usage data for our AI services.
  • It is likely to be used for refunds or cost management and provides insight into how resources are consumed.
  • Data is processed and sent to a data platform for visualization and reporting.

API Management (AI API Gateway):

• Centralizes management of API calls related to AI services.
• Routes API calls to AI services such as OpenAI, cognitive services, and other third-party models.
• Provides secure access to a variety of AI/ML services, ensuring consistent and controlled API usage.
• Provides robust disaster recovery setup with enhanced reliability through automatic failover capabilities.

Data Platform:

• handle Visualization and Reporting About AI usage metrics
• Connected via a private link that ensures secure data transmission across event hubs.

Public and private transportation:

• Public transportation:
  • API calls are made to the API gateway via the Internet from external applications such as Retail Smart Shopping App, Customer Care Chat, and Finance Smart Analysis.
  • These API calls are processed by DNS and DMZ network appliances before being routed to the API management layer.
• Private traffic:
  • Used for communication between internal components (e.g. hub performance monitoring, AI usage metrics, etc.) and secure access to backend AI services.

AI Services:

• Backend system:
  • This includes services like Azure App Services, Azure Kubernetes Service (AKS), and Azure Container Apps (ACA) that host and orchestrate AI workloads.
• AI/ML Services:
  • Cognitive services, Azure AI Search, third-party large-scale language models (LLMs), and custom machine learning models.

Integration with OpenAI and other AI providers:

• Primary and secondary service areas:
  • Endpoint 1 (primary region, e.g. PTU) and Endpoint 2 (secondary region, e.g. PAYG) represent the geographic distribution of AI services to ensure availability and redundancy.
  • API calls to OpenAI services are routed through an API management layer.

Security and Networking:

• DMZ Network Appliance:

  • These devices are likely to be firewalls, intrusion detection/prevention systems (IDS/IPS), or other network security devices protecting the entry points to the AI ​​hub.
• Personal Link:
  • Emphasizes secure communication between internal components to reduce exposure to external threats.

AI Orchestrator:

• You will likely be responsible for managing the deployment, scaling, and lifecycle of AI models and services.

Overall architectural insights:

• This architecture is designed for: Centralized governance AI Service Focus Security, performance monitoring, and Cost Management.
• that API Gateway Plays a central role in standardizing and securing access to AI services, whether internal or third-party.
• Redundancy and failover mechanisms Ensures high availability and business continuity.
• that Use personal link Emphasizes prioritizing secure communications within your infrastructure.

Using Azure Developer CLI (azd) or Bicep (IaC) simplifies the deployment process, providing a one-click deployment option that sets up all the required components in your Azure subscription.

V. Getting Started

To get started with the AI ​​Hub Gateway Solution Accelerator, you need:

Prerequisites

• Azure account (new users can get started with free credits)
• An Azure subscription with access to Azure OpenAI services.
• Appropriate Azure account permissions (e.g. User Access Administrator or Owner)

For regional development:

• Azure CLI
• Azure Developer CLI (azd)
• VS Code

Deployment Options

This solution provides one-click deployment options using Azure Developer CLI (azd) or Bicep (IaC). The basic deployment process is as follows:

1. Clone repository
2. Review and adjust main.bicep Files that fit your configuration needs
3. Run the following command:

azd auth login
azd env new ai-hub-gateway-dev
azd up

Basic configuration and customization

that main.bicep Files allow you to customize various aspects of your deployment, including:

• OpenAI instances and locations
• Model deployment and capacity
• Network configuration

Always ensure that your chosen region has sufficient OpenAI capacity before deploying.

VI. Conclusion

The AI ​​Hub Gateway Solution Accelerator represents a significant step forward in enterprise AI adoption. It addresses many of the challenges organizations face when implementing AI at scale by providing a centralized, secure, and scalable architecture for managing AI services.

Try the AI ​​Hub Gateway Solution Accelerator and see for yourself how it can transform your organization’s approach to managing AI services. Whether you’re just starting your AI journey or looking to optimize your existing AI infrastructure, this solution provides a solid foundation for growth and innovation.