Chapter 13: Building for Everyone: Multitenant Architecture

In this chapter

A multitenant solution refers to a single software application that is utilized by multiple customers, also known as tenants. It’s important to distinguish between tenants and users; while users are individual accounts, a tenant represents a collective group of users from a single organization, company, or group.Examples of multitenant applications include:

Business-to-Business (B2B): multitenant solutions are often seen in software as a service (SaaS) products like accounting software and work tracking tools. These applications are shared among different businesses, each operating as a distinct tenant.

Business-to-Consumer (B2C): multitenant solutions are common in services like music streaming platforms, photo sharing apps, and social networking sites. Here, each consumer forms a tenant.

Enterprise-wide platform solutions: multitenant solutions can take the form of shared platform services. An example is a shared Kubernetes cluster used by multiple business units within a single organization, each unit acting as a separate tenant.

When building your own multitenant solutions, there are several key architectural considerations that will influence your design, as well as different architectural approaches.

Building for Everyone: Multitenant Architecture

Architectural approaches for AI and ML in multitenant solutions

When building AI/ML applications in a multitenant environment, there are several key considerations and approaches to keep in mind:

  • Data Isolation and Privacy: Each tenant’s data should be isolated from others to ensure privacy and security. This is particularly important in AI/ML applications where data is the key driver of the system’s learning and predictions.

  • Model Training and Deployment: Depending on the application, you might need to train a separate model for each tenant or a shared model for all tenants. If you’re training separate models, you’ll need to manage resources efficiently to handle the computational load. If you’re using a shared model, you’ll need to ensure that it performs well across all tenants.

  • Scalability: As the number of tenants increases, your AI/ML application should be able to scale accordingly. This includes the ability to handle increased data volumes, model training tasks, and prediction requests.

  • Performance: The performance of the AI/ML application should be consistent across all tenants, regardless of the size or activity level of each tenant.

  • Customizability: Depending on your application, you might need to provide tenants with the ability to customize the AI/ML models or features to suit their specific needs.

  • Cost Management: The costs of running AI/ML workloads can be high, especially when it comes to data storage and compute resources for model training and prediction. You’ll need to manage these costs effectively while ensuring fair pricing for each tenant.

Approaches to these considerations can vary. For example, you might choose to have each tenant’s data and models completely isolated. Alternatively, you might opt to have data and models that are shared across tenants, but with strict access controls to ensure data privacy. Hybrid approaches are also possible, depending on the specific needs and constraints of your application. The article “Architectural approaches for AI and ML in multitenant solutions” offers a comprehensive and detailed analysis of the topic.

Multitenancy and Azure OpenAI Service

When architecting a multitenant system utilizing Azure OpenAI, several key components and isolation models must be taken into account. The choice of isolation model is crucial and can significantly impact the system’s performance and cost-effectiveness.

Isolation models, which include one instance per tenant, one instance per provider, or shared instances, offer varying degrees of data and performance isolation. Each tenancy model presents its own set of benefits and challenges. Additionally, employing managed identities and Role-Based Access Control (RBAC) can ensure secure access to Azure OpenAI.

In conclusion, careful consideration of isolation models, customization, and security measures is essential for optimal performance and security. For a more comprehensive understanding, the article “Multitenancy and Azure OpenAI Service” provides an in-depth exploration of the topic.

The article “Design patterns for multitenant SaaS applications and Azure AI Search” provides a comprehensive guide on tenant isolation strategies for applications built with Azure AI Search. This service is a powerful tool that empowers developers to integrate rich search experiences into their applications, eliminating the need for infrastructure management or expertise in information retrieval. Additionally, it highlights valuable insights into prevalent design patterns for multitenant scenarios, including one index per tenant, one service per tenant, a mix of both, and a single index with multiple scopes. Each pattern’s advantages and disadvantages are thoroughly analyzed, considering factors like tenant isolation, cloud resource cost, operational ease, global footprint, and scalability.

Key Takeaways for AI Multitenant Architecture

Multitenant architecture is a design pattern that allows multiple customers or users to share the same application or service, while maintaining their own data and configuration. AI multitenant solutions pose unique challenges and opportunities, such as data isolation, model customization, resource allocation, and scalability, with different architectural approaches and trade-offs. To effectively manage these challenges consider the following recommendations:

  • Implement robust data isolation: Ensure that each tenant’s data is securely isolated from others to maintain privacy and security.

  • Allow for model customization: Different tenants may have different needs and preferences. Therefore, it’s important to allow for customization of AI models to cater to individual tenant requirements.

  • Efficient resource allocation: Resources should be allocated in a way that optimizes performance for all tenants. This could involve load balancing, dynamic resource allocation, or other strategies.

  • Plan for scalability: As the number of tenants increases, the system should be able to scale accordingly without compromising on performance or functionality.