Lilly Tech Systems
Multi-Tenant AI Platform
Design and operate AI platforms that serve multiple tenants securely and efficiently. Learn multi-tenant architecture patterns, data and model isolation, resource management, usage-based billing, and operational best practices for shared AI infrastructure.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is multi-tenant AI? Why shared platforms matter, tenancy models, trade-offs, and key design considerations.
2. Architecture
Platform architecture patterns, control plane design, tenant onboarding, shared vs. dedicated components, and routing.
3. Isolation
Data isolation, model isolation, network segmentation, compute boundaries, and compliance with tenant data regulations.
4. Resource Management
Quota systems, fair scheduling, GPU sharing, auto-scaling per tenant, capacity planning, and noisy neighbor prevention.
5. Billing
Usage metering, cost attribution, pricing models, billing integration, chargeback systems, and financial reporting.
6. Best Practices
Operational excellence, tenant lifecycle management, platform evolution, security hardening, and scaling strategies.
What You'll Learn
By the end of this course, you'll be able to:
Design Multi-Tenant Platforms
Architect AI platforms that serve multiple tenants with appropriate isolation, security, and customization capabilities.
Implement Isolation
Build proper data, model, and compute isolation boundaries that satisfy compliance requirements and prevent data leakage.
Manage Resources Fairly
Implement quota systems, fair scheduling, and auto-scaling that prevent noisy neighbors while maximizing resource utilization.
Build Billing Systems
Create usage metering and cost attribution systems that support flexible pricing models and accurate financial reporting.