University-Level AI LAB - Platform Development
Budget: ₹1,500 – ₹12,500 INR
Name : University AI lab setup
Description :
Multi‑Tenant University LLM Gateway Platform
Enterprise Architecture & Technical Design Specification
1. Executive Summary
This document defines the enterprise architecture for a scalable, secure, and multi‑tenant AI platform
that enables universities to provide controlled access to Large Language Models (LLMs) for students.
The system provides governance, quotas, observability, cost control, and white‑label capabilities
so that multiple universities can run their own branded AI environments on a shared platform.
2. Business Objectives
• Provide controlled LLM access for students and faculty
• Support multiple universities on a single platform (multi‑tenant)
• Reduce AI infrastructure costs through routing and caching
• Enable administrators to manage quotas and usage
• Provide observability and analytics for AI usage
• Ensure security, compliance, and auditability
3. Stakeholders
University Administration
IT Infrastructure Teams
Faculty & Researchers
Students
AI Platform Operators
Security & Compliance Teams
4. Functional Requirements
• Student API access to LLMs
• Admin dashboard for student management
• API key generation and rotation
• Multi‑provider LLM routing
• Usage monitoring and analytics
• Prompt caching
• Guardrails and safety policies
• Tenant‑level branding and configuration
5. Non‑Functional Requirements
Scalability: Support 50,000+ students
Availability: 99.9% uptime
Security: Strong API authentication and tenant isolation
Performance: Sub‑second response times where possible
Observability: Full tracing and metrics
Extensibility: Support additional model providers easily
6. High Level Architecture
The platform follows a layered architecture consisting of:
Client Layer
API Gateway Layer
LLM Gateway / Orchestration Layer
Optimization Layer (Caching & Routing)
Observability Layer
Provider Integration Layer
7. System Components
API Gateway
Handles authentication, request validation, rate limiting, and routing.
LLM Gateway
Central orchestration layer that applies quotas, routing policies, and logging.
Model Router
Selects the optimal LLM provider based on policies such as cost, latency, and model capability.
Cache Layer
Stores previously generated responses to reduce cost and latency.
Guardrails Engine
Applies security policies and prompt filtering.
Analytics Service
Collects and aggregates usage data for dashboards and reporting.
8. Multi‑Tenant Architecture
Each university is treated as a tenant with isolated configuration and data.
Tenant configuration includes:
• Branding
• Usage limits
• API quotas
• Allowed models
• Budget controls
Tenant identification is resolved via domain or authentication metadata.
9. Request Processing Flow
1. Client sends request to API Gateway
2. Gateway authenticates API key
3. Tenant is resolved
4. Quota validation performed
5. Guardrails policies applied
6. Cache lookup executed
7. Model router selects provider
8. Provider processes request
9. Usage metrics recorded
10. Response returned to client
10. Data Architecture
Operational Database (PostgreSQL)
Stores tenants, students, API keys, quotas.
Cache Database (Redis)
Stores cached prompts and rate‑limit counters.
Analytics Database (ClickHouse)
Stores usage logs and metrics for reporting.
11. Security Architecture
API Key Hashing
All keys stored using strong hashing algorithms.
Transport Security
All traffic encrypted using HTTPS.
Tenant Isolation
Strict tenant filtering in database queries.
Audit Logging
All administrative operations logged.
12. Observability Architecture
Metrics
Latency
Token usage
Cost per request
Model utilization
Tools commonly used:
Prometheus
Grafana
Distributed tracing frameworks
13. Deployment Architecture
The platform is deployed using containerized microservices.
Recommended infrastructure:
Kubernetes cluster
Horizontal auto‑scaling
Load balancing
Managed databases
Core services include:
Gateway pods
Router pods
Analytics pods
Redis cluster
PostgreSQL cluster
14. Cost Optimization Strategies
Model routing based on cost
Prompt caching
Using smaller models for simple tasks
Batch processing where possible
15. Admin Platform
Features available to administrators:
Student management
Quota configuration
Usage dashboards
Provider configuration
Budget enforcement
16. Scaling Strategy
Horizontal scaling of gateway services
Sharded databases for analytics
Redis cluster for caching
Load balancers for traffic distribution
17. API Design
The platform exposes OpenAI‑compatible endpoints such as:
POST /v1/chat/completions
POST /v1/embeddings
GET /v1/models
This ensures compatibility with existing SDKs and frameworks.
18. Disaster Recovery
Automated backups for databases
Multi‑region deployment
Failover routing for LLM providers
19. Future Enhancements
Semantic caching
AI cost optimization engine
Automated model benchmarking
Student chat interface
Advanced policy enforcement
Note : To bag this project you need to share a protype (visual design ) on Multitenant Application or lab setup and share proof of evidence from the experiences on any Cloud and capable to handle multiple users and sessions
Description :
Multi‑Tenant University LLM Gateway Platform
Enterprise Architecture & Technical Design Specification
1. Executive Summary
This document defines the enterprise architecture for a scalable, secure, and multi‑tenant AI platform
that enables universities to provide controlled access to Large Language Models (LLMs) for students.
The system provides governance, quotas, observability, cost control, and white‑label capabilities
so that multiple universities can run their own branded AI environments on a shared platform.
2. Business Objectives
• Provide controlled LLM access for students and faculty
• Support multiple universities on a single platform (multi‑tenant)
• Reduce AI infrastructure costs through routing and caching
• Enable administrators to manage quotas and usage
• Provide observability and analytics for AI usage
• Ensure security, compliance, and auditability
3. Stakeholders
University Administration
IT Infrastructure Teams
Faculty & Researchers
Students
AI Platform Operators
Security & Compliance Teams
4. Functional Requirements
• Student API access to LLMs
• Admin dashboard for student management
• API key generation and rotation
• Multi‑provider LLM routing
• Usage monitoring and analytics
• Prompt caching
• Guardrails and safety policies
• Tenant‑level branding and configuration
5. Non‑Functional Requirements
Scalability: Support 50,000+ students
Availability: 99.9% uptime
Security: Strong API authentication and tenant isolation
Performance: Sub‑second response times where possible
Observability: Full tracing and metrics
Extensibility: Support additional model providers easily
6. High Level Architecture
The platform follows a layered architecture consisting of:
Client Layer
API Gateway Layer
LLM Gateway / Orchestration Layer
Optimization Layer (Caching & Routing)
Observability Layer
Provider Integration Layer
7. System Components
API Gateway
Handles authentication, request validation, rate limiting, and routing.
LLM Gateway
Central orchestration layer that applies quotas, routing policies, and logging.
Model Router
Selects the optimal LLM provider based on policies such as cost, latency, and model capability.
Cache Layer
Stores previously generated responses to reduce cost and latency.
Guardrails Engine
Applies security policies and prompt filtering.
Analytics Service
Collects and aggregates usage data for dashboards and reporting.
8. Multi‑Tenant Architecture
Each university is treated as a tenant with isolated configuration and data.
Tenant configuration includes:
• Branding
• Usage limits
• API quotas
• Allowed models
• Budget controls
Tenant identification is resolved via domain or authentication metadata.
9. Request Processing Flow
1. Client sends request to API Gateway
2. Gateway authenticates API key
3. Tenant is resolved
4. Quota validation performed
5. Guardrails policies applied
6. Cache lookup executed
7. Model router selects provider
8. Provider processes request
9. Usage metrics recorded
10. Response returned to client
10. Data Architecture
Operational Database (PostgreSQL)
Stores tenants, students, API keys, quotas.
Cache Database (Redis)
Stores cached prompts and rate‑limit counters.
Analytics Database (ClickHouse)
Stores usage logs and metrics for reporting.
11. Security Architecture
API Key Hashing
All keys stored using strong hashing algorithms.
Transport Security
All traffic encrypted using HTTPS.
Tenant Isolation
Strict tenant filtering in database queries.
Audit Logging
All administrative operations logged.
12. Observability Architecture
Metrics
Latency
Token usage
Cost per request
Model utilization
Tools commonly used:
Prometheus
Grafana
Distributed tracing frameworks
13. Deployment Architecture
The platform is deployed using containerized microservices.
Recommended infrastructure:
Kubernetes cluster
Horizontal auto‑scaling
Load balancing
Managed databases
Core services include:
Gateway pods
Router pods
Analytics pods
Redis cluster
PostgreSQL cluster
14. Cost Optimization Strategies
Model routing based on cost
Prompt caching
Using smaller models for simple tasks
Batch processing where possible
15. Admin Platform
Features available to administrators:
Student management
Quota configuration
Usage dashboards
Provider configuration
Budget enforcement
16. Scaling Strategy
Horizontal scaling of gateway services
Sharded databases for analytics
Redis cluster for caching
Load balancers for traffic distribution
17. API Design
The platform exposes OpenAI‑compatible endpoints such as:
POST /v1/chat/completions
POST /v1/embeddings
GET /v1/models
This ensures compatibility with existing SDKs and frameworks.
18. Disaster Recovery
Automated backups for databases
Multi‑region deployment
Failover routing for LLM providers
19. Future Enhancements
Semantic caching
AI cost optimization engine
Automated model benchmarking
Student chat interface
Advanced policy enforcement
Note : To bag this project you need to share a protype (visual design ) on Multitenant Application or lab setup and share proof of evidence from the experiences on any Cloud and capable to handle multiple users and sessions