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Tech Stack Document

Tech Stack Document: Unity AI Beta Program

Version: 1.0
Date: 2023-10-05

1. Introduction

The Unity AI Beta Program establishes an open AI ecosystem connecting creators with AI tools to accelerate RT3D content creation. This document outlines the technical stack, emphasizing scalability for global users, secure AI/creator data handling, and real-time performance for AI-driven workflows.

2. System Architecture Overview

A microservices architecture decouples core components for flexibility:

  • Frontend: Creator portal for AI tool access and beta management.
  • Backend: API gateway, user/auth services, and AI orchestration.
  • AI Layer: Inference engines for Unity Editor plugins and cloud-based AI tools.
  • Data Layer: Structured/unstructured data storage.
  • Infrastructure: Kubernetes-managed cloud deployment.

3. Technology Stack

3.1 Frontend

  • Framework: React 18.2 + TypeScript 5.0
  • UI Library: Material-UI 5.14
  • State Management: Redux Toolkit 1.9
  • Real-Time Updates: Socket.IO 4.7
  • Testing: Jest 29.6 + React Testing Library 14.0

3.2 Backend

  • API Gateway: NGINX 1.24 + OAuth 2.0
  • Core Services:
    • Language: Python 3.11 (FastAPI 0.103)
    • Orchestration: Celery 5.3 + Redis 7.0 (task queue)
  • Authentication: Auth0 SDK + JWT
  • API Standards: RESTful APIs (OpenAPI 3.0)

3.3 AI Services

  • Model Hosting: NVIDIA Triton Inference Server 23.07
  • AI Frameworks: PyTorch 2.0 (custom models), TensorFlow Lite (edge)
  • Unity Integration:
    • Editor Plugins: C# .NET 6 + Unity ML-Agents Toolkit 20.0
    • Cloud APIs: gRPC 1.54 (low-latency inference)
  • Toolchain: Kubeflow 1.7 (MLOps pipelines)

3.4 Database

  • Structured Data: PostgreSQL 15 (user profiles, beta metadata)
  • Unstructured Data: MongoDB 6.0 (AI-generated assets, logs)
  • Caching: Redis 7.0
  • Analytics: Apache Kafka 3.5 + Elasticsearch 8.8 (user behavior)

3.5 DevOps & Infrastructure

  • Cloud: Google Cloud Platform (GKE Autopilot)
  • Containerization: Docker 23.0, Kubernetes 1.27
  • CI/CD: GitHub Actions + Argo CD 2.7
  • Monitoring: Prometheus 2.45 + Grafana 9.5
  • Security: HashiCorp Vault 1.14 (secrets), Open Policy Agent (OPA)

4. Implementation Steps

Phase 1: Foundation (8 Weeks)

  1. Setup Infrastructure:
    • Deploy GKE cluster with autoscaling (min 3 nodes, max 50).
    • Configure VPC networks with Cloud Armor (DDoS protection).
  2. Core Backend:
    • Implement Auth0 integration for OAuth 2.0/JWT.
    • Build REST APIs for user registration/beta enrollment.
  3. Data Layer:
    • Deploy PostgreSQL (Cloud SQL) with read replicas.
    • Configure MongoDB Atlas for document storage.

Phase 2: AI Integration (6 Weeks)

  1. AI Toolchain:
    • Containerize PyTorch models using Triton Inference Server.
    • Develop gRPC endpoints for Unity Editor ↔ Cloud inference.
  2. Unity Plugin SDK:
    • Create C# SDK for real-time AI suggestions (e.g., asset generation).

Phase 3: Frontend & Beta Launch (4 Weeks)

  1. Creator Portal:
    • Build React dashboard for tool access and feedback submission.
    • Integrate Socket.IO for live beta updates.
  2. Testing:
    • Load-test APIs with Locust (10K RPS target).
    • Penetration testing via OWASP ZAP.

5. Security Considerations

  • Data Encryption: AES-256 at rest (GCP KMS), TLS 1.3 in transit.
  • Access Control: RBAC via OPA + Kubernetes Network Policies.
  • AI Ethics: Model bias monitoring (Fiddler.ai integration).
  • Compliance: GDPR/CCPA adherence; audit logs in SIEM.

6. Performance & Scalability

  • AI Latency: <500ms inference via GCP GPUs (T4/V100).
  • Scalability:
    • Auto-scale backend pods (CPU >70%).
    • Redis sharding for 1M+ concurrent users.
  • Cost Optimization: Spot instances for batch AI jobs.

7. Conclusion

This stack enables a secure, scalable ecosystem for Unity’s AI Beta Program. By leveraging Kubernetes, GPU-accelerated AI, and real-time frontend tools, Unity can onboard millions of creators while maintaining sub-second AI responsiveness. Future extensibility includes integrating third-party AI tools via GraphQL APIs.

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