PoC/Demo Deployment Architecture

Lightweight Demo Infrastructure

Platform: Replit Autoscale Deployment

• Runtime: Python 3.11 with Nix package management
• Resources: 2 vCPU, 4GB RAM, 20GB storage
• Port Configuration: 5000 (optimized for Replit's network)
• Auto-scaling: Handles 0-100 concurrent users seamlessly

Database Strategy:

• Primary: Replit PostgreSQL (Neon-backed) for metadata and query history
• Vector Storage: Local ChromaDB with SQLite backend
• File Storage: Local filesystem with JSON cache files
• Backup: Automatic Replit snapshots

Configuration:

# .streamlit/config.toml [server] headless = true address = "0.0.0.0" port = 5000 maxUploadSize = 50 [theme] base = "light" primaryColor = "#1f77b4"

Demo Limitations:

• Maximum 50 pages per crawl session
• 24-hour cache retention
• Single-user concurrent sessions
• Basic rate limiting (1 request/second)

Estimated Costs: -25/month depending on usage

Production-Hardened Deployment Architecture

Multi-Tier Production Infrastructure

Primary Deployment Platform: AWS/GCP with Container Orchestration

Application Tier

Containerization Strategy:

# Production Dockerfile FROM python:3.11-slim WORKDIR /app COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt COPY . . EXPOSE 8080 CMD ["streamlit", "run", "app.py", "--server.port=8080", "--server.address=0.0.0.0"]

Kubernetes Deployment:

• Pods: 3-5 replicas with horizontal auto-scaling
• Resources: 4 vCPU, 8GB RAM per pod
• Load Balancer: NGINX Ingress with SSL termination
• Health Checks: Liveness and readiness probes

Database Architecture

Primary Database:

PostgreSQL 14+ (AWS RDS/Google Cloud SQL)

• Configuration: db.r5.xlarge (4 vCPU, 32GB RAM)
• Storage: 500GB SSD with automated backups
• High Availability: Multi-AZ deployment with read replicas
• Connection Pooling: PgBouncer with 100 max connections

Vector Database:

Managed ChromaDB or Pinecone

• ChromaDB: Self-hosted on dedicated instances
• • Specs: 8 vCPU, 32GB RAM, 1TB NVMe SSD
• • Clustering: 3-node cluster with replication
• Alternative: Pinecone (managed service)
• • Plan: Standard tier with 100M+ vectors
• • Performance: Sub-50ms query latency

Caching Layer:

Redis Cluster

• Configuration: 3-node cluster with 16GB memory each
• Purpose: Session management, query caching, rate limiting
• Persistence: RDB + AOF for durability

Content Delivery & Storage

Object Storage:

AWS S3/Google Cloud Storage

• Cache Files: Compressed JSON with embeddings
• Static Assets: CSS, JS, images with CloudFront CDN
• Backup Strategy: Cross-region replication
• Lifecycle Policies: Automatic archival after 90 days

CDN Configuration:

• Primary: CloudFront/Cloud CDN
• Edge Locations: Global distribution
• Caching: Static assets (1 year), API responses (5 minutes)
• Compression: Gzip/Brotli for text content

Security & Monitoring

API Security:

# Rate limiting configuration RATE_LIMITS = { 'crawling': '5 per hour per user', 'questions': '100 per hour per user', 'search': '1000 per hour per user' } # Authentication AUTH_PROVIDERS = ['OAuth2', 'API_Key', 'JWT']

Infrastructure Security:

• WAF: AWS WAF/Google Cloud Armor with DDoS protection
• VPC: Private subnets for database and application tiers
• Secrets Management: AWS Secrets Manager/Google Secret Manager
• SSL/TLS: Let's Encrypt with automatic renewal

Monitoring Stack:

• Application Monitoring: DataDog/New Relic
• Infrastructure: Prometheus + Grafana
• Logging: ELK Stack (Elasticsearch, Logstash, Kibana)
• Alerting: PagerDuty integration for critical issues

Performance Optimization

Auto-Scaling Configuration:

# Kubernetes HPA Configuration apiVersion: autoscaling/v2 kind: HorizontalPodAutoscaler spec: minReplicas: 3 maxReplicas: 20 metrics: - type: Resource resource: name: cpu target: type: Utilization averageUtilization: 70 - type: Resource resource: name: memory target: type: Utilization averageUtilization: 80

Database Optimization:

• Connection Pooling: PgBouncer with 500 max connections
• Query Optimization: Automated EXPLAIN analysis
• Indexing Strategy: Composite indexes on frequently queried columns
• Partitioning: Date-based partitioning for large tables

Disaster Recovery & Backup

Backup Strategy:

• Database: Automated daily backups with 30-day retention
• Vector Database: Weekly full backups with incremental daily
• Application State: Container image versioning and rollback capability
• File Storage: Cross-region replication with versioning

Recovery Procedures:

• RTO (Recovery Time Objective): 15 minutes
• RPO (Recovery Point Objective): 1 hour
• Failover: Automated with health check triggers
• Testing: Monthly disaster recovery drills

Scaling Scenarios & Resource Planning

Small Scale (100-500 users)

Infrastructure:

• 3 application pods (2 vCPU, 4GB RAM each)
• PostgreSQL: db.t3.large (2 vCPU, 8GB RAM)
• ChromaDB: Single instance (4 vCPU, 16GB RAM)
• Redis: Single instance (2GB memory)

Estimated Costs: 00-1,200/month

Medium Scale (500-5,000 users)

Infrastructure:

• 5-10 application pods with auto-scaling
• PostgreSQL: db.r5.xlarge with read replicas
• ChromaDB: 3-node cluster
• Redis: 3-node cluster
• CDN and advanced monitoring

Estimated Costs: $2,500-4,000/month

Large Scale (5,000+ users)

Infrastructure:

• 10-50 application pods across multiple zones
• PostgreSQL: Multi-AZ with multiple read replicas
• Managed vector database (Pinecone/Weaviate Cloud)
• Full observability and security stack
• Dedicated DevOps engineer

Estimated Costs: $8,000-15,000/month

DevOps & CI/CD Pipeline

Development Workflow

GitHub Actions Pipeline

name: Production Deployment on: push: branches: [main] jobs: test: runs-on: ubuntu-latest steps: - uses: actions/checkout@v3 - name: Run Tests run: | python -m pytest tests/ python -m flake8 src/

build: needs: test runs-on: ubuntu-latest steps: - name: Build Docker Image run: docker build -t web-analyzer:$GITHUB_SHA .

deploy: needs: build runs-on: ubuntu-latest steps: - name: Deploy to Kubernetes run: kubectl apply -f k8s/

Environment Management

• Development: Local Docker Compose
• Staging: Kubernetes cluster with production data subset
• Production: Full production infrastructure with blue-green deployment

Performance Benchmarks

• Crawling: 50-100 pages/minute per worker
• Query Response: <2 seconds for most questions
• Concurrent Users: 1,000+ with proper scaling
• Embedding Processing: 10,000 chunks/minute
• Database Queries: <100ms average response time

This infrastructure design provides a clear evolution path from simple demo to enterprise-grade production deployment, with appropriate cost scaling and performance optimization at each tier.