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Whitepaper

Scalability & Performance at ZEROCODE

Oct 2024
20 min read
Engineering Team

Scalability & Performance at ZEROCODE

Building a platform that generates production-ready applications requires robust, scalable infrastructure. This whitepaper details our technical architecture and the decisions that enable ZEROCODE to serve thousands of users.

Infrastructure Overview

Our platform is built on a modern cloud-native architecture:

  • Frontend: Next.js 14 with App Router, deployed on Vercel Edge Network
  • Backend: Node.js microservices on AWS ECS
  • Database: PostgreSQL (Supabase) with read replicas
  • AI Processing: GPU-accelerated instances for code generation
  • CDN: Cloudflare for global content delivery

Database Architecture

Schema Design

We use a multi-tenant architecture with logical data separation:

-- Projects table
CREATE TABLE projects (
  id UUID PRIMARY KEY,
  user_id UUID REFERENCES users(id),
  name TEXT NOT NULL,
  config JSONB,
  created_at TIMESTAMP DEFAULT NOW()
);

-- Generated code storage
CREATE TABLE code_artifacts (
  id UUID PRIMARY KEY,
  project_id UUID REFERENCES projects(id),
  file_path TEXT,
  content TEXT,
  version INTEGER
);

Performance Optimizations

  1. Indexing Strategy: Composite indexes on frequently queried columns
  2. Connection Pooling: PgBouncer for efficient connection management
  3. Query Optimization: Prepared statements and query plan analysis
  4. Caching Layer: Redis for session data and frequently accessed content

AI Code Generation Pipeline

Request Processing

When a user submits a prompt:

  1. Queue Management: Requests enter a priority queue (AWS SQS)
  2. Resource Allocation: Dynamic scaling based on queue depth
  3. Generation: GPU instances process prompts in parallel
  4. Validation: Automated tests verify generated code
  5. Storage: Code artifacts saved to S3 with versioning

Scaling Strategy

  • Horizontal Scaling: Auto-scaling groups adjust based on demand
  • Load Balancing: Application Load Balancer distributes traffic
  • Circuit Breakers: Prevent cascade failures
  • Rate Limiting: Per-user quotas to ensure fair resource allocation

Performance Metrics

Current system performance:

  • Average Response Time: 2.3 seconds for code generation
  • P95 Latency: 4.8 seconds
  • Throughput: 10,000+ requests per hour
  • Uptime: 99.9% over the last 12 months

Monitoring & Observability

We use a comprehensive monitoring stack:

  • Metrics: Prometheus + Grafana
  • Logging: ELK Stack (Elasticsearch, Logstash, Kibana)
  • Tracing: OpenTelemetry for distributed tracing
  • Alerting: PagerDuty integration for critical issues

Security Considerations

Data Protection

  • Encryption at rest (AES-256)
  • TLS 1.3 for data in transit
  • Regular security audits
  • SOC 2 Type II compliance

Access Control

  • Role-based access control (RBAC)
  • Multi-factor authentication
  • API key rotation policies
  • Audit logging for all operations

Cost Optimization

Strategies for managing infrastructure costs:

  1. Spot Instances: For non-critical workloads
  2. Reserved Capacity: For predictable baseline load
  3. Storage Tiering: S3 Intelligent-Tiering for code artifacts
  4. CDN Optimization: Aggressive caching policies

Future Improvements

Planned enhancements:

  • Edge computing for code generation
  • GraphQL API for more efficient data fetching
  • WebAssembly for client-side processing
  • Multi-region deployment for lower latency

Conclusion

Building a scalable AI development platform requires careful attention to architecture, performance, and cost. Our infrastructure is designed to grow with our users while maintaining high performance and reliability.