Enterprise-Grade Tech Stack

Modern, scalable, and secure technology choices for AI-powered systems

Backend

Core Stack

  • FastAPI + Uvicorn/Gunicorn: High-performance async APIs for microservices and AI integrations
  • Docker: Containerization ensuring parity between local and production environments

Deployment

  • Fly.io: Primary hosting with global edge deployment, built-in Postgres, and fast rollback
  • Enterprise alternatives: AWS ECS/Fargate, GCP Cloud Run, Azure Container Apps

Data Layer

  • PostgreSQL: Main relational database (Fly.io Postgres, RDS, or Cloud SQL)
  • Redis: Caching and ephemeral storage for fast lookups and queueing
  • Vector Database: Milvus, Pinecone, or FAISS for AI embeddings
  • SQLAlchemy/SQLModel: Clean data access abstraction
  • Backup strategy: Nightly dumps + weekly snapshots stored offsite

CI/CD Pipeline

  • GitHub Actions/GitLab CI: Automated testing, linting, and deployments
  • Pre-commit hooks: Pyright for code quality
  • Branch protection: Reviews and test runs required before merge
  • Staging environment: Production mirror with masked data for QA

Observability & Operations

  • Prometheus + Grafana: Metrics collection and live dashboards
  • Sentry: Error tracking and real-time alerting
  • Loki/ELK Stack: Centralized logging and event storage
  • Health endpoints: /healthz, /metrics for uptime monitoring

Cloud & Infrastructure

Cloud Platforms

  • AWS/GCP/Azure: Chosen based on client preference and compliance
  • Compute: ECS/Fargate, Cloud Run for containers
  • Storage: S3/GCS for static assets and model weights

Infrastructure as Code

  • Terraform/Pulumi: Reproducible environments
  • Secrets Management: AWS/GCP Secret Manager integration
  • CDN: Cloudflare/CloudFront for caching and DDoS protection

Machine Learning & AI Infrastructure

Language Models & APIs

  • OpenAI: GPT models for general AI applications
  • Reasoning Models: UX Pilot for complex reasoning tasks
  • Azure OpenAI: Enterprise compliance requirements
  • Hugging Face: Open models and inference API

Development & Orchestration

  • LangChain/LangGraph: Composable reasoning pipelines
  • Embeddings: UX Pilot AI, Sentence-Transformers, or custom models
  • Model versioning: DVC or MLflow for tracking datasets and weights
  • Prompt registry: Version-controlled prompts and test suites

Performance Optimization

Redis caching with TTL for inference results ensures cost control and latency reduction across all AI operations.

Security Baseline

  • Multi-factor Authentication
  • Regular Key Rotation
  • Dependency Scanning
  • Bucket Policy Audits
  • VPC Isolation Options
  • Continuous Monitoring

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