Technical Architecture
Design a robust, scalable technical foundation that supports your AI product's requirements and growth.
Your technical architecture determines your product's performance, costs, scalability, and ability to iterate quickly. AI products have unique architectural considerations: model hosting, inference latency, data pipelines, and compute costs.
The right architecture balances functionality, cost, performance, and time-to-market. For MVP, bias toward simplicity and managed services—you can always optimize later.
Model Selection Strategy
Model Approach Decision Tree
Option 1: Third-Party APIs (Recommended for MVP)
Use OpenAI (GPT-5), Anthropic (Claude 4), Google (Gemini 2.5), or similar
Pros: Fastest time-to-market, no infrastructure management, continuous improvements, excellent quality
Cons: Ongoing API costs, less control, data privacy considerations, vendor lock-in risk
Option 2: Open-Source Models (Self-Hosted)
Use Llama, Mistral, Falcon, or similar open models
Pros: Full control, data stays private, predictable costs at scale, customization
Cons: Infrastructure management, slower iteration, requires ML expertise, hosting costs
Option 3: Fine-Tuned Models
Start with base model, fine-tune on your data
Pros: Optimized performance, smaller/faster models, domain expertise
Cons: Requires quality training data, longer development cycle, ongoing maintenance
Option 4: Custom Model (Advanced)
Build and train your own model architecture
Pros: Maximum customization, potential competitive advantage
Cons: Extremely resource-intensive, long development time, requires deep ML expertise
MVP Recommendation
For most AI product launches, start with third-party APIs (Option 1). This lets you validate product-market fit quickly without infrastructure complexity. You can always migrate to self-hosted models later if unit economics or privacy requirements demand it.
System Architecture Design
Core Components
Frontend Application
User interface, input collection, result display. Tech: React/Next.js, Vue, Svelte
API Layer
Request handling, auth, rate limiting, routing. Tech: Node.js, Python/FastAPI, Go
AI Service Layer
Model inference, prompt engineering, response processing. Tech: Python, LangChain, LlamaIndex
Data Layer
Storage for user data, conversations, feedback. Tech: PostgreSQL, MongoDB, Redis for caching
Queue/Worker System
Async processing for long-running AI tasks. Tech: BullMQ, Celery, AWS SQS
Monitoring & Logging
Observability, performance tracking, error tracking. Tech: Datadog, New Relic, Sentry
Architecture Patterns
- Synchronous (Simple): User request → API → AI model → Response. Good for fast inferences (< 3 seconds)
- Asynchronous (Recommended): User request → Queue → Worker processes → Notify user. Better for slower AI tasks (> 3 seconds)
- Streaming: Send results progressively as AI generates. Excellent UX for text generation (like ChatGPT)
- Batch Processing: Process multiple items together for efficiency. Good for non-real-time use cases
Infrastructure & Hosting
Hosting Options
Serverless (Great for MVP)
Platforms: Vercel, Netlify, AWS Lambda, Cloud Functions
Best for: Low to moderate traffic, unpredictable usage patterns, API-based AI (not self-hosted models)
Container-Based
Platforms: AWS ECS/EKS, Google Kubernetes Engine, DigitalOcean Kubernetes
Best for: Self-hosted models, GPU requirements, high traffic, need for control
Platform as a Service (PaaS)
Platforms: Railway, Render, Fly.io, Heroku
Best for: Simplicity, fast deployment, moderate scale
GPU-Optimized Hosting
Platforms: Modal, RunPod, Lambda Labs, Paperspace
Best for: Self-hosted models requiring GPU acceleration
Infrastructure Checklist
- □CDN for static assets and media delivery
- □Load balancing for high availability
- □Database with backups and replication
- □Caching layer (Redis/Memcached) for frequently accessed data
- □Object storage for user uploads and generated content (S3, DigitalOcean Spaces)
- □CI/CD pipeline for automated deployments
Scalability Planning
Scaling Considerations
- Horizontal Scaling:Add more servers/containers to handle increased load. Essential for AI services with unpredictable traffic spikes.
- Caching Strategy:Cache AI responses for identical or similar requests. Can reduce costs by 50-80% for common queries.
- Rate Limiting:Implement per-user rate limits to prevent abuse and control costs. Start conservative, increase based on usage patterns.
- Model Optimization:Use quantization, distillation, or smaller models for faster inference and lower costs as you scale.
- Database Scaling:Plan for read replicas, sharding, or managed database services as data grows.
- Queue Management:Use job queues to handle burst traffic and prevent system overload during usage spikes.
Security Architecture
Security Essentials
- □Authentication & authorization (OAuth, JWT, or session-based)
- □API key management and rotation
- □Input validation and sanitization (prevent prompt injection)
- □Output filtering (prevent exposure of sensitive data)
- □Encryption in transit (HTTPS/TLS) and at rest
- □Secrets management (never commit API keys, use environment variables or vaults)
- □DDoS protection and WAF (Web Application Firewall)
- □Audit logging for compliance and debugging
Key Takeaways
- For MVP, prefer third-party AI APIs over self-hosting—ship faster, validate PMF first
- Design for async processing if AI tasks take more than 3 seconds
- Choose infrastructure that matches your scale and expertise—serverless for simplicity, containers for control
- Plan for horizontal scaling and implement caching early to manage costs
- Security is critical—validate inputs, filter outputs, encrypt everything, manage secrets properly
- Start simple, add complexity only when necessary—premature optimization wastes time