Scaling & Optimization
Grow your AI product efficiently while maintaining quality and controlling costs.
Performance Optimization
- Model optimization: Use quantization, pruning, or distillation to create smaller, faster models
- Caching: Cache identical requests (up to 80% hit rate for common queries), use embeddings for semantic similarity caching
- Batch processing: Group requests together when possible to improve throughput
- Edge deployment: Deploy models closer to users (edge locations, CDNs) for lower latency
- Async processing: Move long-running tasks to background queues to keep UI responsive
- Connection pooling: Reuse database and API connections to reduce overhead
- CDN for static assets: Serve images, JS, CSS from CDN for faster page loads
Cost Optimization
AI Model Costs
- Switch to cheaper models for simpler tasks (GPT-5 → GPT-4.5 or Claude 4 Sonnet for basic queries)
- Use prompt engineering to reduce token usage
- Implement aggressive caching (50-80% cost reduction)
- Consider self-hosting open-source models if volume is high
- Negotiate enterprise pricing with API providers
Infrastructure Costs
- Right-size instances (don't over-provision)
- Use spot/preemptible instances for non-critical workloads (70% savings)
- Implement autoscaling to match demand
- Optimize database queries and indexes
- Use reserved instances for predictable workloads (30-50% discount)
- Monitor and eliminate wasteful spending
Infrastructure Scaling
Horizontal Scaling: Add more servers/containers to handle increased load. Use load balancers to distribute traffic.
Database Scaling:
- Read replicas for read-heavy workloads
- Sharding for write-heavy or very large datasets
- Connection pooling to handle more concurrent users
- Caching layer (Redis) to reduce database load
Auto-Scaling: Configure automatic scaling based on metrics (CPU, memory, request queue length)
Rate Limiting: Protect infrastructure from abuse while maintaining fair access
Model Improvement
Continuous training: Retrain models on new data regularly to maintain accuracy
Fine-tuning: Use production data to fine-tune for better performance on real use cases
A/B testing: Test model variants, prompts, or features to optimize results
Feedback integration: Incorporate user corrections into training data
Version control: Track model versions, roll back if new versions underperform
Key Takeaways
- Optimize performance through caching, model optimization, and smart architecture
- Reduce costs by right-sizing infrastructure and using cheaper models where appropriate
- Scale horizontally and use auto-scaling to handle growth efficiently
- Continuously improve models based on production data and user feedback