Scaling AI Infrastructure: Lessons from Building Afto

Building AI Infrastructure at Scale

When we started building Afto, we knew we needed an AI infrastructure that could handle millions of automation tasks daily while maintaining low latency and high reliability. Our platform processes diverse workloads including real-time document processing with GPT-4, predictive analytics for inventory management, natural language understanding for chatbots, image recognition for product catalogs, and automated content generation.

Architecture Decisions: We use a multi-model strategy combining GPT-4 and Claude for complex reasoning, specialized models for specific tasks, and custom fine-tuned models for domain-specific needs. This optimizes for both cost and performance. We built a smart routing layer that analyzes requests, selects optimal models, routes to fastest endpoints, and falls back to alternatives if needed. Result: 40 percent cost reduction while maintaining quality.

Caching Strategy: AI inference is expensive. We implemented multi-layer caching with in-memory cache for identical requests, distributed Redis cache for similar requests, and semantic cache using vector similarity. Impact: 60 percent cache hit rate, saving thousands monthly. Want to learn more about our platform? Get in touch at /contact