Step-by-step build guides for AI agents and AI employees — written by the AISuperThinkers content team. Start with a supporting guide, work up to the full playbook.
Embedding spend scales with data volume, not request volume — and it compounds silently. A builder's guide to the four levers that cut vector database cost without degrading recall: dimension reduction, quantization, index selection, and tiered storage.
Hybrid model routing sends each task to the cheapest model that can handle it, cutting inference spend by 40–70% on mixed workloads. This guide covers routing architecture, cost math, and the failure modes that eat the savings — for builders who want control, not hand-holding.
Production-grade PII redaction for AI agents: layered detection, reversible tokenization with an in-memory restore map, and where redaction quietly fails across multi-hop workflows.
