Cloud‑native AI SaaS succeeds when it combines elastic, multi‑tenant infrastructure with grounded intelligence and governed actions. Architect for stateless scale at the edge, identity‑aware retrieval, small‑first model routing, and typed tool‑calls behind policy gates—observed by SLOs and cost budgets. Use event‑driven patterns, strong tenancy isolation, and platform engineering to ship quickly without compromising privacy, reliability, … Read more
Great AI SaaS testing goes beyond unit tests. It continuously validates three things: 1) the product’s facts and payloads are correct (grounding and JSON/action validity), 2) actions are safe and compliant (policy, privacy, fairness), and 3) the system meets performance and cost SLOs in production. Build a layered test strategy: golden evals for content and … Read more
Scalability in AI SaaS means more than handling traffic. It means: grounding outputs in tenant data at low latency; routing requests across small and large models efficiently; executing typed actions safely in downstream systems; operating with clear SLOs, budgets, and auditability; and making the product economical to run as tenants, features, and regions grow. Focus … Read more