I built Scale AI after seeing the same gap in AI SaaS products: teams could ship features quickly, but could not answer which tenant was profitable, why model spend changed, or whether a retry or cache decision protected margin. Scale AI sits between a tenant-aware SaaS and OpenAI-compatible providers. It meters successful calls, enforces hard and soft budgets, routes approved models, runs durable BullMQ jobs, manages versioned pgvector retrieval, and joins AI COGS to revenue. The hero view is contribution margin, payback, and COGS per tenant. The public demo uses synthetic tenants and a local mock provider. I would value feedback from SaaS founders and platform engineers on the tenant economics view, the migration path, and the operational controls you would need before putting an AI product in front of customers or investors. CodeCanyon gave you modules. Scale AI makes the system operable.

Scale AI

Know which AI tenants make money before costs scale

Know which AI tenants make money before costs scale