An AI SaaS has a cost structure that traditional SaaS does not. Every active user consumes tokens, so gross margin depends on engineering decisions — caching, model selection, context discipline — in a way that server costs never did. Products that ignore this reach product-market fit and discover their best customers are their least profitable.

Who this is for

Founders building a product where AI is the core value rather than a feature. Existing SaaS businesses adding AI capability to an established platform. Teams whose AI product works but whose margin does not.

Problems we solve

Inverted unit economics. Heavy users costing more in inference than they pay in subscription.
No usage metering. Consumption-based pricing impossible because consumption is not measured per tenant.
Tenant leakage. Shared vector indexes where one customer's documents can surface in another's results.
Noisy neighbours. One tenant's bulk job exhausting the rate limit for everyone.
Unbounded abuse. Free tiers with no ceiling, discovered through the invoice.

What we build

Multi-tenant architecture with strict data isolation, including per-tenant vector namespaces
Token metering per tenant, per feature, exposed to both billing and the customer
Subscription and usage-based billing with Stripe, including quotas and overage handling
Per-tenant rate limiting and fair-use queueing
Caching layers that cut inference cost without cutting perceived quality
Roles, permissions, team accounts and audit logging
Admin tooling for support staff to inspect usage and diagnose tenant issues

How we work

We model unit economics before writing the application: expected tokens per action, per user, per plan, and what that implies for pricing. That number shapes the architecture — where caching goes, which model handles which step, what the free tier can safely allow. Tenant isolation is designed in from the first migration, because retrofitting it into a live product is among the most expensive corrections available.

Technologies we use

Laravel or Node.js on the backend, Next.js and React on the front end, PostgreSQL or MySQL with Redis for caching, queues and rate limiting. Qdrant or pgvector with per-tenant namespaces. OpenAI, Claude, Gemini or Ollama behind a provider abstraction. Stripe for billing, Docker and CI/CD for deployment.

Business benefits

Pricing grounded in measured cost per customer rather than a guess
Margin that holds as usage grows instead of eroding
Isolation strong enough to answer enterprise security questionnaires
Usage visibility that supports upgrade conversations with evidence

Where it pays off

Launching an AI product with tiered or usage-based pricing
Adding AI features to an existing SaaS without breaking its margin
Fixing an AI product whose costs scale faster than its revenue
Preparing a product for enterprise buyers with isolation requirements

Common questions

How should we price an AI product?

Measure first. We instrument token consumption per action during the build so pricing is set against real numbers. Flat-rate pricing works when usage is predictable; where it is not, usage tiers with a generous included allowance tend to hold up better.

Can tenants share a vector database?

Only with enforced namespace or payload isolation and filtering applied at query time. We treat cross-tenant retrieval as a security boundary and test it as one.

How do we stop a free tier being abused?

Hard quotas, per-tenant rate limits, and cost ceilings that degrade the feature rather than the invoice. Verification requirements on signup where the risk warrants it.

Building an AI product? Get the unit economics right before the architecture sets.

AI-Powered SaaS Development