AI-native SaaS, engineered for the long haul.
Build a SaaS product where AI is the core value — not a sprinkled-on feature. Multi-tenant architecture, sustainable unit economics, defensibility against the next model release.
AI-native SaaS — or a SaaS with AI features?
Both are valid. Different architectures, different unit economics, different defensibility. This page is about the first.
| AI-native SaaS | SaaS with AI features | |
|---|---|---|
| Where value lives | The AI is the product | AI augments an existing product |
| What users pay for | The AI's outputs | The product, with AI inside |
| Cost structure | Variable per-token dominates | Fixed infra + occasional API spend |
| Defensibility risk | “OpenAI just released that” | Lower — value is in the workflow |
| Build complexity | Higher — AI infra is core | Lower — AI is an add-on |
Four things every AI SaaS has to solve.
Cost-per-user math that holds at scale
We build per-tenant cost modeling into the architecture from day one — cost ceilings, model routing by tier, dashboards your finance team can read.
Multi-tenant model isolation
Tenant A's prompts, caching, and fine-tuned adapters never bleed into Tenant B's. Boring engineering solutions to non-obvious failure modes.
Defensibility past the model
The frontier model today is commodity in 18 months. We help design where your moat actually lives — data, workflow, integrations, brand, distribution.
Eval as a product surface
Your evals double as marketing: 'here's why our product is more accurate than the same prompt to GPT-4 directly.'
Three multi-tenancy patterns we deploy.
Shared everything
All tenants share model + infra, isolated at the app layer. For freemium and early-stage products.
Shared infra, isolated data
Per-tenant vector namespaces, prompts, adapters, evals. The production default for mid-market + enterprise.
Dedicated stack per tenant
Per-tenant models and infrastructure for the enterprise tier. Works at high ACV.
How we engage on AI SaaS projects.
- Defensibility model + unit economics
- Working wedge feature
- Pricing-model design
- Full multi-tenant architecture
- AI gateway + per-tenant evals
- Billing, admin, observability, GA rollout
- 2–4 senior AI engineers embedded
- Delivery alongside your product team
- Model migrations + cost optimization
Common questions.
Should I start with the AI feature or the SaaS foundation?
How do I price AI SaaS without losing money?
What if OpenAI launches the same thing?
Do I need a private LLM for SaaS?
Will I own the code and data?
Tell us what you want to ship.
Bring the wedge idea, the existing product, or the napkin sketch. We'll talk through what an AI SaaS build actually looks like — wedge sprint, full build, or embedded team.