AI SaaS Product Development
From product idea to a billing, multi-tenant platform in production
We build AI SaaS products end to end: the application, the AI layer underneath it, the tenancy and permissions model, subscription billing, usage metering, and the infrastructure to run it. Not a prototype — a product you can sell access to.
- Tenant isolation
- MultiTenant isolation
- Usage billing
- MeteredUsage billing
- Grade infra
- ProdGrade infra
Typical stack
- Next.js
- NestJS
- Postgres
- Stripe
- Vercel
Why this keeps costing you
AI product prototypes are quick. Turning one into something you can charge for is not. Tenant isolation, per-customer usage limits, billing that reflects real consumption, cost control on model calls, and an evaluation process that stops quality regressing — that is where most AI products stall.
Inside an AI SaaS platform we ship
- Multi-tenant architecture with enforced data isolation per customer
- Subscription billing, plan limits, and usage-based metering via Stripe
- Authentication, roles, team management, and invitations
- The AI layer — retrieval, agents, or generation — engineered for cost and latency
- Admin tooling for support, impersonation, and plan management
- Deployment, observability, and CI so shipping is routine rather than an event
The part most implementations skip
We treat model cost and latency as product constraints from the first architectural decision, not as something to optimise after launch. Tenancy, metering, and evaluation get designed in at the start because retrofitting any of the three means a rewrite.
What you actually receive
- Product and technical architecture agreed before build starts
- Working application deployed to staging and production
- Billing integration with plans, trials, upgrades, and dunning
- Cost model showing unit economics per active customer
- Handover documentation and a runbook for your team or ours
AI SaaS Development in your sector
How ai saas development plays out in the industries we work with most.
Common Questions
- Can you take over an existing codebase?
- Yes. We start with an audit covering architecture, cost exposure, and the parts that will break under load, then agree what is worth keeping before touching anything.
- How do you keep model costs under control?
- Model routing so cheap requests do not hit expensive models, aggressive caching of repeated work, prompt and context budgets, and per-tenant usage caps. Costs are tracked per customer so you can see unit economics rather than one alarming monthly invoice.
- What does a typical build take?
- A focused first version with billing usually runs eight to fourteen weeks depending on the AI layer's complexity. We scope to a launchable slice first rather than building the full roadmap before anyone can pay you.
Often built alongside this
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