SERVICE
AI-Powered SaaS Development
Full-stack SaaS platforms with AI built in — shipped to production.
API endpoints in one shipped platform
Reviewed by Mohammed Affaan Khan, GenAI & Agentic AI Engineer · Updated July 2026
How much does saas builds cost? See what drives the price — and get a quote scoped to your budget.→What saas builds actually means
A SaaS platform is multi-tenant software where a single deployment serves many customers with strict isolation between them. That one property drives nearly every architectural decision that follows: schema design, authorisation, background work, billing, and how a deploy that breaks one tenant is prevented from breaking all of them.
The failure mode is rarely the features. It is the operational surface — onboarding, billing edge cases, permissions, support tooling and observability — which is invisible in a demo and is most of what determines whether the business is operable at a hundred customers.
What you get
- Multi-tenant architecture & RBAC
- Stripe billing with webhook lifecycles
- Real-time features (Socket.IO)
- Embedded AI agents & 9-language i18n
How we build it
Tenancy model
Shared schema, schema-per-tenant or database-per-tenant. Decided first, because it is the most expensive decision to reverse and it is driven by compliance and customer size, not preference.
Identity and authorisation
Users, organisations, roles and invitations, with permission checks enforced in one place rather than scattered across handlers.
Core product
The workflow the customer is actually buying, built in increments that ship behind flags.
Billing
Plans, trials, proration, dunning and the upgrade path. Rarely interesting and consistently underestimated.
Operations surface
Admin tooling, impersonation with an audit trail, and the support views that decide whether a small team can run this.
Observability
Per-tenant metrics and error tracking, so a problem affecting one customer is visible before they report it.
The stack
APPLICATION
TypeScript with Next.js, or Python with FastAPI where the product is API-first.
DATA
PostgreSQL with row-level security or schema isolation, and migrations that run safely against live tenants.
BACKGROUND WORK
Queues and scheduled jobs with per-tenant isolation, so one customer's heavy job does not starve everyone else.
BILLING
Stripe or a comparable provider, with webhook handling that is idempotent because deliveries repeat.
INFRASTRUCTURE
Containers with horizontal scaling, staged deploys and a rollback that works under pressure.
OBSERVABILITY
Structured logs, traces and per-tenant dashboards.
What it connects to
- Stripe, Paddle and billing providers
- SSO — SAML, OIDC and Google Workspace
- Webhooks and public APIs for your customers
- CRM and support desks
- Analytics and product telemetry
- Email and notification delivery
Where teams use it
Vertical SaaS
Software for one industry, where domain depth is the moat and the integrations are specific.
Internal platform to product
Turning a tool built for one operation into something sellable, which is mostly tenancy and billing work.
Marketplace platforms
Two-sided products with onboarding, payouts and trust mechanics.
API products
Where the API is the product, so keys, quotas, docs and versioning are the surface.
AI-native SaaS
Products where inference cost is a real line item and has to be metered per tenant.
How a build runs
Architecture
1–2 weeks
Tenancy model, auth design and the deployment shape.
Foundation
3–4 weeks
Auth, tenancy, billing skeleton and CI running end to end.
Product build
2–5 months
The core workflow, shipped in increments.
Operations
3–4 weeks
Admin tooling, observability and support surfaces.
Scale
ongoing
Performance, new plans and enterprise requirements as they arrive.
When this is the wrong answer
If you have one customer, you have custom software rather than SaaS. Building multi-tenancy before the second customer exists is a common and expensive way to be slow.
The tenancy decision is the one to get right early. Migrating from shared schema to per-tenant databases under load is among the more painful projects in this category.
Enterprise requirements — SSO, audit logs, data residency, security review — arrive later and are substantial. Worth designing space for, not worth building before anyone asks.
Inference cost in an AI-native product is a per-tenant variable. Without metering, one heavy customer can quietly consume the margin from twenty others.
Proof
Multi-tenant restaurant SaaS: 132 endpoints, 9 languages
Complete restaurant operating system — POS, kitchen displays, QR ordering, inventory, reservations, analytics — with an AI booking agent.
Frequently asked questions
Can you build our entire product?
Yes — we have shipped complete platforms (POS, kitchen displays, QR ordering, inventory, analytics) spanning 132 API endpoints and 53 frontend pages.
Do you handle payments and subscriptions?
We implement Stripe subscription billing with full webhook lifecycle management, trials, upgrades and dunning.
Can AI be part of the product, not a bolt-on?
That is our specialty: conversational booking/ordering agents, AI analytics and automation woven into the core product experience.
How do you handle multi-tenancy?
Shared schema with row-level security, schema-per-tenant, or database-per-tenant, decided in the first fortnight because it is the most expensive decision to reverse. The choice follows your compliance requirements and customer size rather than a default preference.
What about enterprise requirements like SSO and audit logs?
They arrive later and they are substantial. We design space for them without building them before anyone asks — SAML and OIDC, audit trails, data residency and the security questionnaire that accompanies the first large customer.
Can you take over an existing SaaS product?
Frequently. The first week is reading and shipping something small rather than proposing an architecture. A rewrite is rarely the right answer and never the cheap one.
How do you handle billing?
Stripe or a comparable provider, with idempotent webhook handling because deliveries repeat. Plans, trials, proration, dunning and the upgrade path are unglamorous and consistently underestimated, so they are scoped as real work.
What if inference costs get out of control?
In an AI-native product, metering per tenant is a design requirement rather than an optimisation. Without it, one heavy customer quietly consumes the margin from twenty others and the reporting shows nothing until renewal.
When should we build multi-tenancy?
When the second customer is real. Building it for a customer that does not yet exist is a common and expensive way to be slow — with one customer, you have custom software, and that is fine.
Can our own team maintain it after handover?
That is the intended end state. We use standard, widely-known technology rather than anything clever, document decisions as they are made rather than at the end, and run handover sessions with your engineers. If a system can only be maintained by us, we have built it wrong.
Who owns the code and the models?
You do, from the first commit. Work happens in your repository under your licence and the contract assigns IP outright. We keep no rights, hold no keys you cannot rotate, and build nothing proprietary that makes leaving expensive.
What happens to our data?
It stays in infrastructure you control or a region you nominate. We do not train shared models on your data, and where a third-party model provider is involved we tell you which, what it receives, and what its retention terms are — before anything is sent.
SaaS Builds by industry
What this is built on
The engineering disciplines behind saas builds, each with its own scope and constraints.
SaaS Builds near you
North America
United Kingdom
Europe
Asia-Pacific
Latin America
Before you choose anyone
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