SaaS development — built to scale from day one.

SaaS development builds a multi-tenant product that serves many customers simultaneously from a single codebase with subscription billing and self-service onboarding; custom software development builds a bespoke single-tenant application for one organisation’s specific internal workflow. There are four technical differences. Multi-tenancy: SaaS serves thousands of customers from one deployment, custom software serves one organisation. Billing: SaaS requires subscription management, dunning, and metering, custom software uses one-time or annual licence pricing. Onboarding: SaaS users sign up and start without human involvement, custom software is deployed by the development team. Scalability: SaaS scales with tenant count, custom software scales with one organisation’s usage. Custom software typically costs less upfront but produces no recurring revenue; SaaS costs more upfront but produces compounding MRR.

The most proven SaaS stack is Next.js or React with TypeScript for the frontend, FastAPI (Python) or Node.js for the backend, PostgreSQL with row-level security for multi-tenant data, Redis for caching, Stripe for billing, Auth0 or Clerk for authentication, and AWS or GCP for cloud infrastructure. Selection criteria are talent pool size (popular stacks hire faster and more cheaply), SaaS-specific library support (Stripe SDKs, Auth0 integrations, and ORM support for RLS are mature in the Python and JavaScript ecosystems), and performance characteristics (FastAPI handles high-throughput async workloads; Node.js handles real-time WebSocket connections efficiently). The wrong reason to choose a stack is novelty — a SaaS product’s competitive advantage comes from product decisions, not the programming language it runs on.

Tenant data security is enforced at three layers: PostgreSQL RLS policies that filter every query to the authenticated tenant’s rows before results return, API middleware that extracts the tenant ID from the JWT and sets the database session variable before any query executes, and automated cross-tenant isolation tests that attempt to read Tenant A’s data while authenticated as Tenant B — running on every deployment. Frontend-only data isolation is not security; all isolation must be enforced server-side. For regulated industries where data co-location is itself a compliance issue, database-per-tenant architecture makes cross-tenant access architecturally impossible rather than policy-enforced. SOC 2 Type II certification requires documented, audited evidence of tenant isolation, so our audit log infrastructure records every data access event with actor ID, tenant ID, and timestamp.

Yes. We migrate legacy desktop applications, single-tenant web apps, and on-premise installations to multi-tenant SaaS — covering architecture redesign, database restructuring (adding tenant_id columns, implementing RLS, moving from a single schema to a multi-tenant data model), cloud environment setup, Stripe billing integration, and user data migration with zero-downtime deployment. The process: audit the existing codebase for architectural assumptions that conflict with multi-tenancy, design the multi-tenant target architecture, implement it in parallel with the existing system, migrate data with dual-write during the transition, and cut over with a tested rollback plan. SaaS migrations typically take 12–20 weeks for mid-size applications and 20–36 weeks for large enterprise applications with complex data models.

Usage-based billing charges customers based on consumption — API calls, active users, data processed, or events triggered — implemented through a dedicated metering service that records usage events in real time and aggregates them into Stripe billable items before each invoice cycle. Every billable event is written to a usage ledger with customer ID, event type, quantity, and timestamp. At the end of the billing cycle, the metering service aggregates events per customer and sends usage records to Stripe before invoices generate. Without a dedicated metering service, recording usage inside application code leads to missed events during errors, inaccurate billing, and an inability to add new billable dimensions without application changes. Usage-based billing increases ARPU as customers grow — revenue scales with customer success without sales intervention.

SOC 2 (System and Organisation Controls 2) is a security certification framework verifying that a SaaS company has implemented controls for security, availability, processing integrity, confidentiality, and privacy — required by enterprise procurement teams as a condition of purchasing B2B SaaS that handles sensitive data. SOC 2 Type I certifies that controls are designed correctly at a point in time; Type II certifies that they operated effectively over a 6–12 month audit period. SOC 2 becomes practically required when selling to enterprise customers in regulated industries — financial services, healthcare, legal, and government — who include it in vendor security questionnaires. Building SOC 2-ready architecture from the start (audit logs with actor and tenant ID, encryption at rest and in transit, least-privilege RBAC, vulnerability management, and incident response procedures) costs significantly less than retrofitting those controls later.

Directly. SaaS products gain GEO (Generative Engine Optimisation) and AEO (Answer Engine Optimisation) visibility by establishing a specific brand entity definition, building topical authority content that answers buyer evaluation questions, and structuring comparison and use-case pages that AI engines extract and cite in software recommendation responses. Buyers use ChatGPT and Perplexity to evaluate SaaS products before visiting vendor websites — asking questions like “what is the best SaaS project management tool for remote engineering teams.” Products cited in those answers receive qualified traffic because the buyer arrives having already evaluated alternatives. Products with specific positioning (“project management for distributed engineering teams”) receive more AI citations than products with generic positioning (“business management software”), because AI engines match specific buyer queries to specific product claims.