SaaS has to work as a business as well as software.

There is no single tenancy model that fits every SaaS product. Shared tables can reduce infrastructure cost but require rigorous tenant-aware access controls. Separate schemas or databases provide stronger isolation but increase operational overhead. The right choice depends on customer requirements, data sensitivity, compliance, expected scale and unit economics.

Not necessarily. A well-structured modular application can be easier and faster to evolve while the product is still finding its shape. Microservices become more compelling when independent deployment, team boundaries, scaling characteristics or domain complexity justify the additional operational overhead. Architecture should follow the product's actual growth requirements.

Yes. The work can involve more than moving the application to cloud infrastructure. Tenancy, identity, data isolation, billing, customer administration, deployment, monitoring and security may all need to change. We first assess what can be retained and what needs restructuring, then create a migration path that avoids unnecessary disruption.

Enterprise readiness can affect architecture well before the first enterprise contract. Requirements may include SSO, granular permissions, audit trails, data residency, security reviews, customer-specific configuration and enterprise integrations. We identify the requirements that matter to your target customers and design the product so they can be supported without creating a separate product for every account.

Pricing and architecture are connected when the product supports plans, usage limits, feature entitlements or metered billing. The platform needs reliable ways to identify usage and enforce what each customer has purchased. We design these rules so the commercial model can change without forcing widespread changes across the application.

We look at architecture and usage together. Database design, caching, background processing, storage, data transfer, autoscaling and third-party services can all affect cost. For AI-enabled SaaS, model and inference costs add another variable. The objective is to understand the cost of serving customers and make infrastructure choices that support healthy product economics.

Yes. AI can support search, recommendations, document processing, assistants, agents or workflow automation. The engineering work also needs to address context, data access, permissions, model evaluation, latency, reliability and inference cost. AI should fit into the existing product and architecture rather than become a disconnected feature.

Launch starts the next stage of product engineering. Real customers reveal new requirements, usage patterns expose architectural constraints and enterprise customers introduce new integration or security needs. We can continue as an engineering partner, improving the product, addressing technical constraints and evolving the platform as the business grows.