Start with the problem, target users, and business model before making technology decisions. Architecture, multi-tenancy, security, integrations, scalability, observability, and cloud costs should then be considered early to avoid expensive changes as the product grows.

For many new applications, yes. Cloud-native architectures can provide greater scalability, resilience, deployment flexibility, and access to managed services. However, the architecture should always reflect the application's actual requirements rather than adopting cloud technologies unnecessarily.

Modernization is often preferable when valuable parts of the existing application can be retained. A technical assessment can determine whether to refactor, re-platform, re-architect, selectively rebuild, or replace the application based on technical debt and future requirements.

Scalability starts with architecture. Decisions around tenant isolation, databases, APIs, asynchronous processing, caching, infrastructure, observability, and automated deployment all influence how efficiently a SaaS platform can accommodate more users and workloads.

AI can be introduced through capabilities such as intelligent search, copilots, content generation, workflow automation, recommendations, and agents. The right approach depends on the use case, available data, existing architecture, security requirements, and how AI needs to interact with other systems.

Model Context Protocol (MCP) provides a standardized way for AI applications and agents to connect with external tools, data, and systems. Custom MCP servers can make existing software capabilities securely accessible to AI without creating a separate integration approach for every interaction.