B2B AI Application Tech Stack: What Founders Need to Know

Building a B2B AI application involves navigating numerous infrastructure decisions, each influencing the other, from development environments to backend frameworks, databases, and AI model providers. These choices interconnect, forming a complex web where initial decisions can constrain future options, impacting scalability and vendor lock-in. Key components include selecting the right development environment and backend framework based on team expertise and project needs, choosing suitable agentic frameworks and frontend frameworks that support AI integration and scalability, and opting for databases that balance structured and unstructured data requirements. Authentication and identity management are crucial for enterprise readiness, requiring robust user management and multi-tenancy capabilities. Deployment platforms should offer scalability and ease of use, while observability and monitoring ensure application reliability. Evaluation tools are essential for assessing AI outputs, ensuring they meet business demands for accuracy and relevance. Throughout, the emphasis is on making informed, agile decisions that align with team strengths and customer needs, providing room for growth and adaptation as technology and requirements evolve.