As OpenAI models become deeply embedded in enterprise workflows, a key architectural concern is vendor concentration risk.

How should organizations design AI systems that:

• Maintain interoperability across multiple model providers

• Avoid lock-in at the API, fine-tuning, and orchestration layers

• Preserve evaluation consistency across different LLMs

• Manage governance, safety, and auditability in multi-model environments

• Control inference cost without degrading performance

Is the answer model abstraction layers, agent orchestration frameworks, open-weight fallbacks, or something else?

Looking for insights from those building production-scale AI systems.

We are a small firm in fintech. Currently have a financial/data analyst with years of experience, primarily working in Excel and demonstrating strong proficiency in it. As we look to expand their role and skill set for broader analytical responsibilities, we are evaluating the right data visualization tool to invest in for training and adoption. While Tableau was widely regarded as the industry standard a few years ago, current trends suggest Power BI may be becoming the preferred choice. Given our aim to integrate more robust data visualization and SQL capabilities into our analytics function, which platform would offer better long-term value and alignment with current market demand?