1. How Does This Scale Beyond a Demo?
Every demo looks great with a handful of users. The real test is scaling to production. Ask vendors to move beyond buzzwords and detail the specific architectural patterns for handling massive request volumes. What does the reference architecture for a high-throughput,
low-latency system actually look like? Inquire about their solutions for multi-region deployments, traffic shaping, and how the system handles 'noisy neighbor' problems in a multi-tenant environment. A vague answer here often means their elegant demo is a monolith that won't survive first contact with a real-world workload.
2. What’s the Total Cost of Ownership, Really?
The license fee is just the start. The total cost of ownership (TCO) for an AI platform includes often-hidden expenses. Ask for a detailed breakdown of costs related to data ingress and egress, API call overages, data storage, and model retraining. What are the unit economics? Inquire about the operational overhead: how many engineers are needed to maintain and govern this system? Many enterprise AI projects fail to show ROI not because the tech doesn't work, but because the ancillary costs were never accounted for.
3. Where Does My Data Go and Who Governs It?
Data is the lifeblood of AI, and its governance is non-negotiable. Ask vendors for specifics on data residency and sovereignty. Can they guarantee your data will be processed and stored in specific geographic regions to comply with regulations like GDPR? Crucially, ask if your proprietary data—prompts, inputs, and outputs—is ever used to train their models for other customers. A trustworthy platform provides contractual assurances against this and offers robust tools for managing data lineage and access controls, rather than just policy documents.
4. How Do I Avoid Vendor Lock-In?
Today’s cutting-edge platform can become tomorrow’s legacy burden. A key architectural question is about interoperability and exit strategies. How easily can you import open-source models or migrate models trained on their platform to another environment? Are the APIs and tool schemas proprietary, or do they adhere to open standards? A resilient architecture allows you to swap out components, including the core models, without having to rewrite the entire application. Ask them to map out the 'exit readiness' plan; if they don't have one, that's a major red flag.
5. What Does MLOps Look Like in Your World?
A model is only as good as its performance in production, which degrades over time. This is where MLOps (Machine Learning Operations) becomes critical. Go beyond their claims of 'built-in monitoring' and ask for details. What specific tools are provided for detecting model drift, data drift, and performance degradation in real time? How does the platform connect evaluation with production telemetry to create a feedback loop? A mature platform will offer automated workflows for triggering retraining and provide a clear audit trail for every prediction and decision.
6. How Is This Secured Against AI-Specific Threats?
Securing an AI system is more complex than securing traditional software. You need to ask about AI-specific vulnerabilities. How do they protect against prompt injection, data poisoning, and adversarial attacks? As AI agents gain the ability to act on behalf of users, how is identity propagated and controlled? Microsoft's focus on agentic security with platforms like Microsoft Entra highlights this growing concern. The vendor should be able to provide clear architecture diagrams and operational proof of their security measures, not just a list of compliance certifications.
7. Where Does the ‘Managed Service’ End and the ‘Black Box’ Begin?
Managed services and platforms like Azure AI are great for accelerating development, but they can also obscure critical details. The final, crucial question is about control and transparency. At what point does the abstraction become a 'black box' that hinders debugging or customization? How much control do you have over the underlying infrastructure for fine-tuning performance or cost? While you may not need to manage everything, you need the ability to inspect and understand why the system is behaving a certain way, especially when it fails. A good partner provides observability, even within a managed environment.
