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Evaluate AI vendors with a rigorous framework covering capability, reliability, cost, and trust.
Read through the lesson, mark it complete when the concept is clear, then move to the next lesson in the sequence or jump back to the module map.
AI vendor marketing is exceptionally good. Every vendor has impressive demos, case studies, and benchmark results. Separating real capability from marketing is challenging and important.
A structured evaluation process protects you from buying the wrong thing.
Capability — Can it actually do what you need? Test on your real use cases with your real data, not their curated demos. Performance on your data can differ dramatically from benchmark performance.
Reliability — What's the uptime SLA? What's the latency? What happens during failures? Get historical incident data, not just SLA documents.
Security and compliance — Where does your data go? Who can access it? How long is it retained? What certifications does the vendor hold (SOC2, ISO 27001, HIPAA)?
Cost — What's the pricing model? How does cost scale with volume? What are the overage charges? Get quotes for 3x your projected volume.
Support and partnership — What level of support is included? How responsive are they? Are they a vendor or a partner?
Always run a time-boxed PoC (2-4 weeks) before signing an annual contract. Define clear evaluation criteria before the PoC starts — don't evaluate after the fact.
Before evaluating vendors, decide: should we build this capability ourselves? Building gives control, customization, and no vendor dependency. Buying gives speed, maintained infrastructure, and specialized expertise.
Rule of thumb: buy for commodity capabilities (email, payments, authentication), build for differentiated capabilities (proprietary models, specialized data).