Buying AI is different from buying traditional software. The evaluation process requires understanding factors that don't apply to conventional tools.
Technical Evaluation
Model Performance: What accuracy can you expect? On what benchmarks?
Data Requirements: What data do they need? How much? What format?
Integration: How does it connect to your systems?
Scalability: Can it handle your volume? What about 10x growth?
Customization: Can you adapt it to your specific needs?
Vendor Viability
Financial Health: AI startups fail frequently. Can they survive?
Team Quality: Who built this? What's their track record?
Customer Base: Who else uses them? Can you talk to references?
Roadmap: Where are they headed? Does it align with your needs?
Operational Considerations
Support: What help is available? What's the SLA?
Training: How do you learn to use it effectively?
Maintenance: Who handles updates, retraining, fixes?
Security: How is data protected? What certifications do they have?
Pricing Deep Dive
AI pricing can be complex:
- Per-query vs subscription
- Training costs vs inference costs
- Data storage fees
- Implementation services
Get total cost of ownership, not just license fees.
Proof of Concept
Never buy without a POC:
- Use your real data
- Test real use cases
- Measure real metrics
- Include real users
Decision Framework
Weight factors by importance for your situation. Don't let impressive demos override fundamental concerns.