How to Evaluate AI Vendors: A Procurement Checklist

The AI vendor market is flooded. Every SaaS product has added "AI" to its marketing. Separating genuine AI capability from marketing hype requires structured evaluation. Here's the checklist. For development partners specifically, see how to choose an AI development partner.

Gartner predicted in 2024 that 30% of generative AI projects will be abandoned after proof of concept - due to poor data quality, escalating costs, or unclear business value. Picking the wrong vendor is one of the fastest paths to that outcome.

Dimension 1: Genuine AI Capability

Questions to Ask

• "What specific AI/ML models power your product?"
• "What happens if I send the same request twice - do I get the same result?"
• "How does your AI improve over time? With whose data?"
• "What's the latency for AI-powered features?"

What to Verify

Is it actually AI? Some products labeled "AI" are rule-based systems or simple keyword matching. Ask for technical details about the model architecture.

Is it their AI? Many vendors wrap OpenAI or Anthropic APIs with minimal customization. This isn't necessarily bad, but you should know what you're paying for. A thin wrapper over GPT-4 shouldn't cost enterprise-tier pricing.

Can they explain the limitations? Honest vendors tell you where their AI fails. Dishonest ones claim it works perfectly. Ask: "In what scenarios does your AI produce incorrect results?"

Red Flags

• Can't name the underlying model or approach
• Claims 99%+ accuracy without context
• "Our proprietary AI" with no technical details
• Demo only works with pre-selected data

Dimension 2: Accuracy and Reliability

Questions to Ask

• "What's your accuracy rate for [your specific use case]?"
• "Can we run a POC with our data?"
• "How do you measure and report accuracy?"
• "What's your uptime SLA for AI features?"

What to Verify

Test with your data. Demo accuracy with curated data is meaningless. The only valid accuracy test uses your real data with your real edge cases. According to Gartner's 2025 data readiness report, 60% of AI projects will fail through 2026 because organizations lack AI-ready data - which means your real data is the only honest test of whether a vendor's system will actually work in your environment.

Understand the failure modes. When the AI is wrong, what happens? Does it fail silently (worst case), flag uncertainty, or escalate? The failure mode matters more than the accuracy rate.

Check for hallucination handling. Does the vendor have mechanisms to detect and prevent AI hallucinations? This is especially critical for customer-facing or compliance-sensitive applications.

Red Flags

• Refuses to do a POC with your data
• Accuracy claims without methodology
• No explanation of failure handling
• "The AI never makes mistakes"

Dimension 3: Security and Compliance

Questions to Ask

• "Where is my data processed and stored?"
• "Is my data used to train your models?"
• "What compliance certifications do you hold?"
• "Can I get a SOC 2 Type II report?"
• "What happens to my data if I cancel?"

What to Verify

Data processing location. For regulated industries or GDPR compliance, data residency matters. Know where your data goes - including which LLM provider processes it.

Training data usage. Some vendors use customer data to improve their models. This might violate your data policies. Get explicit contractual guarantees about data usage.

Model access controls. Who at the vendor can see your data? What are their access controls? How are audit logs maintained?

Critical Checklist

• SOC 2 Type II certification (or clear timeline)
• Data Processing Agreement (DPA) available
• GDPR compliance documentation
• Data residency options
• Customer data not used for model training (contractual guarantee)
• Encryption at rest and in transit
• Access control documentation
• Incident response plan
• Data deletion procedures

Dimension 4: Pricing Transparency

Questions to Ask

• "What's the total cost for [your expected usage volume]?"
• "Are there per-token, per-query, or per-user fees?"
• "What happens if usage spikes?"
• "What costs extra beyond the base subscription?"

What to Verify

Understand the pricing model. AI products often have usage-based pricing that's hard to predict. A $500/month subscription that adds $0.05 per AI query at 100,000 queries/month is actually $5,500/month.

Model the worst case. Calculate cost at 3x your expected usage. If that number is unacceptable, the pricing model is a risk.

Ask about hidden costs. Implementation fees, training fees, premium support, additional integrations, data storage - these can double the advertised price.

Red Flags

• Pricing page says "Contact sales" with no ranges
• Per-token pricing without usage monitoring tools
• Separate charges for features that should be included
• Annual contracts with auto-renewal and no usage adjustments

Dimension 5: Integration Quality

Questions to Ask

• "Do you have a REST API? What's the documentation quality?"
• "What authentication methods do you support?"
• "How do webhooks and real-time notifications work?"
• "Can we see your API documentation before buying?"

What to Verify

API-first design. If the vendor only has a UI and no API, you're locked into their interface. API access is essential for integrating AI capabilities into your own product.

Documentation quality. Read the API docs before buying. Poor documentation means painful integration. If the docs don't exist, the API isn't ready.

Sandbox environment. Can you test the integration without production risk? A sandbox is essential for development and ongoing testing.

Integration Checklist

• REST API available
• API documentation is complete and accurate
• Sandbox / test environment provided
• Webhook support for real-time events
• SSO / SAML integration
• Rate limiting is documented and reasonable
• SDKs available for your tech stack
• API versioning policy (won't break your integration)

The POC Test

"We've had clients come to us after a vendor POC that used clean, pre-filtered demo data - and then their system fell apart in week one of production because real data is messy. Always insist on running the POC with your actual data, including your ugliest edge cases." - 1Raft Engineering Team

The single most valuable evaluation step is a proof-of-concept with your real data. A proper POC should:

1. Use your data, not the vendor's demo data
2. Cover your most common use cases AND your known edge cases
3. Run for at least 2 weeks to capture variability
4. Measure accuracy against a human baseline
5. Test the integration (not just the AI accuracy)

Vendors that refuse a POC or insist on using demo data are hiding something. A confident vendor welcomes the test.

Making the Decision

Score each vendor across the five dimensions. Weight them based on your priorities:

• If you're in a regulated industry, weight security/compliance highest
• If accuracy is critical (healthcare, finance), weight accuracy/reliability highest
• If you're integrating into an existing product, weight integration quality highest
• If budget is constrained, weight pricing transparency highest

The best AI vendor is rarely the flashiest. It's the one that's honest about limitations, transparent about pricing, and confident enough to let you test with your own data.

For a broader view of the market, see our best AI development companies comparison. At 1Raft, we welcome structured evaluation: project-based pricing with no hidden fees, production case studies with measurable outcomes from 100+ shipped products, and references from clients across healthcare, fintech, and commerce. Talk to our team to start your evaluation.