AI Vendor Evaluation Checklist (Free Template)
Score AI vendors across 6 categories with this structured checklist. Technical depth, production track record, and pricing transparency criteria included.
TL;DR
- Most AI vendor evaluations over-index on demos and under-index on production readiness, failure handling, and post-deployment support
- This checklist scores vendors across 6 categories: Technical Depth, Production Track Record, Data & Security, Pricing Transparency, Integration & Support, and Strategic Fit
- Each category has weighted scoring criteria so you can compare vendors quantitatively instead of relying on slide decks
- Download-ready format you can copy into your procurement process today
Choosing the wrong AI vendor is expensive. BCG’s 2025 research found that 74% of companies struggle to achieve and scale value from their AI initiatives. S&P Global’s 2025 survey puts it more starkly: 42% of organizations have abandoned most of their AI initiatives entirely. A structured vendor evaluation is the cheapest insurance against joining those numbers.
This checklist is built from patterns we see in enterprise AI engagements at Clarity. It covers what to ask, what to look for, and how to score responses so you can compare vendors apples-to-apples.
How to Use This Checklist
Each of the 6 categories contains 4-5 criteria. Score each criterion from 0 to 3:
- 0 — Missing: Vendor cannot address this area
- 1 — Weak: Vendor acknowledges the area but has no clear process or evidence
- 2 — Adequate: Vendor demonstrates competence with some evidence
- 3 — Strong: Vendor provides concrete examples, references, or documentation
Weight each category by importance to your organization (suggested weights included). Multiply category averages by weights to get a composite score.
Typical Vendor Evaluation
- ×Watch a 30-minute demo
- ×Compare hourly rates
- ×Ask for case studies (get marketing PDFs)
- ×Check if they use GPT-4 or Claude
- ×Decision based on sales relationship
Structured Vendor Evaluation
- ✓Score 6 categories with weighted criteria
- ✓Request production architecture diagrams
- ✓Ask for failure taxonomies and incident response logs
- ✓Evaluate data handling and security posture
- ✓Decision based on quantitative composite score
Category 1: Technical Depth (Weight: 25%)
This is where most evaluations start and stop. The difference between a useful technical evaluation and a waste of time is specificity — asking questions that cannot be answered with marketing copy.
Criteria
1.1 — Architecture Transparency Can the vendor explain their system architecture in technical detail? Ask for a diagram showing data flow from input to output, including preprocessing, model inference, post-processing, and feedback loops. Vendors who build production AI systems can draw this on a whiteboard in 10 minutes.
1.2 — Model Selection Rationale Why did they choose their specific model architecture? There is no universally correct answer here, but the reasoning should be specific to your use case. Watch for vendors who default to “we use GPT-4” without explaining why that model fits your constraints around latency, cost, data privacy, and accuracy requirements.
1.3 — Evaluation Framework How do they measure whether their AI is working? Ask for their evaluation metrics, test sets, and how they handle cases where ground truth is ambiguous. Vendors with production experience will have a nuanced answer about multi-dimensional evaluation. Vendors without it will mention accuracy and F1 scores.
1.4 — Failure Handling What happens when the model is wrong? Ask for their failure taxonomy — the specific categories of errors their system can produce — and how each category is detected and handled. This single question separates vendors who have shipped production AI from those who have shipped demos.
1.5 — Context and Personalization Architecture How does the system adapt to individual users or use cases over time? Look for approaches that go beyond basic prompt engineering. Understanding how a vendor handles user context, memory, and personalization reveals architectural maturity.
Category 2: Production Track Record (Weight: 20%)
Demos are easy. Production is hard. Gartner’s July 2024 research found that 30% of generative AI projects are abandoned after the proof-of-concept phase. This category separates vendors who have crossed that gap from those who have not.
Criteria
2.1 — Production Deployments How many production AI systems are they currently operating? Ask for the distinction between POCs, pilots, and production. Ask about scale: user counts, request volumes, uptime. Vague answers like “we’ve worked with Fortune 500 companies” without specifics are a signal.
2.2 — Time to Production What is their average timeline from kickoff to production deployment? Ask for at least two examples with actual timelines, including what caused delays if any. Credible answers include specific numbers and honest acknowledgment of setbacks.
2.3 — Post-Launch Support Model What happens after deployment? How long do they provide active support? What does their monitoring setup look like? Ask whether they have SLAs for model performance degradation and how they handle production incidents.
2.4 — Client References Can they provide references from clients with similar use cases? Not case study PDFs — actual conversations with technical leads who managed the engagement. Willingness to connect you with past clients is a strong positive signal.
Category 3: Data and Security (Weight: 20%)
AI vendors will have access to your data. This category assesses whether they treat that access with appropriate seriousness.
Criteria
3.1 — Data Handling Policies Where does your data go? Is it used for model training? Who has access? Ask for written data handling policies and verify they align with your compliance requirements (SOC 2, GDPR, HIPAA, or industry-specific regulations).
3.2 — Security Certifications What security certifications do they hold? SOC 2 Type II is the baseline for enterprise work. Ask for audit reports, not just certification badges.
3.3 — Data Isolation In multi-tenant architectures, how is your data isolated from other clients? Ask about encryption at rest and in transit, access controls, and data retention policies. This is especially important for vendors offering shared infrastructure.
3.4 — Model Security How do they prevent prompt injection, data leakage, and adversarial attacks? Vendors building production AI should be able to describe specific mitigation strategies, not just acknowledge the risks.
Category 4: Pricing Transparency (Weight: 15%)
AI consulting rates range from $150 to $500 per hour (OrientSoftware, 2024), and 73% of AI buyers now prefer fixed-fee pricing over hourly billing (Stack.expert, 2025). The pricing model matters as much as the price.
Criteria
4.1 — Pricing Model Clarity Is the pricing model clearly defined? Fixed-fee, hourly, outcome-based, or hybrid? Ask for a written breakdown that includes all costs: development, infrastructure, data processing, API calls, support, and ongoing maintenance.
4.2 — Scope Management How do they handle scope changes? AI projects routinely discover requirements during implementation that weren’t visible during scoping. Ask for their change order process and how they price additions.
4.3 — Infrastructure Costs Who pays for compute and API costs during development and after deployment? This is a common source of budget surprises. Get specific numbers based on your expected usage patterns.
4.4 — Total Cost of Ownership Can they provide a 12-month and 24-month total cost projection that includes development, deployment, maintenance, and infrastructure? Vendors who have done this before can provide realistic estimates. Vendors who have not will give you development costs only.
Category 5: Integration and Support (Weight: 10%)
The technical quality of the AI is irrelevant if it cannot integrate with your existing systems or if support disappears after the check clears.
Criteria
5.1 — Integration Approach How do they integrate with your existing tech stack? Ask for details about APIs, data pipelines, authentication, and any required changes to your infrastructure. Vendors should be able to describe integration patterns for your specific stack.
5.2 — Documentation Quality Ask to see their documentation for a current client integration (redacted as needed). The quality, completeness, and currency of documentation is a direct signal of engineering discipline.
5.3 — Support Response Times What are their SLAs for different severity levels? How do they handle critical production issues outside business hours? Ask for their on-call structure and escalation process.
5.4 — Knowledge Transfer How do they ensure your team can operate and iterate on the system after the engagement ends? Look for structured handoff processes, training sessions, and documentation that enables your team’s independence.
Category 6: Strategic Fit (Weight: 10%)
This category is subjective but important. A technically excellent vendor who does not understand your domain or cannot work within your organizational constraints will still fail.
Criteria
6.1 — Domain Understanding How well do they understand your industry and specific use case? Domain expertise reduces time to production because the team spends less time learning your business and more time building. Ask what they know about your vertical before telling them.
6.2 — Team Composition Who will actually work on your project? Ask for bios and experience of the specific engineers and leads assigned to your engagement — not the company’s most impressive team members who staff the sales process.
6.3 — Communication Style How do they communicate progress, blockers, and decisions? Ask about cadence (daily standups, weekly demos, async updates), tooling, and how they handle disagreements about technical direction.
6.4 — Long-Term Vision Alignment Where is the vendor headed strategically? If your use case is peripheral to their roadmap, you risk becoming a low-priority client when resources get tight. Understanding their product direction helps assess long-term partnership viability.
Scoring Template
Copy this table into a spreadsheet and fill it in for each vendor you evaluate.
| Category | Weight | Criteria | Score (0-3) | Notes |
|---|---|---|---|---|
| Technical Depth | 25% | Architecture Transparency | ||
| Model Selection Rationale | ||||
| Evaluation Framework | ||||
| Failure Handling | ||||
| Context & Personalization | ||||
| Production Track Record | 20% | Production Deployments | ||
| Time to Production | ||||
| Post-Launch Support | ||||
| Client References | ||||
| Data & Security | 20% | Data Handling Policies | ||
| Security Certifications | ||||
| Data Isolation | ||||
| Model Security | ||||
| Pricing Transparency | 15% | Pricing Model Clarity | ||
| Scope Management | ||||
| Infrastructure Costs | ||||
| Total Cost of Ownership | ||||
| Integration & Support | 10% | Integration Approach | ||
| Documentation Quality | ||||
| Support Response Times | ||||
| Knowledge Transfer | ||||
| Strategic Fit | 10% | Domain Understanding | ||
| Team Composition | ||||
| Communication Style | ||||
| Long-Term Alignment |
How to calculate the composite score:
- Average the criteria scores within each category (max 3.0)
- Multiply each category average by its weight
- Sum the weighted scores for a composite (max 3.0)
- Compare composites across vendors
A composite score below 1.5 is a red flag. Between 1.5 and 2.0 means the vendor has gaps you need to evaluate against your risk tolerance. Above 2.0 indicates a vendor with solid fundamentals.
Red Flags to Watch For
Regardless of scores, these signals should give you pause:
- No production references — Only POC or pilot deployments, no currently-operating production systems
- Vague pricing — Cannot provide a total cost projection beyond the initial build phase
- Resistance to technical questions — Redirects architecture questions to sales or marketing materials
- No failure taxonomy — Cannot describe specific ways their AI systems fail and how failures are detected
- Team bait-and-switch — The team presented during sales differs from the team assigned to your project
- No evaluation framework — Measures success by “client satisfaction” without quantitative metrics
What Clarity Scores on This Checklist
We built this checklist because we think vendor evaluation should be transparent. Here is how we would score ourselves, with honest acknowledgment of where we are still growing:
- Technical Depth: We publish our architectural approach and provide architecture diagrams during discovery. Our Sprint Zero process is designed to surface failure modes before development begins.
- Production Track Record: We are a focused practice. We share specific timelines and outcomes from past work, including what went wrong.
- Pricing Transparency: We use fixed-fee pricing at transparent rates because we believe hourly billing creates misaligned incentives.
If you want to run this evaluation against us, start a conversation. We will answer every question on this checklist.
References
- BCG. “From Potential to Profit: Closing the AI Impact Gap.” 2025.
- S&P Global Market Intelligence. “AI & Automation Trends Survey.” 2025.
- Gartner. “Generative AI Projects After POC.” July 2024.
- OrientSoftware. “AI Consulting Rates.” 2024.
- Stack.expert. “AI Buyer Preferences Survey.” 2025.
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