· NERVICO · artificial-intelligence · 10 min read
AI Vendor Evaluation: Complete Checklist for Businesses
Exhaustive checklist for evaluating AI vendors: technical, commercial, security, and operational criteria with a scoring framework for informed decision-making.
The market for enterprise AI tools has gone from a handful of established options to hundreds of vendors competing in every category. Each promises to transform your business. Each has a flawless pitch. And each has limitations they will not mention until you sign the contract.
According to Gartner, 30% of generative AI projects in enterprises will be abandoned after the proof-of-concept phase before the end of 2025, partly due to incorrect vendor selection. Not because the technology fails, but because the chosen vendor does not fit the company’s actual needs.
This article presents a complete evaluation framework with specific criteria, questions to ask each vendor, and a scoring system that enables objective comparison. This is not a generic guide. It is the process we use internally to evaluate technology before recommending it to our clients.
Why AI Vendor Evaluation Is Different
Evaluating an AI vendor is not like evaluating conventional SaaS. There are fundamental differences:
Technology changes every quarter. The vendor leading today may be irrelevant in 6 months. Models improve constantly, prices drop, and new competitors appear every week. Your evaluation must consider not just the current state but the vendor’s ability to adapt.
Results depend on your data. A model that works perfectly with demo data may fail with yours. Evaluation must include tests with your company’s real data.
Lock-in is real and costly. Migrating from one AI vendor to another is not like swapping one SaaS for another. It means retraining models, rewriting integrations, rebuilding knowledge bases, and in many cases, losing months of optimization.
Security has new implications. When a language model accesses your internal data, the questions about privacy, compliance, and security are different and more complex than with traditional SaaS.
The Evaluation Framework: Seven Dimensions
Dimension 1: Technical Capability
This dimension evaluates whether the tool can do what it promises at a technical level.
Evaluation criteria:
| Criterion | Key question | How to verify |
|---|---|---|
| Model quality | What accuracy does it achieve on your use case? | Test with 100+ real cases from your company |
| Latency | How long does it take to respond? | Measure times under real conditions, not in a demo |
| Scalability | Does it work at your expected volume? | Load test at 3x current volume |
| Customization | Can you adapt the model to your domain? | Verify fine-tuning, RAG, custom prompts |
| Multilingual | Does it work in the languages you need? | Test in all required languages, not just English |
| Multimodal | Does it process the formats you need? | Test with real documents (PDF, images, audio) |
Questions for the vendor:
- What base model do you use and how often do you update it.
- Can I bring my own model or am I limited to yours.
- How do you handle hallucinations and what error rate do you document.
- What is your guaranteed uptime and what SLA do you offer on latency.
- What happens to service quality during demand spikes.
Dimension 2: Security and Compliance
The most important and the most frequently underestimated.
Evaluation criteria:
| Criterion | Key question | How to verify |
|---|---|---|
| Data residency | Where is your data processed and stored? | Request infrastructure documentation and regions |
| Certifications | What security certifications does it have? | Verify SOC 2, ISO 27001, GDPR compliance |
| Privacy | Is your data used to train models? | Read the ToS carefully; do not rely on verbal assurances |
| Access control | Who can see which data? | Review the permissions and roles model |
| Auditability | Can every interaction be audited? | Verify complete logs with timestamps |
| Retention | How long do they retain your data and can you delete it? | Request the retention policy in writing |
Questions for the vendor:
- Is my data ever used to train or improve your models?
- Can I require that my data be processed exclusively within the EU (or other specific region)?
- What happens to my data if I cancel the contract?
- Do you have a standard DPA (Data Processing Agreement)?
- Have you had any security incidents in the last 24 months?
- Who within your organization has access to customer data?
Dimension 3: Integration and Architecture
How the tool connects with your existing infrastructure.
Evaluation criteria:
| Criterion | Key question | How to verify |
|---|---|---|
| APIs | Does it have documented, stable APIs? | Review the API documentation |
| Native integrations | Does it connect with your current tools? | List your tools and verify compatibility |
| Webhooks/events | Can it react to events in real time? | Test the integration with a simple case |
| SDK | Does it have SDKs for your tech stack? | Verify support for your languages and frameworks |
| Export | Can you export your data and configurations? | Test a full export during the evaluation |
| On-premise | Can it be deployed on your infrastructure? | If required, verify it is a real option |
Questions for the vendor:
- How often do you change the API and how do you manage versioning?
- What is the average integration time for a company our size?
- If we want to migrate to another provider, what data and configurations can we take?
- What rate limits does the API have and how do you handle traffic spikes?
Dimension 4: Pricing Model
The real cost of an AI tool is rarely the license price.
Evaluation criteria:
| Criterion | Key question | How to verify |
|---|---|---|
| Billing model | Per user, per usage, per volume? | Request the complete pricing structure |
| Hidden costs | Are there setup, training, or premium support fees? | Request a detailed breakdown |
| Cost scaling | How does price change as you grow? | Calculate cost at 2x and 5x your current volume |
| Commitment | Does it require annual or monthly contract? | Negotiate flexibility in the first months |
| Overages | Are there penalties for exceeding usage? | Read the contract’s fine print |
Questions for the vendor:
- What is the total cost at our current volume and at 3x that volume.
- Are there additional costs for technical support, custom integrations, or training?
- What happens if we exceed the contracted volume?
- Do you offer a trial period with real data before signing?
- How have your prices evolved over the last 12 months?
Dimension 5: Support and Customer Success
What happens after signing the contract.
Evaluation criteria:
| Criterion | Key question | How to verify |
|---|---|---|
| Onboarding | Does it include implementation support? | Request the detailed onboarding plan |
| Response times | How quickly do they respond to incidents? | Verify SLAs in writing |
| Support channels | How do you reach them when there is a problem? | Test support during the evaluation |
| Documentation | Is technical documentation current? | Review the documentation and compare with the product |
| Community | Is there an active user community? | Search for forums, Slack, or Discord for the product |
| Account manager | Do you have a dedicated contact? | Ask from what contract level this is available |
Questions for the vendor:
- What happens if we find a critical bug on a Friday night?
- Who will be our point of contact and what internal access level do they have?
- How often do you inform us of changes, updates, or known issues?
- Can we speak with other customers of a similar size?
Dimension 6: Vendor Maturity
How solid the provider is as a company.
Evaluation criteria:
| Criterion | Key question | How to verify |
|---|---|---|
| Funding | Does the company have funding for the next 18-24 months? | Search for public information on rounds and investors |
| Team | Who is behind the product? | Research the founding and technical team |
| Traction | How many customers do they have in your segment? | Request specific references from your industry |
| Roadmap | Where is the product heading? | Request the roadmap and evaluate alignment with your needs |
| Dependencies | Do they depend on an external model provider? | Assess the risk if that provider changes prices or terms |
| Competition | How do they position themselves against alternatives? | Request an honest comparison, not just their strengths |
Questions for the vendor:
- If tomorrow OpenAI (or Anthropic, or Google) raises prices by 200%, how does it affect your service?
- What is your real competitive advantage over open-source alternatives?
- What percentage of your customers renew?
- Have you lost customers in the last 6 months and why?
Dimension 7: Use Case Fit
The most important and the most frequently poorly evaluated.
Evaluation criteria:
| Criterion | Key question | How to verify |
|---|---|---|
| Functional fit | Does it solve your specific problem? | Define 10 scenarios and test them |
| Industry experience | Have they worked with companies like yours? | Request success stories from your sector |
| Complexity vs. need | Are you buying more than you need? | Evaluate whether a simpler solution would suffice |
| Time-to-value | When will you see results? | Request a realistic timeline with milestones |
| Internal effort | How much do you need to invest from your team? | Estimate the required internal hours |
The Scoring System
To compare vendors objectively, assign weight to each dimension based on your priorities and score each vendor from 1 to 5.
Weighting Example
| Dimension | Weight (regulated company) | Weight (growth startup) |
|---|---|---|
| Technical capability | 20% | 25% |
| Security and compliance | 30% | 10% |
| Integration | 15% | 20% |
| Pricing | 10% | 20% |
| Support | 10% | 10% |
| Vendor maturity | 10% | 5% |
| Use case fit | 5% | 10% |
How to Score
5 points: meets all dimension criteria without reservation. 4 points: meets most criteria with minor areas for improvement. 3 points: meets minimum criteria but has significant gaps. 2 points: fails to meet several important criteria. 1 point: does not meet the fundamental criteria of the dimension.
Final Score Calculation
Final score = Sum of (Dimension score x Dimension weight)Score above 4.0: strong candidate, proceed to negotiation. Score between 3.0 and 4.0: viable candidate with reservations, negotiate improvements in weak areas. Score below 3.0: discard or re-evaluate whether your requirements are correct.
The Complete Evaluation Process
Week 1: Preparation
- Define your functional, technical, and security requirements.
- Prepare 50-100 test cases with real data.
- List the 3-5 candidate vendors.
- Prepare the evaluation matrix with dimension weights.
Week 2: Demos and Initial Evaluation
- Request personalized demos (not generic) with your use case.
- Ask the questions from each dimension during the demo.
- Complete the initial scoring based on the demo and documentation.
- Discard vendors scoring below 2.5.
Weeks 3-4: Technical Proof of Concept
- Request access to a test environment with the 2-3 finalists.
- Run your 50-100 test cases with real data.
- Measure latency, accuracy, and response quality.
- Test integrations with your current systems.
- Involve end users in the testing.
Week 5: Final Evaluation and Decision
- Complete scoring for all dimensions with PoC data.
- Compare final scores.
- Negotiate commercial terms with the finalist.
- Document the decision and criteria for future reference.
Red Flags: When to Discard a Vendor
Discard immediately if:
- They do not allow testing with your data before signing a contract.
- They cannot confirm in writing where your data is processed.
- They do not have a DPA or resist signing yours.
- They dodge pricing questions with “it depends on the case.”
- They do not have verifiable customers in your segment.
Evaluate with caution if:
- It is a startup with less than 18 months of secured funding.
- It depends exclusively on one model provider (e.g., only GPT-4).
- Technical documentation is incomplete or outdated.
- Support during the evaluation is slow or generic.
- The roadmap does not include features you need in the short term.
Common Evaluation Mistakes
Mistake 1: Evaluating Only the Demo
Demos are optimized to impress. Always test with your real data, your volumes, and your edge cases. A vendor that shines in a demo may fail with real data.
Mistake 2: Not Involving the Technical Team
AI purchasing decisions cannot be made from the business side alone. The technical team must evaluate integration, architecture, and maintenance implications.
Mistake 3: Optimizing Only on Price
The cheapest vendor may cost double if it requires triple the internal integration hours, has worse support, or generates more errors requiring human correction.
Mistake 4: Not Considering Switching Costs
Ask yourself: if in 12 months this vendor does not work out, how much will it cost to migrate to another. If the answer is “a lot,” demand contractual flexibility and ensure you can export your data and configurations.
Mistake 5: Being Influenced by Brand
That a vendor has the best-known brand does not mean it is the best for your case. Evaluate the fit with your specific problem, not market share.
Conclusion: Decide With Data, Not Demos
AI vendor evaluation is a process that requires rigor because the consequences of a poor choice are costly: money lost, time lost, and in some cases, damaged customer relationships.
The framework presented here does not eliminate risk, but it reduces it significantly by converting a subjective decision (“I liked this vendor better in the demo”) into an objective evaluation based on measurable criteria.
If you need help evaluating AI vendors for your company, you can explore our AI assistant services which include technology selection advisory. We also offer a free AI audit that includes an initial assessment of your use case and vendor recommendations aligned with your needs.
