Picking the wrong AI vendor costs more than money. It costs time, momentum, and organizational trust in AI initiatives.

The AI vendor landscape in 2025 is crowded. Everyone claims to be the best solution for your needs. Demos look impressive. Sales presentations promise transformation. But according to MIT’s 2025 research, purchasing AI tools from specialized vendors succeeds about 67% of the time, while building internally succeeds only one-third as often. The vendor matters.

This guide provides a systematic approach to evaluating AI vendors. Not a list of vendors to buy from, but a framework for making the decision yourself.

Before You Evaluate: Know What You Need

Vendor evaluation starts before you talk to vendors.

Define the problem first: What specific business problem are you solving? “We need AI” isn’t a requirement. “We need to reduce prospect research time from 45 minutes to 15 minutes” is a requirement.

Identify must-haves vs. nice-to-haves: What capabilities are essential? What would be bonus? Rank them. This prevents being seduced by impressive features you don’t actually need.

Set budget parameters: What can you spend? Include implementation, training, and ongoing costs, not just licensing. Know your range before pricing discussions.

Establish timeline: When do you need this working? Vendors who can’t meet your timeline eliminate themselves.

Define success criteria: How will you know if the vendor worked out? Specific metrics you’ll measure after implementation.

Document this before reaching out to vendors. It keeps you focused when sales conversations try to expand scope.

The Evaluation Framework

Evaluate vendors across seven categories:

1. Technical Capabilities
2. Security and Compliance
3. Integration and Implementation
4. Support and Training
5. Pricing and Total Cost
6. Vendor Stability
7. Strategic Fit

Not all categories matter equally for every situation. Weight them based on your priorities.

Category 1: Technical Capabilities

Can the vendor’s technology actually do what you need?

Core Functionality

Questions to ask:

• Does the product solve our specific use case?
• What are the product’s limitations?
• How does it perform on tasks similar to ours?
• What output quality can we expect?

Evaluation approach:

• Hands-on trial with your actual data and tasks
• Not just demos with the vendor’s prepared examples
• Test edge cases and difficult scenarios
• Compare outputs to your quality standards

Red flags:

• Vendor can’t demo your specific use case
• Only willing to show prepared demos
• Vague answers about limitations
• Claims that work for “everything”

AI Model Details

Questions to ask:

• What AI models power the product?
• How was the model trained? On what data?
• How often is the model updated?
• Can we use our own data to customize?

Why this matters: Research from Amplience notes that enterprises need to understand how a vendor’s AI model was trained and ensure that it has been trained on high-quality data from reliable sources.

Red flags:

• No transparency about model origins
• Can’t explain training data sources
• Model updates are infrequent or unclear
• No path to customization if needed

Performance and Reliability

Questions to ask:

• What’s the typical response time?
• What’s the uptime history? (Ask for SLA documentation)
• How does performance scale with usage?
• What happens during peak demand?

Evaluation approach:

• Request performance metrics and SLA terms
• Test during different times of day
• Ask for customer references to verify claims

Category 2: Security and Compliance

AI touches your data. Security failures here are catastrophic.

Data Handling

Questions to ask:

• Where is our data stored?
• Is data encrypted at rest and in transit?
• Do you use customer data to train your models?
• Can we delete our data? How quickly?
• What data do you retain and for how long?

Critical point: According to recent enterprise research, 92% of AI vendors claim broad data usage rights, far exceeding the market average of 63%. Read the fine print on data rights carefully.

Red flags:

• Vague answers about data usage
• Customer data used for model training by default
• No clear data deletion process
• Data stored in jurisdictions with weak privacy laws

Compliance Certifications

Questions to ask:

• What security certifications do you hold? (SOC 2 Type II, ISO 27001, etc.)
• Are you GDPR compliant?
• Are you CCPA compliant?
• What industry-specific compliance do you support? (HIPAA, PCI, etc.)

Evaluation approach:

• Request certification documentation
• Verify certifications are current
• Understand what’s actually covered by certifications

Red flags:

• Certifications are pending or partial
• Can’t provide documentation
• Certifications don’t cover the AI components

Security Practices

Questions to ask:

• How do you handle access controls?
• What authentication options are available?
• Do you support SSO and SCIM?
• What’s your vulnerability management process?
• When was your last penetration test?

Documentation to request:

• Security whitepaper
• Data Processing Agreement (DPA)
• Privacy policy
• Incident response plan

Category 3: Integration and Implementation

The best technology fails if you can’t integrate it.

Technical Integration

Questions to ask:

• What integrations are available out of the box?
• Is there an API for custom integrations?
• What does API documentation look like?
• Can we trial the API before purchasing?

Evaluation approach:

• Review API documentation quality
• Test integrations with your existing tools
• Understand rate limits and constraints

Red flags:

• No API or limited API access
• Integrations with your tools “coming soon”
• Poor or incomplete documentation
• Hidden API costs or restrictions

Implementation Process

Questions to ask:

• What does a typical implementation look like?
• How long does implementation take?
• What resources are required from our side?
• Who manages the implementation?
• What are common implementation challenges?

Evaluation approach:

• Get detailed implementation timeline
• Understand your team’s time commitment
• Ask for implementation references

Netguru’s AI vendor selection guide distinguishes between turnkey and bespoke implementations. Turnkey projects provide a hands-off approach while custom-built integrations are often more attractive to enterprise businesses but require more resources.

Data Migration and Configuration

Questions to ask:

• What data do we need to provide?
• What format is required?
• How is initial configuration handled?
• Can we migrate existing prompts/workflows?

Category 4: Support and Training

Post-sale support determines long-term success.

Support Availability

Questions to ask:

• What support channels are available?
• What are support hours? Is 24/7 available?
• What are typical response times?
• Is support included or additional cost?
• Do we get a dedicated account manager?

Evaluation approach:

• Test support responsiveness before buying
• Ask for support SLAs in writing
• Request customer references specifically about support

Training Resources

Questions to ask:

• What training is included?
• What format is training? (Live, on-demand, documentation)
• Is training role-specific or generic?
• How do you train new employees after launch?
• What ongoing learning resources exist?

Evaluation approach:

• Review training materials during evaluation
• Understand training time requirements
• Assess training quality and relevance

Our AI training program guide covers what effective AI training looks like. Compare vendor training against those standards.

Customer Success

Questions to ask:

• Do we get a customer success manager?
• What does the customer success program include?
• How do you measure customer success?
• What’s your customer retention rate?

Category 5: Pricing and Total Cost

Software licensing is just the beginning.

Pricing Structure

Questions to ask:

• What’s the pricing model? (Per seat, usage-based, flat rate)
• What’s included in base pricing?
• What costs extra?
• How does pricing scale as we grow?
• What are the contract terms?

Common pricing models:

Per-seat licensing: Predictable but may limit adoption Usage-based: Scales with value but less predictable Flat rate: Simple but may not fit usage patterns Hybrid: Combination approaches

Hidden Costs

Ask specifically about:

• Implementation fees
• Training fees
• Integration costs
• API overage charges
• Premium support costs
• Feature upgrade costs
• Data storage fees
• Minimum commitments

Calculation to request: Ask the vendor to calculate total cost for your specific scenario over 3 years, including all fees. Compare this across vendors.

Total Cost of Ownership

Beyond vendor fees, consider:

• Internal implementation time
• Training time for employees
• IT support burden
• Change management resources
• Opportunity cost during transition

Our building an AI business case guide covers calculating total cost of ownership in detail.

Category 6: Vendor Stability

Will this vendor be here in three years?

Financial Health

Questions to ask:

• How is the company funded?
• When was the last funding round?
• What’s your revenue trajectory?
• How many employees do you have?
• Is the company profitable?

Public companies: Review financial statements Private companies: Assess investors, growth trajectory, market position

Market Position

Evaluation approach:

• Research analyst reports on the vendor
• Check customer reviews (G2, Capterra, etc.)
• Understand competitive positioning
• Assess product development velocity

Red flags:

• Recent layoffs
• Key leadership departures
• Slowing product development
• Negative customer review trends
• Unclear funding situation

Data Portability

Questions to ask:

• Can we export our data if we leave?
• In what format?
• What’s the process and timeline?
• Are there exit fees?

If the vendor fails or you need to switch, data portability determines your options.

Category 7: Strategic Fit

Beyond capabilities, does this vendor fit your organization?

Cultural Alignment

Consider:

• Does the vendor understand your industry?
• Do they share your values around AI ethics?
• Is their communication style compatible?
• Do you trust them as a long-term partner?

Product Roadmap

Questions to ask:

• What’s on the product roadmap?
• How do you prioritize customer requests?
• How often do you release updates?
• Can we influence roadmap direction?

Evaluation approach:

• Request roadmap documentation
• Assess whether roadmap aligns with your future needs
• Understand how customer feedback influences development

Partnership Approach

Indicators of good partnerships:

• Dedicated account management
• Regular business reviews
• Willingness to customize
• Transparent communication
• Investment in your success

Research from Segalco emphasizes choosing a vendor that aligns with your business culture and values. Rather than seeking a supplier, seek a partner demonstrating commitment to understanding your unique needs.

The Evaluation Process

Step 1: Create Shortlist (Week 1)

Based on initial research, identify 3-5 vendors to evaluate seriously.

Sources for shortlist:

• Industry analyst reports
• Peer recommendations
• Review sites
• Conference presentations
• Your own research

Step 2: Initial Demos (Week 2-3)

Request demos from shortlisted vendors. Provide your specific use cases in advance.

During demos:

• Note what they show vs. what they avoid
• Ask how they’d handle your specific scenarios
• Assess presentation honesty vs. sales pressure

Step 3: Deep Dive Evaluation (Week 4-6)

For top 2-3 candidates:

• Hands-on trial with your data
• Technical evaluation with your IT team
• Security assessment
• Reference calls with current customers
• Detailed pricing discussion

Step 4: Reference Checks (Week 6-7)

Talk to actual customers. Request references similar to your use case.

Questions for references:

• How long have you used the product?
• What went well during implementation?
• What challenges did you face?
• How is ongoing support?
• Would you choose them again?
• What would you do differently?

Ask for references the vendor doesn’t provide. Find them through your network or review sites.

Step 5: Final Decision (Week 8)

Compile evaluation data. Score vendors against your criteria. Make recommendation.

Decision documentation:

• Summary of each vendor’s strengths/weaknesses
• Scoring against evaluation criteria
• Total cost comparison
• Recommendation with rationale
• Implementation plan for chosen vendor

The Evaluation Scorecard

Use this template to score vendors:

Category	Weight	Vendor A	Vendor B	Vendor C
Technical Capabilities	__%	__/10	__/10	__/10
Security & Compliance	__%	__/10	__/10	__/10
Integration	__%	__/10	__/10	__/10
Support & Training	__%	__/10	__/10	__/10
Pricing & TCO	__%	__/10	__/10	__/10
Vendor Stability	__%	__/10	__/10	__/10
Strategic Fit	__%	__/10	__/10	__/10
Weighted Total	100%	____	____	____

Assign weights based on your priorities. Security-sensitive industries weight compliance heavily. Budget-constrained organizations weight pricing heavily.

Common Evaluation Mistakes

Letting demos drive decisions. Demos show best-case scenarios. Hands-on trials show reality.

Ignoring references. Vendor-provided references are screened. Find independent ones too.

Underweighting implementation. The best product poorly implemented fails. Implementation quality matters as much as features.

Focusing only on features. Security, support, and stability can be more important than the extra feature you don’t really need.

Not involving the right people. IT, security, finance, and end users should all input. Don’t let one function decide alone.

Rushing the process. A few extra weeks of evaluation prevents years of regret. Take the time to evaluate properly.

After Selection

Vendor selected. Now what?

Contract negotiation:

• Review terms carefully
• Negotiate pricing and terms
• Clarify SLAs in writing
• Understand exit clauses

Implementation planning:

• Detailed timeline
• Resource allocation
• Communication plan
• Success metrics

Relationship setup:

• Identify key contacts
• Establish communication cadence
• Schedule business reviews
• Set expectations

For help designing the initial implementation, see our AI pilot program design guide. For change management during rollout, see our AI change management guide.

The Checklist Summary

Before signing with any AI vendor, verify:

Technical:

• Product solves our specific use case
• Hands-on trial completed successfully
• Limitations understood and acceptable
• Performance meets requirements

Security:

• Data handling practices reviewed
• Compliance certifications verified
• Security documentation received
• DPA signed

Integration:

• Integrations with our tools confirmed
• API quality assessed
• Implementation timeline agreed
• Resource requirements clear

Support:

• Support levels and SLAs documented
• Training approach acceptable
• Customer success structure understood

Pricing:

• Total cost calculated (not just licensing)
• Contract terms reviewed
• Exit costs understood
• Scaling costs projected

Vendor:

• Financial stability assessed
• References checked
• Roadmap reviewed
• Partnership approach confirmed

Skip none of these. Each has been a failure point for AI vendor relationships.

The right vendor accelerates your AI success. The wrong vendor sets you back. Take the time to choose well.