Selecting the right AI vendor is about finding a partner that aligns with your business needs and your goals. It's about choosing a solution that embodies technology, governance, and long-term vision that caters to your unique values and use cases.
Today, we are exploring the AI vendor checklist: 20 essential questions every firm must ask when considering adopting and onboarding a new AI vendor.
The AI Vendor Checklist
Adopting the wrong solution can cost more than just time; it costs value and budget. So, to prepare for adopting your next AI solution, below are 20 essential questions that every business should ask before committing to a new vendor:
1. What specific enterprise use cases does your AI solution address?
This question will allow you to gauge specific model use cases and how they will fit into your system.
2. Does your platform support customization?
Understanding white-labeling is essential for brand identity opportunities.
3. Does your firm understand my industry's regulatory demands?
Each sector requires specific compliance regulations, and understanding how your vendor will address yours is necessary.
4. What underlying models or base technologies do you use (open source, closed models, multimodal, etc.)?
Understanding a stack or backend infrastructure will enable smoother integrations.
5. How is the AI deployed?
You need to understand how a vendor's solution is deployed and what environment it functions in to gauge compatibility.
6. Can your AI integrate with our existing tech stack, workflows, and data pipelines?
Asking this will determine if migration is possible.
7. What are the resource requirements for deployment and maintenance (compute, storage, team support)?
Resource allocation is a necessary set of knowledge to obtain, as it helps you understand what the AI model needs to operate and the intensive workloads it can handle.
8. How is data encrypted, stored, and protected?
This is one of the most important questions to ask; a model cannot be adopted without first knowing its data security and management practices.
9. Who owns the data during and after processing?
A chief concern for companies is data sovereignty, especially when sensitive datasets are ingested by a model they may not control.
10. Will our data be used to train broader models?
Understanding data-sharing standards is essential for measuring confidentiality and maintaining a competitive edge.
11. What compliance standards and certifications do you possess (e.g., GDPR, HIPAA, ISO, SOC)?
Understanding a vendor's alignment with compliance guidelines speaks to their legitimacy and credibility.
12. What is your governance model for AI?
Understanding how a vendor regulates their model's functions, ensuring it doesn't overstep its automations, and maintaining data integrity are extremely important.
13. How does your model manage harmful outputs?
AI output accuracy is everything. Understanding how a vendor solution mitigates harmful or inaccurate outputs is an indicator of model performance.
14. Is human oversight built in?
Human-in-the-loop feedback keeps AI systems accountable.
15. What performance metrics can you share?
Whether a testimonial or statistics, vendors should be able to share evidence of their model's success.
16. Can the platform scale with our expected demand and growth?
This question will give you an idea of a vendor model's limitations and its ability to scale with increased workloads.
17. What's the vendor's roadmap?
Understanding a vendor's development timeline will give you an idea of when you can implement planned updates for your own company.
18. What does onboarding look like?
Knowing whether an implementation process is frictionless can be a significant factor in vendor selection.
19. What is the pricing model?
Also, an insanely vital question to ask: understanding pricing will provide insight into what models are capable of at their costs.
20. Can you quantify potential ROI?
Asking this will pressure the vendor to demonstrate how adopting their model will deliver value.
Why This Checklist Matters
Leveraging this checklist when selecting an AI vendor will help leadership teams choose a solution that aligns with their needs in productivity, data management and access, and security and compliance. It is okay to interview vendors, treating them as a potential next step, because you want to ensure that whoever you're working with will deliver results tailored to your needs.
At FluxAI, enterprise AI deserves more than attention; it requires transparency, security, flexibility, and alignment to real business goals. Use this checklist to discover a vendor that works best for you!
How FluxAI Answers These Questions
FluxAI is built to answer every question on this checklist with transparency and confidence.
Our Answers: Use Cases: Workflow automation, document intelligence, private AI chat across all industries
Customization: Fully customizable deployment on your infrastructure
Compliance: HIPAA-ready with BAA, GDPR compliant, SOC 2 Type II in progress
Technology: Support for all open-source LLMs (Llama, Gemma, GPT-OSS, Deepseek, custom models)
Deployment: On-premises, private cloud, or hybrid—your choice
Integration: 400+ pre-built integrations via n8n workflow automation
Data Ownership: Your data stays on your infrastructure—you own it completely
Training: We never train on customer data, ever
Scaling: Enterprise-grade architecture built to handle growth
Ready to evaluate FluxAI using this checklist?
Use this checklist to make informed AI vendor decisions—and see how FluxAI measures up.