With the rapid expansion of AI technologies, enterprises must carefully evaluate which model types align with business goals, data availability, compute capacity, and governance requirements.

This checklist from Red Hat provides a practical decision-making framework to help organisations select the right AI model—ensuring scalability, responsible use, and long-term ROI.

What You’ll Learn

This asset breaks down key considerations in the AI model selection process, including:

1. Aligning model capabilities with your use case

Understand when to use predictive models, traditional machine learning, or generative AI.

2. Evaluating data readiness and model requirements

Assess whether your data supports training, fine-tuning, or inference workloads.

3. Governance, risk, and transparency

Ensure compliance and control through explainability, lifecycle management, and guardrails.

4. Cost and resource implications

Learn how to balance performance needs with compute, storage, and maintenance overhead.

5. Open-source vs proprietary models

Compare flexibility, security posture, ecosystem support, and potential vendor lock-in.

6. Deployment considerations

Plan for hybrid cloud, containerised, or on-prem environments to support enterprise scalability.