When to Run AI On-Premises vs. in the Cloud
TechTarget, Tuesday, June 23rd, 2026
AI workloads should be matched to on-premises or cloud based on data sensitivity, latency, cost, and maturity.
Deployment location directly affects data governance, security, cost, performance, and scalability, with McKinsey noting only 6% of organizations achieved meaningful AI impact partly due to misaligned environments.
Cloud spans IaaS, PaaS, APIs, and SaaS tiers and best fits experimentation and teams lacking AI engineering talent.
Hybrid and multi-cloud patterns offer flexibility and vendor independence at the cost of complexity.
The decision should be workload-specific rather than a binary choice.