This independently verified report from UserEvidence captures how MinIO customers across 25 countries and 9 industries are moving from AI experimentation to production execution. The data is striking: 70% of respondents are already running AI or ML projects, with just 14% still in the planning phase — and the share moving from planning to production has doubled in the past year. The report reveals the actual infrastructure strategies enterprises are using: 59% run AI workloads on-premises, prioritizing latency, bandwidth, and architectural control. 55% rely on open-source frameworks like TensorFlow and PyTorch. The dominant workload mix spans generative AI (46%), NLP (45%), anomaly detection (34%), and classical ML (31%). Three constraints shape every AI infrastructure decision nearly equally: cost efficiency (58%), security (56%), and performance (54%). The report benchmarks where leading enterprises stand across infrastructure strategy, workload type, tooling preference, and deployment environment, providing a data-grounded view of what AI-ready infrastructure looks like in practice.
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70% of MinIO customers are already running AI or ML projects in production — the share moving from planning to production has doubled in the past year, making infrastructure readiness an urgent competitive priority.
On-premises infrastructure remains the backbone of enterprise AI, with 59% running workloads on-prem due to latency, bandwidth, and sovereignty requirements — cloud is used primarily for burst capacity.
Cost, security, and performance are weighted nearly equally as top AI infrastructure constraints, requiring architectures that treat all three as first-order design principles rather than tradeoffs.
Enterprise technology leaders, AI infrastructure architects, and data platform teams benchmarking their AI strategy against real-world deployment patterns across industries and geographies.
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