AI Infrastructure Architecture for Business
How to architect AI infrastructure. Cloud, hybrid, on-premise considerations.
Cloud-native
AWS, Azure, GCP with AI services. Fastest deployment. Scales easily. Most enterprises start here.
Hybrid
Some on-premise, some cloud. Sensitive data on-premise; compute in cloud. Common in regulated industries.
On-premise
Full control. Higher cost. Required for some compliance situations.
Edge
AI close to data source. Real-time inference. Bandwidth optimization.
Bottom line
Most enterprises cloud-first with hybrid for specific needs. Match architecture to requirements.
Frequently asked questions
Cloud or on-premise for AI?
Cloud-first for most. On-premise for specific compliance or data sensitivity. Hybrid common in regulated industries.
Best cloud for AI?
AWS, Azure, Google all competitive. Microsoft strong for M365 integration. AWS broad. Google strong for AI specifically. Often multi-cloud.
What about edge AI?
Important for real-time inference, IoT, autonomous systems. Specific use cases. Most enterprise AI cloud-based.
Cost implications?
Cloud variable cost; on-premise fixed cost plus operations. Crossover at scale. Most enterprises cloud-first until specific reason.
Compliance considerations?
Data residency, sovereignty, sector-specific. Cloud providers offer compliant regions. On-premise for highest-risk.
Related guides
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