Hybrid Cloud AI Design Patterns

Learn proven architectural patterns for splitting AI workloads between on-premises and cloud environments to maximize performance, cost efficiency, and compliance.

Pattern 1: Cloud Burst Training

Run baseline training on on-premises GPUs and burst to cloud when you need more capacity for larger experiments, hyperparameter sweeps, or deadline-driven projects.

# Cloud Burst Training Flow
1. Data stays on-premises (primary copy)
2. Anonymized/sampled data synced to cloud for experiments
3. On-prem GPUs handle daily training jobs
4. Cloud GPUs activated for:
   - Large model training (needs 100+ GPUs)
   - Hyperparameter sweeps (parallel experiments)
   - Deadline-driven projects (need more capacity now)
5. Trained model artifacts synced back to on-premises
6. Cloud resources released after training completes

Pattern 2: Split Inference

Run latency-sensitive or data-sensitive inference on-premises and scale customer-facing inference in the cloud. This pattern is common in healthcare and financial services.

Pattern 3: Train On-Prem, Serve in Cloud

Keep sensitive training data on-premises, train models locally, then deploy trained models (which contain no raw data) to cloud for global serving. This satisfies data residency while enabling global reach.

Pattern 4: Federated Learning

Train model fragments at multiple locations (on-premises sites, edge devices) without centralizing raw data. Only model gradients or weights are shared, preserving data privacy while building a global model.

Pattern Comparison