AI Serving Layer Architecture Advanced

The serving layer is where AI models meet real users. A well-architected serving infrastructure must deliver predictions with low latency, high availability, and predictable cost while supporting model updates, A/B testing, and graceful degradation.

Serving Patterns

Model Serving Frameworks

• NVIDIA Triton - Multi-framework inference server with dynamic batching, model ensembles, and GPU scheduling
• TensorFlow Serving - Production serving for TensorFlow models with versioning and warm-up
• TorchServe - PyTorch native serving with handler customization and metrics
• vLLM - High-throughput LLM serving with PagedAttention and continuous batching
• BentoML - Framework-agnostic serving with built-in containerization

Deployment Strategies

• Blue/green - Deploy new model version alongside old, switch traffic atomically after validation
• Canary - Route a small percentage of traffic to the new model, gradually increase if metrics are healthy
• Shadow - Run new model in parallel without serving its predictions; compare outputs offline
• Multi-armed bandit - Dynamically allocate traffic based on real-time performance metrics

Auto-scaling for Inference

Configure scaling policies based on the right metrics:

• GPU utilization - Scale when GPU utilization exceeds 70% sustained
• Request queue depth - Scale when pending requests exceed threshold
• Latency p99 - Scale when tail latency breaches SLA
• Custom metrics - Scale based on tokens-per-second for LLM workloads