In the race to scale generative AI, the tension between infrastructure efficiency and system reliability has become a defining challenge for platform engineers. Salesforce recently addressed this by leveraging Inference Components (IC) to consolidate multiple models onto shared GPU instances, a move that slashed their infrastructure costs by 8x. While the financial gains were immediate, the consolidation created a new technical hurdle: maintaining high availability (HA) in a multi-Availability Zone (AZ) environment.

The Balancing Act of Model Placement

Standard deployment algorithms often prioritize resource density, which can inadvertently lead to model replicas clustering within a single AZ. In a production environment like Salesforce's Agentforce, this creates a single point of failure; if that specific zone experiences an outage, the service goes dark. To mitigate this, Salesforce turned to On-Demand Capacity Reservations (ODCR) to pre-provision GPU capacity across target zones. This ensures that when the system attempts to distribute replicas, the physical hardware is already reserved and ready to accept the workload, preventing deployment failures caused by localized capacity crunches.

Controlling Distribution with SchedulingConfig

AWS recently introduced the `SchedulingConfig` parameter within the `CreateInferenceComponent` API, providing granular control over where model replicas land. By utilizing the `SPREAD` strategy in combination with the `MaxImbalance` parameter, engineers can now force a balanced distribution of replicas across different instances and zones. The `SPREAD` strategy ensures that replicas are distributed to avoid having multiple copies of the same model on a single failing instance, while `MaxImbalance` dictates the allowable difference in replica counts between zones.

For example, setting `MaxImbalance` to 1 ensures that the difference in replica counts between any two zones never exceeds one. For critical workloads, setting this to 0 forces an exact split, ensuring that even if one AZ goes offline, the service remains operational in the other. The configuration is applied as follows:

{"SchedulingStrategy": "SPREAD", "MaxImbalance": 1}

Operationalizing Reliability and Observability

Achieving this balance requires moving beyond simple deployment scripts. Salesforce utilizes the `ScaleInPolicy` with a `CONSOLIDATION` strategy to ensure that as the system scales, the background sweeper maintains these distribution constraints even during routine maintenance or instance reclamation. To monitor these deployments, teams rely on SageMaker AI Inference Components and the Reliability tab within SageMaker AI Insights. This dashboard provides real-time visibility into AZ balance metrics, allowing engineers to identify and correct clustering issues before they impact production SLAs.

For teams managing large-scale AI workloads, the lesson is clear: cost-efficiency and high availability are not mutually exclusive, provided you retain manual control over the underlying infrastructure placement. By combining pre-reserved capacity with strict scheduling constraints, organizations can achieve the density required for cost savings without sacrificing the resilience demanded by enterprise-grade AI applications.