ToolsAugust 28, 2026via AWS Machine Learning Blog
Spreading the load: How Salesforce met Multi-AZ HA with SageMaker Inference Components
Why it matters
A practical case study in using SageMaker's scheduling features to balance high-availability compliance with model co-hosting efficiency. Relevant to practitioners deploying inference at scale.
Key signals
- Salesforce deployed across multiple Availability Zones using SageMaker Inference Components
- Used SchedulingConfig parameter for model placement strategy
- Maintained cost efficiency through multi-model co-hosting while meeting Multi-AZ HA requirements
- This is a vendor (AWS) technical deep-dive on production inference patterns
- Salesforce deployed models across multiple Availability Zones using SageMaker Inference Components
- Used SchedulingConfig parameter for AZ-aware placement
- Maintained multi-model co-hosting cost efficiency while meeting Multi-AZ HA compliance
- Published by AWS on SageMaker ML blog
The hook
Salesforce solved multi-region AI inference without the cost tax—here's how they did it.
Learn how Salesforce used Amazon SageMaker AI Inference Component placement (the SchedulingConfig parameter) to distribute model copies across multiple Availability Zones, meeting their Multi-AZ high availability compliance requirements without sacrificing the cost efficiency of multi-model co-hosti…