Normalized claim
Quantified impact: 5 x increase
Inference latency improved by 5x in a closed-book pilot.
Workday Accelerates Generative AI & ML Product Development Using Amazon SageMaker
Use case typeAI platformUpdated Jun 13, 2026
Workday uses Amazon SageMaker to help engineers build, train, evaluate, customize, and deploy machine learning and large language models for generative AI product development. The company also uses Amazon Bedrock to prototype and scale generative AI capabilities across regions.
- Organization
- Workday
- Industry
- Tech & Comms
- Location
- United States
- Published
- May 2026
Planned next steps
- Inference latency improved by 5x in a closed-book pilot.
Why do we believe this?Outcome claims, sources, and evidence checks
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Workday
- Provider
- AWS
- Maturity
- Production
- Linked source
- AWS Solutions Case Study
Workday needed to accelerate ML and generative AI product development while meeting global data residency requirements, improving inference latency, and reducing operational burden
Customer identity supportedSource describes one deploymentMaturity supported
Primary read
Use case focus
Showing 3 of 5
- 1Generative AI Product Development
- 2Model Evaluation and Fine-Tuning
- 3LLM Deployment
- Adopted Amazon SageMaker Studio, JumpStart, Ground Truth Plus, and Notebook Instances to search, evaluate, customize, fine-tune, and deploy LLMs.
- Used a federated, distributed inference system across AWS regions.
- Used Amazon Bedrock to prototype multibillion-parameter models and identify which AI capabilities to invest in.
- Scaled from about 1,000 inference requests to tens of millions per day with virtually no downtime.
- Enabled immediate deployment of new features across regions.
Sources & evidence1
Evidence: Medium65/100Evidence strength
- Customer explicitly identified
- Deployment status explicitly supported
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: May 27, 2026Publisher: AWSEvidence: VendorConfidence: Medium
AI-generated summary. Verify important details with the linked sources before relying on this case.
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