Normalized claim
Cost: 33% decrease
Reduced overall cloud overhead costs by about 33%.
Innovaccer modernizes healthcare AI data platform with Amazon Bedrock and Amazon SageMaker
Use case typeAI platformUpdated Jun 13, 2026
Innovaccer, a healthcare AI and analytics SaaS company, modernized its AWS-based architecture to control costs and maintain performance as it scaled enterprise deployments. The company manages patient data in both structured and unstructured formats and runs large-scale analytics across hundreds of terabytes of data each day. Innovaccer also used Amazon Bedrock to quickly establish a low-code/no-code RAG-based generative AI system over multimodal content.
- Organization
- Innovaccer
- Industry
- Healthcare
- Location
- United States
- Published
- May 2026
Reported outcomes
−65%
quantified impactCost savings
−33%cost+30%quantified impact−45%cost
Strategic outcomes
Cost efficiencyReduced cloud overhead costsSpeed & agilityDeployed RAG system rapidlyCost efficiencyLowered database operating costsNew product / capabilityBuilt low-code RAG search capability
Catalog median for cost savings deployments: −40% across 177 reported metrics. Compare benchmarks →
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Quantified impact: 30% increase
Improved performance by 30%.
Normalized claim
Quantified impact: 65% decrease
Achieved a 65% reduction in management overhead.
Normalized claim
Cost: 45% decrease
Saved 45% of monthly Amazon RDS cost after adopting Aurora I/O Optimized.
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Innovaccer
- Provider
- AWS
- Maturity
- Unknown
- Linked source
- AWS Solutions Case Study
No explicit deployment-stage evidence found.
Customer identity supportedSource describes one deploymentMaturity evidence evaluated
Primary read
Use case focus
Showing 3 of 4
- 1Generative AI
- 2Retrieval-Augmented Generation (RAG)
- 3Data platform modernization
- Managing increasing volumes of varied patient data in structured and unstructured formats.
- Modernizing infrastructure to control cloud costs while maintaining performance and reliability for large enterprise deployments.
- Modernized its database and analytics architecture on AWS using Amazon Aurora, Amazon EKS, Amazon ECS, Amazon OpenSearch Service, AWS Graviton Processors, Amazon SageMaker, and Amazon Bedrock.
- Adopted Amazon Aurora as a PostgreSQL-compatible relational database for OLTP and OLAP workloads.
- Moved Amazon RDS instances to AWS Graviton Processors and adopted Amazon Aurora I/O Optimized for IOPS-intensive workloads.
- Implemented Amazon EKS and Amazon ECS for containerized workloads and Amazon OpenSearch Service as part of the modernization effort.
- Used Amazon Bedrock to create a low-code/no-code RAG system for searching multimodal content.
- Reduced overall cloud overhead costs by about 33%.
- Improved performance by 30%.
- Achieved a 65% reduction in management overhead.
- Saved 45% of monthly Amazon RDS cost after adopting Aurora I/O Optimized.
- Got the RAG-based system running in just a few minutes.
Architecture
Innovaccer modernized its AWS environment by using Amazon Aurora for relational database workloads, Amazon EKS and Amazon ECS for container orchestration, Amazon OpenSearch Service for search, AWS Graviton Processors for compute efficiency, and Amazon Bedrock for low-code/no-code RAG over multimodal content.
Sources & evidence1
Evidence: Medium50/100Evidence strength
- Customer explicitly identified
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: May 13, 2026Publisher: AWSEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
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