Normalized claim
Individual search times: 50-75% decrease
cut individual search times by 50-75 percent
BioNTech accelerates proteomics data processing 500x using AWS Storage Gateway and parallel compute
Use case typeData platform modernizationUpdated Jul 18, 2026
BioNTech, headquartered in Germany, needed to streamline the storage, organization, and processing of mass spectrometry data for proteomics and immunopeptidomics workflows. The company migrated on-premises data movement and compute workflows to AWS so researchers could process larger data volumes and run many searches in parallel.
Reported outcomes
500x
data processing speedTime & speed
50-75%individual search times60 hourswork reprocessed
Strategic outcomes
Other strategic outcomeFreed scientists from manual data managementScale & capacityEnabled parallel processing at far larger scaleEcosystem & partnershipsImproved sharing and collaboration on mass spectrometry data
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Data processing speed: 500 x increase
massively accelerating data processing up to 500 times
Normalized claim
Work reprocessed: 60 hours decrease
could redo all the work from the past 7 years in 60 hours
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- BioNTech
- Provider
- AWS
- Maturity
- Unknown
- Linked source
- AWS Customer Stories
No explicit deployment-stage evidence found.
Customer identity supportedSource describes one deploymentMaturity evidence evaluated
Primary read
Use case focus
Showing 3 of 3
- 1Data platform modernization
- 2Workflow orchestration
- 3Operations optimization
- Manual movement of mass spectrometry data from instrument computers to local workstations was time-consuming.
- Local hardware could not support growing data volumes or hundreds of parallel search requests.
- BioNTech needed a more scalable way to store, organize, and process terabytes of MS data.
- BioNTech installed the AWS Storage Gateway agent on each instrument computer to move raw MS data automatically to Amazon S3.
- The team ran Spectrum Mill on Amazon EC2, used Amazon EC2 Spot Instances for lower-cost compute, and orchestrated workflows with Amazon SQS, Amazon API Gateway, Amazon EC2 Auto Scaling, and Amazon Machine Images.
- The company cut individual search times by 50-75 percent.
- It can now run hundreds of instances in parallel.
- Mass spectrometry data processing accelerated up to 500 times.
- BioNTech says it could redo seven years of work in 60 hours for a fraction of the price.
Architecture
BioNTech installed the AWS Storage Gateway agent on instrument computers to move raw mass spectrometry data to Amazon S3, hosted Spectrum Mill on Amazon EC2, used Amazon EC2 Spot Instances for cost-efficient compute, and scaled runs with Amazon EC2 Auto Scaling, Amazon Machine Images, Amazon SQS, and Amazon API Gateway.
Sources & evidence1
Evidence: Medium50/100Evidence strength
- Customer explicitly identified
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: Jul 18, 2026Publisher: AWSEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
Explore related AI use cases
Customer evidence
Provider evidence
Browse the catalog
Was this useful?
Community
Comments
No published comments yet.