Normalized claim
Time: 10 seconds decrease
Reduced query time from about two minutes to 10 seconds.
GCPEvidence: Medium50/100
CytoReason uses BigQuery and GKE to accelerate AI disease modeling for clinical trials (Vertex AI/Gemini not specified)
Use case typeAI development platformUpdated Jun 13, 2026
CytoReason is an Israeli biotech and data platform that creates AI-based computational disease models using public and proprietary data. The company maps human diseases tissue by tissue and cell by cell to help pharma customers shorten clinical trials and reduce drug development costs. CytoReason moved PostgreSQL databases and analytics workloads to BigQuery to store and query very large datasets at speed. It also uses Google Kubernetes Engine for autoscaling and high-performance computing, and worked with WideOps to optimize Kubernetes infrastructure costs. The article says CytoReason built its own high-performance computing solution internally on GKE and uses Cloud Storage plus billing tools for cost optimization.
- Organization
- CytoReason
- Industry
- Pharma
- Location
- Israel
- Published
- June 2026
Reported outcomes
Time: Approximately 10 seconds
Time & speed
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
- CytoReason
- Provider
- GCP
- Maturity
- Unknown
- Linked source
- Google Cloud Customer Story
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
- 2Analytics Acceleration
- 3MLOps / AI Infrastructure
- Migrated PostgreSQL databases to BigQuery for faster storage and querying of large datasets.
- Used Google Kubernetes Engine for automated cluster lifecycle management, pod and cluster autoscaling, and infrastructure cost optimization.
- Built an internal high-performance computing solution on GKE to handle phases with more disease model generation.
- Used Cloud Storage classes and billing export reports to improve cost optimization.
- Partnered with WideOps to optimize GKE usage and reduce infrastructure spending.
Technologies
- Reduced query time from about two minutes to 10 seconds.
- Contributed to scalable processing for major pharma clients such as Sanofi and Pfizer.
Architecture
CytoReason migrated PostgreSQL data into BigQuery for faster analytics and query performance, and runs an internally built high-performance computing solution on Google Kubernetes Engine. The setup relies on GKE autoscaling and health checks, with Cloud Storage and billing tools used for cost optimization, and WideOps assisted with Kubernetes cost and infrastructure optimization.
Sources & evidence1
Evidence: Medium50/100Evidence strength
- Customer explicitly identified
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: Jun 3, 2026Publisher: Google CloudEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
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