Normalized claim
Time: 8 hours decrease
Climate data processing time fell from up to 8 hours to 30 minutes, a reduction of more than 90%.
GCPEvidence: Medium50/100
ICPAC transformed into a 24/7 climate intelligence platform (Gemini roadmap via conversational interfaces)
Use case typeAI platformUpdated Jun 13, 2026
The IGAD Climate Prediction and Applications Centre (ICPAC) serves 11 countries in the Greater Horn of Africa with climate forecasting and early warning services. To improve reliability after on-premises disruptions and a monolithic setup, ICPAC moved to Google Cloud and adopted a microservices architecture on Google Kubernetes Engine. ICPAC uses Cloud Run to process geospatial data, automated deployments with Cloud Build and Artifact Registry, and secured data and access with Cloud Storage and IAM. The organization reports that climate data processing dropped from up to 8 hours to 30 minutes, cloud services costs fell by more than 40%, and the platform now runs 24/7. ICPAC also plans to use Gemini for conversational interfaces so users can ask questions in local languages.
- Organization
- IGAD Climate Prediction and Applications Centre
- Industry
- Public Sector
- Location
- Kenya
- Published
- June 2026
Reported outcomes
−40%
costCost savings
8 hourstime30 minutestime−90%time
Strategic outcomes
Speed & agilityEnabled continuous 24/7 climate monitoringCost efficiencyReduced cloud services costsNew product / capabilityAccelerated climate data processingCustomer experience & trustDelivered more actionable reports remotely
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Time: 30 minutes decrease
Climate data processing time fell from up to 8 hours to 30 minutes, a reduction of more than 90%.
Normalized claim
Time: 90% decrease
Climate data processing time fell from up to 8 hours to 30 minutes, a reduction of more than 90%.
Normalized claim
Cost: 40% decrease
Cloud services costs decreased by more than 40% through rightsizing and event-based scaling.
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- IGAD Climate Prediction and Applications Centre
- Provider
- GCP
- Maturity
- Unknown
- Linked source
- Google Cloud 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
- 1Cloud modernization
- 2Workflow automation
- 3Generative AI roadmap
- On-premises infrastructure and a monolithic cloud setup caused reliability issues, including interruptions from power and internet disruptions.
- Manual, error-prone deployment workflows slowed updates for mission-critical early warning services.
- Overprovisioned virtual machines increased cloud costs for a regionally important climate platform.
- ICPAC migrated from a monolithic architecture to microservices on Google Kubernetes Engine.
- The East Africa Hazards Watch platform uses Cloud Run to process large geospatial data workloads.
- Cloud Build and Artifact Registry were used to automate CI/CD pipelines for secure deployments.
- Cloud Storage and custom service accounts with IAM improved data security and reduced exposure from shared credentials.
- ICPAC plans to use Gemini to create conversational interfaces for localized question answering.
- Climate data processing time fell from up to 8 hours to 30 minutes, a reduction of more than 90%.
- Cloud services costs decreased by more than 40% through rightsizing and event-based scaling.
- The platform now supports 24/7 availability for mission-critical climate monitoring and early warnings.
- Government officials and disaster management teams can access more actionable reports from anywhere.
Architecture
ICPAC modernized from a monolithic environment to microservices on Google Kubernetes Engine, used Cloud Run for geospatial processing, automated delivery with Cloud Build and Artifact Registry, and secured access with Cloud Storage and IAM. Google Cloud autoscaling and event-based scaling were used to optimize uptime and cost, and Gemini is planned for conversational interfaces.
Sources & evidence1
Evidence: Medium50/100Evidence strength
- Customer explicitly identified
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: Jun 12, 2026Publisher: Google CloudEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
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