Normalized claim
Events per second: 100,000 events/sec
the production cluster now handles 100,000 events per second
GCPEvidence: Medium50/100
Tabby case study | Google Cloud
Use case typeData platform modernizationUpdated Jan 1, 2026
Tabby is a buy now, pay later fintech that built a scalable Google Cloud platform to handle high event and request volumes, simplify internal reporting, and let non-technical risk analysts test ML models quickly for recommendation and risk modeling. The company uses BigQuery and Bigtable for analytics, Vertex AI for model experimentation, and GKE, Pub/Sub, and Cloud SQL for production scalability.
- Organization
- Tabby
- Industry
- Finance
- Location
- United Arab Emirates
- Published
- January 2026
Reported outcomes
14,000,000,000 USD
annualized salesRevenue & growth
100,000 events/secevents per second350,000,000 requests/dayrequests per day20,000,000 usersregistered shoppers40,000 sellersactive sellers
Strategic outcomes
Scale & capacityScaled through seasonal traffic spikesInnovation & cultureEnabled analyst self-service model testingBetter decisions & insightImproved internal reporting and strategy visibilityRisk & complianceSupported data sovereignty compliance in Saudi Arabia
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Requests per day: 350,000,000 requests/day
processes around 350 million requests per day
Normalized claim
Registered shoppers: 20,000,000 users increase
expanded to serve more than 20 million registered shoppers
Normalized claim
Active sellers: 40,000 sellers
and 40,000 active sellers
Normalized claim
Annualized sales: 14,000,000,000 USD increase
driving annualized sales of more than $14 billion across the region
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Tabby
- 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
- 2AI model training
- 3Operational analytics
- Build a scalable fintech platform that can handle high event and request volumes.
- Enable risk analysts and other non-technical users to test machine learning models quickly for recommendation and risk modeling.
- Maintain resilience and support seasonal traffic spikes with minimal disruption.
- Built the core infrastructure on Google Kubernetes Engine, Pub/Sub, and Cloud SQL.
- Used Bigtable and BigQuery as the analytics foundation for dashboards and investor reporting.
- Applied Vertex AI so risk analysts can test models and data themselves without relying on engineering or data science teams.
- The production cluster handles about 100,000 events per second and around 350 million requests per day.
- Tabby serves more than 20 million registered shoppers and 40,000 active sellers.
- Annualized sales exceed $14 billion across the region.
- The platform scales through seasonal spikes with minimal disruption and improved traffic management.
Architecture
Built on Google Kubernetes Engine, Pub/Sub, Cloud SQL, BigQuery, Bigtable, and Vertex AI; the article describes both the production infrastructure and the analytics/ML workflow used by non-technical analysts.
Sources & evidence1
Evidence: Medium50/100Evidence strength
- Customer explicitly identified
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: Jan 1, 2026Publisher: Google CloudEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
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