Dating App Kubernetes Platform: Scaling to 600+ Pods with ARM64 Graviton and Karpenter Auto-Scaling
Built consumer-scale EKS platform for Alyke dating app with Karpenter provisioning, ARM64 Graviton nodes saving 40% on compute, and geographic recommendation services scaling to 600+ pods.
Key Results
Pod capacity during peak matching hours
Compute savings with ARM64/Graviton
Time to scale from baseline to peak
Geographic recommendation zones
What We Were Solving
Alyke is a personality-based dating app that matches users based on compatibility scores, interests, and location. Their recommendation engine and real-time matching algorithms required massive compute resources during peak evening hours when dating app usage spikes dramatically.
Scaling challenges:
- Recommendation engine needed to process personality compatibility and location-based matching in real-time for millions of users
- Traffic spikes of 10-15x during peak hours (evenings and weekends) required elastic scaling without manual intervention
- Geographic sharding needed for location-based recommendations without introducing latency for local matches
- Cost optimization critical for a venture-backed startup scaling to millions of users — infrastructure costs directly impacted runway
- Node provisioning taking 10+ minutes was too slow for sudden traffic surges during viral marketing campaigns
How We Solved It
We designed a high-scale Kubernetes platform with intelligent auto-scaling and cost-optimized compute that handles massive traffic variability while minimizing cloud spend.
ARM64 Cost Optimization
Built the EKS cluster with ARM64/Graviton nodes, achieving 40% compute cost savings compared to equivalent x86 instances. Cross-compiled all application containers to support ARM architecture natively.
Karpenter Auto-Scaling
- Implemented Karpenter for just-in-time node provisioning — scales from baseline to 600+ pods in under 2 minutes
- Configured Spot instance pools with automatic fallback to on-demand, achieving 70% Spot coverage
- Set up HPA with 60% CPU threshold for responsive horizontal pod scaling
Geographic Recommendation Architecture
- Designed 4 regional services (SC1-SC4) each scaling independently from 12-150 pods based on regional demand
- Regional cron job system for batch processing matches across geographic zones during off-peak hours
Load Testing & Validation
Implemented Locust-based load testing framework to validate capacity planning and ensure the platform handles 10x traffic spikes without degradation.
Technologies Used
“Our recommendation engine needs to process millions of potential matches in real-time. The infrastructure handles our evening traffic spikes without breaking a sweat, and the Graviton nodes cut our AWS bill significantly. We can focus on building features instead of worrying about scaling.”
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