Back to Home

How Geo-Distribution Works

📍 Before: Single Region

Traditional infrastructure deploys everything in one location (e.g., US East). Users far from this region experience high latency due to physical distance.

  • One data center location
  • High latency for distant users
  • No regional redundancy
  • Limited capacity scaling

🌍 After: Geo-Distributed

Cachee automatically deploys your infrastructure across 497 edge locations globally. Users connect to the nearest location for minimal latency.

  • 497 edge locations worldwide
  • Sub-30ms latency globally
  • Automatic failover & redundancy
  • Infinite capacity scaling

⚡ Performance Impact

86% average latency reduction globally. Users in Asia, Europe, South America, and Africa see the most dramatic improvements—from 200-300ms down to under 30ms.

  • Asia: 245ms → 19ms (92% faster)
  • Europe: 125ms → 15ms (88% faster)
  • Africa: 345ms → 35ms (90% faster)
  • South America: 285ms → 22ms (92% faster)

🤖 How It Works

Cachee's AI analyzes your traffic patterns, user locations, and data compliance requirements to automatically deploy your infrastructure optimally.

  • AI-driven location selection
  • Automatic traffic routing
  • Smart caching strategies
  • Zero manual configuration

Technical Implementation

Our geo-distribution engine uses anycast routing to direct users to the nearest edge location. Each location runs a full stack: Redis for hot data, MongoDB for warm data, and intelligent tiering to S3 for cold data. Data is automatically replicated based on access patterns, with hot data kept in multiple regions and cold data stored cost-effectively in a single region.


Our ML models predict traffic patterns and pre-warm caches in regions before demand spikes. Compliance requirements (GDPR, data residency) are automatically enforced at the infrastructure level—no application code changes needed.


The entire system deploys in 30 seconds with a single API call. No terraform, no manual configuration, no DevOps overhead. Just instant global scale.