LLM Inference at the Edge
Fintech
Healthtech
LegalTech
Education
Customer Service
Challenges with Public Cloud
High latency caused by distant regional data centers
Expensive egress and model serving costs
Limited control over data and compliance exposure
Infrastructure either oversized or underused
What Changes with Redsand
AI inference runs closer to users, within the application or point of interaction
Data remains under enterprise control with no third-party access or hidden layers
Flexible infrastructure designed to match actual usage and reduce cost per model call
Sample Use Cases
Data remains under enterprise control with no third-party access or hidden layers
Inference nodes placed inside branch networks such as banks or clinics
On-campus hosting for education platforms requiring real-time content generation
Benefits
Lower latency compared to centralized cloud-based LLMs
Significant reduction in cost per inference with consistent performance
High uptime and reliability for real-time assistants in sensitive environments
