CASE STUDY
The client is a security and surveillance company leveraging AI to automate face detection and identification from real-time CCTV footage. Their AI-powered video analysis system processes high-resolution video streams to enhance safety and situational awareness.
09 Jan 2025
5 minutes
Introduction
The company’s AI-based surveillance system faced multiple challenges as it scaled to monitor more locations and higher volumes of video feeds.
The primary issues included:
Heavy Computational Workload: Processing high-resolution video streams in real time required significant GPU power.
Limited Scalability: GPU availability constraints with the existing provider led to delays during high-demand periods.
Expensive GPU Allocation: Paying for dedicated GPU instances during low-demand periods inflated operational costs.
These challenges hampered the company’s ability to deliver reliable, cost-efficient, and scalable surveillance solutions.
By adopting Toystack AI, the company achieved:
50% cost reduction through dynamic scaling.
3X faster inference times, improving response efficiency.
5X increased processing capacity for analyzing video feeds from more CCTV cameras.
This case study explores how Toystack’s GPU infrastructure helped optimize costs and improve performance for the company’s surveillance system.
What the problem was ?
How We Helped
ToyStack's Impact
Conclusion
By migrating to Toystack AI, the company transformed its AI-powered surveillance system into a cost-efficient and scalable solution.
Latency reduced to under 800 milliseconds, ensuring real-time monitoring.
GPU costs decreased by 50%, enabling sustainable scaling.
Increased processing capacity allowed expansion to new locations.
Looking Ahead: With Toystack’s support, the company is poised to further scale its surveillance operations and continue enhancing public safety through innovative AI solutions.
