February 15, 2026 · Alocity Team
How Edge AI Is Rewriting the Economics of Video Surveillance
Edge AI moves video analytics from the cloud to local NVIDIA-powered hardware, cutting bandwidth and storage costs. Here's what it means for security teams.
- edge AI video surveillance
- on-premise video analytics
- NVIDIA edge AI
- cloud video costs
- AI NVR
Industry coverage is tracking a clear shift in edge AI video surveillance: analytics are moving off the cloud and onto local, NVIDIA-powered hardware. Here is why that matters for anyone budgeting for cameras.
The hidden cost of cloud-only analytics
Streaming every camera to the cloud for AI processing means paying for upload bandwidth, cloud compute and storage, every month, for every camera. As camera counts grow, those costs grow in step. Latency and internet outages add operational risk on top.
What changes at the edge
Edge AI runs inference next to the cameras. Platforms such as NVIDIA Jetson were built for exactly this kind of on-device video analytics. The result:
- Lower bandwidth: only events, metadata and selected clips leave the site.
- Faster alerts: detections happen in milliseconds, not round trips.
- Resilience: analytics keep running if the internet drops.
- Reuse existing cameras: add AI without replacing working hardware.
Where Alocity fits
Alocity's AIVR recorders process video locally on NVIDIA hardware, then sync to the cloud for search, sharing and multi-site management. It is the hybrid model the coverage describes: edge for intelligence, cloud for access. Read how the recorders were introduced in the AIVR launch announcement, and see how natural-language search works across sites once footage is indexed.
Compare the costs for your sites
Talk to our team about an edge AI cost comparison for your sites.