Running inference on every camera individually gets expensive and hard to manage past a handful of units. Consolidating multiple camera feeds onto a single edge gateway reduces per-camera cost and centralizes management, but it introduces a new problem: making sure the gateway's processing budget doesn't get overwhelmed. Here's a practical deployment sequence.

Step 1: Calculate Your Processing Budget First

Before connecting a single camera, total up the expected frame rate and resolution across every feed you plan to run through the gateway, and check that figure against the gateway's rated throughput - an EdgeForce gateway's specification sheet lists throughput in operations per second, which you can roughly map against your model's per-frame cost. Underestimating this step is the most common cause of dropped frames and inconsistent alert timing after deployment.

Step 2: Segment Cameras by Priority

Not every camera feed needs the same frame rate. A shelf camera from VisiTech tracking slow-moving inventory changes can run effective analysis at a much lower frame rate than an Optisight entry-counting camera at a busy doorway. Configure lower priority feeds to sample less frequently, freeing processing headroom for the feeds where timing actually matters.

Step 3: Network Topology

Run cameras to the gateway over PoE where possible - it simplifies cabling and avoids the reliability issues that come with WiFi-connected cameras dropping frames under network congestion. Keep the gateway's uplink to your central system on a separate network segment from the camera feeds themselves, so a burst of camera traffic doesn't compete with the alerts and metadata you're trying to send upstream.

Deployment Checklist

Confirm total camera bandwidth doesn't exceed the gateway's rated ingest capacity
Assign frame-rate priorities per camera based on how time-sensitive its alerts actually are
Separate camera-feed traffic from upstream reporting traffic on the network
Set up local buffering so brief network interruptions don't drop analytics events entirely

Step 4: Model Assignment and Load Testing

Assign models to camera feeds based on the gateway's remaining capacity after your highest-priority feeds are accounted for, and run a load test with all cameras active simultaneously before going live - many issues that don't appear with two or three test cameras only surface once every planned camera is streaming at once. Watch for processing queue backlog specifically, since it's a leading indicator of an overloaded gateway well before frames actually start dropping.

Step 5: Monitor and Rebalance

Once live, monitor per-camera latency rather than just overall gateway health - a single misbehaving camera feed (often one with a lens issue causing constant false detections) can consume a disproportionate share of processing budget and degrade performance for every other feed on the same gateway. Rebalancing which cameras share a gateway, rather than simply adding more compute, is often the more cost-effective fix.

A well-planned multi-camera gateway deployment scales more cheaply than one inference device per camera, but only if the processing budget is planned upfront rather than discovered through trial and error after installation.

Field notes on intelligent hardware

How to Set Up a Retail Shelf-Camera System

How to Deploy an Edge AI Gateway for Multi-Camera Retail Analytics

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