Private Cameras
Analyze cameras behind a firewall with EyePop cloud inference
Use this path when cameras stay on a private network but inference runs in EyePop cloud. It is different from an on-premise runtime, which runs inference inside your infrastructure.
Choose an integration
VMS plugin
The plugin pushes MPEG-TS over HTTPS
Customer
A VMS with a supported plugin runtime
Customer connector
A customer service pushes MPEG-TS over HTTPS
Customer
A custom integration without a VMS plugin
Managed Tailscale
The cloud worker reads RTSP across the tailnet
Customer
A managed, private network integration
No pattern requires an inbound firewall port: video is either pushed outbound from inside the network, or carried over a mutually authenticated VPN overlay. With cloud inference, encoded video leaves the premises over TLS or WireGuard. Choose an on-premise runtime if video must be processed locally.
Connect a camera on a private network
A camera on a private LAN has no route in from outside it, and closing that gap is usually the hardest part of the integration. Port forwarding exposes the camera to the internet. A hand-rolled VPN or reverse tunnel works, but it is yours to run and keep up. On-premise installs hit a third problem: a container on a Docker bridge network cannot reach a LAN camera without host networking or an explicit route.
Tailscale is the shortest way through. Install it on the camera's network and on whatever reads the stream, and both sides sit on one tailnet — a stable address, no inbound firewall rule, no port forwarding.
Setting up the tailnet is yours to do today; the CLI does not configure it for you. For the managed pattern above, where an EyePop cloud worker reads RTSP across your tailnet, contact support@eyepop.ai.
Integration requirements
One persistent session per camera is the recommended default for failure isolation.
Push integrations send H.264 in MPEG-TS (
video/mp2t) and receive JSONL predictions.The customer owns prediction storage, reconnect and retry, and operation of the on-premise component in every pattern.
Confirm network egress, bandwidth, retention, region, and tailnet ACL requirements during deployment design.
Next steps
Deployment — the persistent session a camera stream attaches to
On-Premise — open the On-Premise section in this site's navigation to run inference locally instead of sending video out
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