Cloud vs. Local: Why Onlyface Outperforms Traditional Face Swap Tools
We break down the technical differences between cloud-based and GPU-local face swapping, and why latency numbers tell only half the story.
Face-swap benchmarks usually lead with latency, and the local-GPU tools usually win that benchmark. When the model and the video are on the same machine, the data never has to cross a network. A modern consumer GPU can run an inference pass inside the budget of a single video frame, and the rest is frame-buffer bookkeeping.
That number, though, is only half the story. Local face swap trades latency for three structural costs that don't show up in a benchmark table: hardware cost, operational complexity, and concurrency.
Hardware cost is the obvious one. A GPU capable of running a modern face-swap model at 1080p is a meaningful investment. For a creator, that investment only pays off if they can amortise it across enough streaming hours. For a team wanting to give every employee a swap-enabled meeting face, it does not amortise at all.
Operational complexity is the quieter cost. Native face-swap applications depend on specific CUDA versions, specific driver builds, specific model file formats, and specific virtual-camera shims. When any one of those components updates out of lockstep, the pipeline breaks, and the creator has to debug a stack they didn't build. Onlyface users never see this stack because it lives on our servers.
Concurrency is the cost that only matters once a team tries to scale. A local GPU can run exactly one face-swap session at a time. A Onlyface GPU pool can run many — the concurrency ceiling is a capacity-planning problem for us, not a hardware-purchase problem for the creator.
What about the network latency Onlyface adds? On a well-connected residential link in most of the world, a frame round-trip over WebRTC to a nearby GPU is well inside the frame budget that live video already tolerates. Voice calls, remote collaboration, cloud-rendered games — all of them run inside the same envelope, and viewers don't perceive the added delay.
The trade-off is therefore not "fast local vs. slow cloud". It's "a fast pipeline you have to maintain" versus "a fast pipeline someone else maintains, delivered by the same transport that every video meeting already uses." For creators whose core skill is content rather than GPU driver administration, the second offer is the better deal.
There are still cases where local face swap wins unambiguously: air-gapped installations, cinema VFX rigs where deterministic offline rendering matters, and research pipelines where the model itself is the deliverable. Onlyface is not trying to be the answer for those. Onlyface is the answer for live video — and live video is where the cloud architecture most obviously pays off.