Mine the hard cases
Filter production events to low confidence, user corrections, or a model disagreeing with the one that took over from it.
WildEdge vs Qualcomm AI Hub
- Good for
- Pre-production benchmarking and profiling, on phones, Windows-on-Arm PCs and embedded boards.
- Limits
- Pre-production, Qualcomm’s own device farm, Qualcomm chips only.
WildEdgeLive production monitoring | Qualcomm AI HubPre-production benchmarking |
|
|---|---|---|
| Stage | Development and production, on the same instrumentation | Pre-production only |
| Devices | The real devices your users hold | Cloud-hosted Qualcomm device farm |
| Models | Custom & trained models, self-hosted LLMs, cloud LLMs, agentic workflows | PyTorch, TorchScript, ONNX & TFLite, compiled for Qualcomm targets |
| Platforms | Python, Android, iOS | Snapdragon phones, Windows-on-Arm PCs, embedded boards |
| Measures | Latency, confidence drift, error rates, thermal state and CPU throttling, broken down by device cohort | Layer-level latency, memory use and accelerator utilization |
| Coding agent access | An MCP server with scoped tokens and an audit trail. Ask your coding agent why a model regressed and it queries the events itself | None. Results are read in the web console or the Python client |
| What leaves your control | Structured telemetry only. Raw inputs stay on the device by default | Your model, uploaded to Qualcomm’s cloud |
| Availability | Available now, instrument in minutes | Public and free, Qualcomm silicon only |
Datasets
No data flywheel
A lab fleet never produces the cases your model gets wrong. Real users do, and those are the ones worth training on.
Correct the labels
Override what the model predicted, in place, keeping the record of what was changed and by whom.
Hand it to training
Integrate your training pipelines against a manifest naming an immutable set of records. The dataset and the model stay reproducible.
Get started
Know where your AI struggles in the real world.
Add the SDK and watch real inference across the devices, runtimes and providers you already use.
Competitor details reflect their publicly documented capabilities at the time of writing. If something here is out of date, tell us at contact@wildedge.dev and we will correct it.