Your crash reporter tells you the app crashed. It won't tell you your CoreML model's confidence score drifted 12% on iPhone 12s running hot.
Wild Edge is the only monitoring platform built for inference running on the hardware you ship — not on a server you rent.
Free up to 10k MAU · No credit card required · 5-minute SDK setup
"We shipped a model update and didn't notice our INT8 variant was silently degrading on Samsung Exynos devices. Wild Edge caught the drift in 48 hours — before it rolled out to the full Android fleet."
There are hundreds of tools for monitoring a model on an AWS server. None built for a model running inside 5 million iPhones.
Crash reporters and app monitoring tools tell you the app crashed. They won't tell you your model's confidence score for Class A has drifted 12% over the last 48 hours. You'd have to build that detection logic yourself.
Server-side observability tools expect you to stream raw feature vectors to their API. In mobile, sending raw images or sensor logs from a million devices destroys your cloud bill — and kills the user's data plan.
The SDK captures rich inference telemetry — outcomes, latency, confidence — while keeping your users' actual content private. No images, no audio, no text ever leaves the device.
The SDK captures inference outcomes, confidence scores, latency, hardware events, and input statistics — but never the raw inputs themselves. For images, only brightness and blur stats. For text, only token counts and language.
Events are buffered locally and synced in batches — on a schedule or when the app backgrounds. No raw images, no raw audio, no raw text. Ever. What reaches the server is structured telemetry about model behaviour, not user content.
Wild Edge aggregates summaries from across your fleet, runs drift detection, and alerts you the moment something goes wrong — broken down by device model, OS version, quantization format, and hardware accelerator.
Framework integrations patch your runtime at init time. Your inference code stays exactly as it is — no log calls, no manual timers, no changes to model loading.
import WildEdge
// That's it. configure() swizzles MLModel.prediction(from:) at runtime.
// Every CoreML model in the app is instrumented — including third-party SDKs.
WildEdge.configure(apiKey: "we_live_iddqd")
// Use CoreML exactly as before — nothing else to change
let model = try YOLOv8n(configuration: .init())
let result = try model.prediction(input: features)
// ↑ latency, confidence, Neural Engine vs CPU fallback — all capturedNo log calls scattered through your code · No raw data leaves the device · Works offline
Wild Edge shows you what generic APM tools can't — the intersection of ML performance and real-world hardware.
"Your model is 40% slower on iPhone 12 vs iPhone 13 due to Neural Engine limitations." Break down every metric by device model, accelerator, and OS version.
"Prediction accuracy drops when the phone is over 40°C because the GPU is being throttled." Catch the invisible performance killer hiding in your users' pockets.
"Your INT8 model is drifting faster than your FP16 version in production." Compare model variants side-by-side across real device fleets.
Track tokens/sec, time-to-first-token, KV cache usage, and context utilization for GGUF, CoreML, and ONNX language models running on-device.
Automatic drift detection across confidence scores, label distributions, and input statistics. Get alerted before accuracy degradation reaches your users.
"Is v2.1 actually better than v2.0 on the real fleet?" Tag model versions and compare drift rates, latency, and confidence side-by-side across device cohorts. Ship updates with data, not hope.
We never see the user's images, audio, or text. Only the statistical shape of model performance. Pass your Apple ATT audit without breaking a sweat.
Also: SNPE / QNN · OpenVINO · MediaPipe · NCNN · any custom C runtime via the C SDK
Shipping ML on a Jetson Orin, Raspberry Pi 5, or a Cortex-A MCU? Wild Edge's C SDK and Python client work on bare Linux, RTOS, and store-and-forward environments where there's no persistent connection.
See inference latency and accuracy segmented by which accelerator actually ran the model — Hexagon DSP, Mali GPU, or fallback CPU.
Events buffer locally in SQLite and flush when connectivity is available — whether that's WiFi, cellular, or a daily USB sync.
Your model may run fine on the dev board but degrade on production hardware. Wild Edge surfaces that before a firmware OTA rolls out.
Because the compute happens on the user's device, our infrastructure costs are minimal — and we pass those savings on to you. One model, one line item.
Do you know how it's performing right now? Set up Wild Edge in 5 minutes and find out.
Free up to 10k MAU · No credit card · SDK for iOS, Android, and Linux