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 AI running inside devices — not on servers. Set it and forget it.
Free up to 10k MAU · No credit card required · 5-minute SDK setup
Built for teams shipping AI on
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.
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.
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.
With Apple's App Tracking Transparency, developers are terrified of sending user data to third-party servers. Wild Edge's edge-summarization architecture means we never receive images, audio, or text — only anonymous statistical distributions.
For images, we only store brightness histograms and blur scores — never pixels.
Device identifiers are SHA-256 hashed with an app-specific salt on the client.
Buffers locally in SQLite, syncs opportunistically. No data loss on poor connectivity.
Because the compute happens on the user's device, our infrastructure costs are minimal — and we pass those savings on to you.
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