Don't let your
model die at
the edge.

Your crash reporter tells you the app crashed. It won't tell you your model's confidence score drifted 12% on iPhone 12s running hot, silently degrading inference across your fleet while CI stays green.

Wild Edge monitors inference where it actually runs: on the device. Not on a server you rent.

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No credit card required · 5-minute SDK setup

Drift Alert Detected

yolov8n · iOS & Android fleet · 2 hours ago

Avg Confidence Score ↓ −12.4%

14-day avg: 94.7% Now: 82.3%

By Device

iPhone 12

72% · 28ms

Galaxy S22

79% · 14ms

iPhone 14

94% · 9ms

Pixel 8

93% · 11ms

iPhone 15 Pro

96% · 7ms

Likely cause: Thermal throttling

31% of iPhone 12 devices exceeded 40°C in the last 2h. Neural Engine throttled, falling back to CPU.

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The Mobile MLOps Gap

Your current tools weren't
built for this.

There are hundreds of tools for monitoring a model on an AWS server. None built for a model running inside 5 million edge devices.

The Generalist Problem

General-purpose APM doesn't speak ML

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.

No concept of confidence score drift

No hardware-aware latency breakdown

No quantization loss tracking

The Server-First Problem

Server-first MLOps wasn't designed for mobile

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 blows up your cloud bill and kills the user's data plan.

Designed for raw feature vector uploads

1M events/day = punishing cloud costs

Fails Apple ATT privacy requirements

The Architecture

The brain lives on the device.

The SDK captures inference outcomes, latency, and confidence. Your users' images, audio, and text never leave the device.

Step 1 — On Device

SDK instruments every inference

The SDK captures inference outcomes, confidence scores, latency, and hardware events. Not the raw inputs. For images, only brightness and blur stats. For text, only token counts and language.

Step 2 — Sync

Batched, privacy-safe sync

Events buffer locally and sync in batches, on a schedule or when the app backgrounds. What reaches the server is structured telemetry about model behaviour. No images, audio, or text. Ever.

Structured telemetry only

Step 3 — Dashboard

Know before your users do

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.

5-minute integration

Instrument once.
Never log again.

Framework integrations patch your runtime at init time. Your inference code doesn't change.

InferenceManager.swift

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 changes.
let model = try YOLOv8n(configuration: .init())
let result = try model.prediction(input: features)
// ↑ latency, confidence, Neural Engine vs CPU fallback: all captured

import dev.wildedge.WildEdge
import dev.wildedge.integrations.tflite.track

// 1. Configure once in Application.onCreate()
WildEdge.configure(apiKey = "we_live_idkfa")

// 2. Add .track() at construction. One word, nothing else changes.
val interpreter = Interpreter(modelBuffer, options).track("yolov8n-v2")

// 3. Use interpreter exactly as before. Telemetry is automatic.
interpreter.run(inputBuffer, outputBuffer)
// ↑ latency, Hexagon DSP vs CPU, NNAPI delegate: all captured

import timm
import torch
import wildedge

client = wildedge.WildEdge(app_version="1.0.0")
client.instrument("timm")

model = timm.create_model("resnet18", pretrained=True)
model.eval()

with torch.inference_mode():
    output = model(batch)
# ↑ load timing, per-call latency, CPU / CUDA / MPS

import torch
import wildedge

client = wildedge.WildEdge(app_version="1.0.0")

model = client.load(MyModel)
model.eval()

with torch.inference_mode():
    output = model(batch)
# ↑ per-call latency, output tensor stats, CPU / CUDA / MPS

from llama_cpp import Llama
import wildedge

client = wildedge.WildEdge(app_version="1.0.0")
client.instrument("gguf")

llm = Llama("Llama-3.2-1B-Instruct-Q4_K_M.gguf", n_ctx=2048, n_gpu_layers=-1)
result = llm("Write a haiku about on-device AI.", max_tokens=128)
# ↑ tokens/sec, latency, GPU layers: all captured

import onnxruntime as ort
import wildedge

client = wildedge.WildEdge(app_version="1.0.0")
client.instrument("onnx")

session = ort.InferenceSession("model.onnx")
outputs = session.run(None, {"pixel_values": batch})
# ↑ latency, confidence, hardware: auto-captured on every call

# Zero code changes. Preload interposes TFLite / ONNX at the linker level.
# Works on Jetson, RPi, x86: any Linux with a dynamic runtime.

$ LD_PRELOAD=libwildedge_tflite.so ./my_app
# ↑ TfLiteInterpreterInvoke() is intercepted via dlsym(RTLD_NEXT, ...)
# ↑ latency, output scores, thread: captured for every inference call

# Or bake it into your launch script / systemd unit:
Environment="LD_PRELOAD=/usr/lib/libwildedge_tflite.so"
Environment="WILDEDGE_API_KEY=we_live_rosebud"

#include "wildedge.h"

/* No dynamic linker on RTOS. Use the handle pattern instead. */

/* 1. Configure once at firmware init */
we_configure("we_live_1up1up", WE_TRANSPORT_UART);

/* 2. Register model with its scoring strategy */
we_model_t *mdl = we_model_register(
    "wake_word_v3", my_model_run,
    WE_SCORE_ARGMAX(output_buf, NUM_CLASSES)
);

/* 3. Call through the handle. Timing and telemetry automatic. */
we_model_run(mdl, input_buf, output_buf, INPUT_LEN);

No log calls scattered through your code · No raw data leaves the device · Works offline

Built for on-device ML

Not just latency.
Answers.

Generic APM tools see request latency. Wild Edge sees what your model actually does on each piece of hardware your users carry: latency, confidence scores, drift, quantization loss, and thermal effects, sliced by device, OS, accelerator, and model version.

Model × Hardware Matrix

"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.

Thermal Correlation

"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.

Quantization Loss Tracking

"Your INT8 model is drifting faster than your FP16 version in production." Compare model variants side-by-side across real device fleets.

On-device LLM Telemetry

Track tokens/sec, time-to-first-token, KV cache usage, and context utilization for GGUF, CoreML, and ONNX language models running on-device.

Confidence & Distribution Drift

Automatic drift detection across confidence scores, label distributions, and input statistics. You'll know when accuracy starts slipping, not after your users do.

For PMs

Model Version A/B

"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.

Works With Your Data Warehouse

Your inference data flows directly into your existing analytics stack. Correlate model performance with business outcomes like revenue, churn, and support volume, without leaving your data warehouse.

Privacy by Design

We never see the user's images, audio, or text. Only the statistical shape of model performance. ATT audits pass. There's nothing to disclose.

ATT-compliant by architecture

Telemetry stays structured and under your control

Works with your runtime

CoreML

iOS / macOS

TFLite

Android / Linux

ONNX Runtime

Cross-platform

GGUF

On-device LLMs

ExecuTorch

Meta / mobile

TensorRT

NVIDIA / Jetson

Also: SNPE / QNN · OpenVINO · MediaPipe · NCNN · any custom C runtime via the C SDK

Embedded & Firmware

Not just mobile.
Any edge.

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.

NPU / DSP / CPU breakdown

See inference latency and accuracy per accelerator: Hexagon DSP, Mali GPU, or fallback CPU. Know which path your model actually took.

Offline-first, store-and-forward

Events buffer locally and flush when connectivity comes back.

Fleet variance across hardware SKUs

Your model may run fine on the dev board but degrade on production hardware. You'll know before you push the OTA.

object_detector · Jetson Orin fleet

v4 · 847 devices · synced 4 min ago

Avg Confidence Score ↓ −1.8%

14-day avg: 95.2% Now: 93.4%

By Device

Orin NX 16GB

96.2% · 8.7ms

Orin NX 8GB

95.8% · 9.1ms

AGX Orin

97.1% · 6.3ms

Orin Nano

93.4% · 31ms

Orin Nano has no DLA. Runs on GPU only.

57 units running 3.4× slower than the rest of the fleet.

Private Beta Pricing

Free to start.
Custom when you're ready.

Start free. Talk to us when you outgrow it.

Free

$0

Forever, no credit card

1 model
7-day data retention
Drift & latency alerts

Get Started

Custom

For teams that need more

Unlimited models
Custom data retention
Dedicated support
On-prem deployment option

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Team

The Humans Behind Wild Edge

Bold builders who love taking edge ML from idea to production with clarity and care.

BB

BigBoss

Leader

A known leader with a huge soul, always feeling the pull of adventures.

TO

TheOptimist

Builder of Momentum

Changes reality to match his mind, while looking after a squad of engineers.

YE

Your Favourite Engineer

Automation Fan

Passionate about building and shipping, loves when things get done automagically.

Your model is in
5 million pockets.

Do you know how it's performing right now? Set up Wild Edge in 5 minutes and find out.