Build, Train & DeployAI for Every Edge Device
Collect sensor data, build production-ready AI models, optimize them for embedded hardware, deploy to edge devices, and monitor them in real time — all from one intelligent platform.
- No credit card
- Enterprise Ready
- GPU Training
- Open Source Compatible
One Platform. Endless AI Possibilities.
Every stage of the edge AI lifecycle — from raw signal to monitored fleet — lives in a single, connected workflow.
Data Collection
Ingest images from any device or upload.
Build Dataset
Dataset management with versioning, AI labeling, and precise annotation tools.
Train Models
Object Detection (YOLO-Pro, FOMO, MobileNetV2 SSD)
Optimize
int8 Quantization, Float32 for embedded targets.
Deploy
TensorFlow Lite, Arduino, Raspberry Pi.
Monitor
Real-time device and model performance monitoring across your entire fleet.
Everything you need to ship edge AI
A complete toolkit for the entire lifecycle, engineered for teams that move fast without cutting corners.
Smart Dataset Management
Organize, filter, and split datasets with an explorer built for scale.
Automatic AI Labeling
Bootstrap annotations with AI-assisted labeling and human-in-the-loop review.
GPU Training
Accelerated training jobs with live loss and accuracy telemetry.
Dataset Versioning
Track every change with immutable, reproducible dataset versions.
Real-time Device Monitoring
Watch inference latency, throughput, and health across your fleet.
One-click Deployment
Ship optimized builds to any supported target in a single action.
Model Testing & Evaluation
Confusion matrices, per-class metrics, and live classification testing.
Train Any Edge AI Model
From vision to sound to motion — pick an architecture, train on GPU, and optimize for your target in minutes.
Object Detection
Locate and classify multiple objects with bounding boxes.
YOLO-Pro
High-accuracy real-time detection tuned for edge throughput.
FOMO
Faster Objects, More Objects — tiny centroid detection for MCUs.
MobileNetV2 SSD
Single-shot detection with an efficient mobile backbone.
Manage Everything in One Dashboard
Dataset Explorer
Browse, filter and inspect every sample.
Training Jobs
Queue GPU jobs and watch them converge live.
Model Metrics
Accuracy, loss, and per-class breakdowns.
Model Testing
Validate against held-out test data.
Deployments
Track every build across every target.
Device Fleet
Monitor connected devices in real time.
Deploy Anywhere
Export optimized builds for the real hardware your product runs on — no lock-in, no rewrites.
Raspberry Pi 4
.pxe package
UNO Q
.pxe package
TensorFlow Lite
Cross-platform
The complete edge AI stack
Most tools cover a slice of the lifecycle. Petal Edge covers all of it.
Petal Edge
Typical Tools
Simple, scalable pricing
Start free and grow into production. No credit card required to begin.
For individuals exploring edge AI.
- 1 project
- Community GPU queue
- Image & audio models
- TensorFlow Lite export
- Community support
For teams shipping to production.
- Unlimited projects
- Priority GPU training
- All model architectures
- Every deploy target + .pxe
- AI labeling & versioning
- Fleet monitoring
- REST API access
For organizations at scale.
- Everything in Professional
- SSO & RBAC
- Dedicated GPU capacity
- Audit logs & data isolation
- SLA & priority support
- On-prem / VPC option
Frequently asked questions
Petal Edge is an end-to-end enterprise Edge AI platform. It covers the full lifecycle: collecting sensor data, building and labeling datasets, training and optimizing models, deploying to edge devices, and monitoring your fleet — all in one product.
Image Classification, Object Detection (including YOLO-Pro, FOMO, and MobileNetV2 SSD), Audio Classification, and Motion / Sensor Classification — with efficient mobile backbones tuned for embedded hardware.
Yes. Bring your own datasets, choose an architecture, and train on GPU with live loss and accuracy telemetry. You can version datasets and re-train reproducibly at any time.
Absolutely. Training jobs run on accelerated GPU infrastructure, with priority queues on paid plans and dedicated capacity available for Enterprise.
Yes. Export optimized builds for Arduino (Nano 33 BLE), ESP32, Raspberry Pi 4, UNO Q (.pxe), TensorFlow Lite, and native C++/Linux — with int8 quantization and EON-style compilation.
Start Building Production-Ready Edge AI Today
Join the teams shipping intelligent products to every edge device.