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Bring intelligence to the edge — where cloud latency is not an option.

Edge Deploy

Compresses and deploys ML models to edge devices, IoT gateways, and network equipment for ultra-low-latency inference where cloud round-trips are unacceptable. Critical for industrial IoT, telecom network element inference, and healthcare monitoring devices in low-connectivity environments.

Why It Matters

EdgeDeploy takes ML to the places the cloud can’t reach — factory floors, network elements, and medical devices where a round-trip to a data center is too slow, too unreliable, or impossible.
Model compression and OTA update pipelines make edge inference deployable and maintainable at fleet scale, not just in one-off pilots.
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Latency and connectivity independence

Ultra-low-latency inference runs on the device itself — industrial safety systems, network equipment, and patient monitors keep working when connectivity doesn't.

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Fleet-scale model management

OTA update pipelines — missing from centralized MLOps platforms — mean thousands of deployed devices get model improvements without field visits or manual reflashing.

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Right-sized models for constrained hardware

ONNX Runtime, TensorFlow Lite, and OpenVINO compression squeeze production-grade accuracy into the compute and power budgets of real edge devices.

The Cloudly Advantage

EdgeDeploy extends Cloudly’s reach beyond the data center into operational technology — the domain where our telecom, manufacturing, and healthcare targets run their core business.
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Telecom vertical sharp edge

Network element inference is a use case cloud-centric vendors can't serve — a distinctive opening into telecom operators' network engineering budgets, beyond their IT departments.

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Industrial IoT and manufacturing entry

Edge ML on factory equipment opens a client segment our data-center products alone can't reach — expanding the addressable market for the whole portfolio.

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Hybrid story completion

Cloud training (TrainForge) plus edge serving (EdgeDeploy) makes Cloudly's cloud-agnostic, hybrid positioning fully concrete — no hyperscaler tells this story neutrally.

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Greengrass pulls AWS services

AWS IoT Greengrass deployments create IoT-flavored AWS Migration and Monitoring engagements — a fresh angle in accounts where standard cloud services are already covered.

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Fleet operations recurring revenue

Managing OTA model updates across device fleets is an ongoing managed service by nature — revenue scales with every device the client deploys.

The Final Takeaway

Healthcare monitoring wedge — Low-connectivity patient monitoring aligns with health-authority mandates for local processing — pairing naturally with our governance suite for regulated medical deployments.

Powered By

ONNX Runtime

Core to the EdgeDeploy technology stack.

TensorFlow Lite

Core to the EdgeDeploy technology stack.

AWS IoT Greengrass

Core to the EdgeDeploy technology stack.

GCP Edge TPU

Core to the EdgeDeploy technology stack.

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