Does the tool assume fanless deployment is a hard constraint?

Yes. If fanless or sealed deployment is selected, the recommendation logic favors lower-power platforms that are more realistic for passive cooling and enclosure-limited environments.

Find the right hardware for your deployment

Input your deployment constraints. The engine calculates compute requirements, power envelope, and ranks best-fit edge AI platforms with purchase links.

// Define requirements

Workload

Scenario

Single Camera Vision

Multi-Camera Analytics

Industrial Inspection

Low Power AI Sensor

Edge AI Server

Robotics / Autonomous

Task

Classification

Object Detection

Segmentation

Pose Estimation

Multi-Model Pipeline

Streams

Constraints

Power

≤ 5W Ultra-Low

5–10W Low

10–25W Moderate

25W+ Performance

Resolution

720p

1080p

Environment

Fanless / Sealed

Active Cooled

Industrial Temp Range

Mobile Robot

Edge Server / Rack

Budget Sensitivity

Lowest Cost

Balanced

Highest Performance

Performance

Latency

Batch / Offline OK

Near Real-Time

Real-Time Critical

Advanced Options

Ambient Temp

25°C (indoor)

35°C (warm indoor)

45°C (outdoor)

55°C (industrial)

Compliance

None

IP67 (dust/water)

MIL-STD-810G

ATEX Zone 2

// Select required parameters to continue

Select one option from each category above

// Calculating optimal configuration…

// Recommendation

// Primary Recommendation

—

MATCH CONFIDENCE —

📊 View Specs Deployment Checklist

// Alternative options

// machine-readable output — application/json

What this Hardware Selector decides

This tool recommends the best-fit edge AI compute platform based on deployment scenario, model type, stream count, power budget, environment, and supporting constraints such as resolution, budget sensitivity, latency, thermal profile, and compliance requirements. It is designed for engineers evaluating Jetson, Coral TPU, Hailo-8, RK3588, and AGX Orin-class hardware for real deployments.

// Inputs considered

Scenario

Single-camera vision, multi-camera analytics, industrial inspection, low-power sensor, edge server, or robotics.

Model Type

Classification, object detection, segmentation, pose estimation, or multi-model pipelines.

Streams + Constraints

Concurrent video streams, power envelope, and deployment environment determine realistic platform fit.

// How recommendations are scored

This decision engine weighs platform fit across compute capability, video stream capacity, power envelope, thermal and environmental suitability, and ecosystem alignment. A platform receives a higher match confidence when its realistic deployment characteristics align with the workload rather than just matching a single benchmark number.

Compute fit for the selected model type and stream count
Power compatibility with the stated deployment budget
Environmental suitability for fanless, industrial, mobile, or rack deployments
Platform flexibility for single-model versus multi-model pipelines
Alternative recommendations when the top choice is power-, cost-, or cooling-constrained

// What the output includes

Primary recommendation: the best-fit hardware platform for the selected constraints
Match confidence: a percentage score representing how well the platform aligns with the full decision profile
Alternatives: secondary options that remain viable for the same workload
Deployment metrics: compute TOPS, power range, stream capacity, cooling assumptions, and estimated cost
Machine-readable JSON: a structured result that can be copied, shared, or reused by downstream systems

// Worked examples

// Example 01

Battery-powered sensor node

Single camera, classification, 1 stream, ultra-low power, fanless → Coral TPU is usually favored for per-watt efficiency and passive deployment.

// Example 02

Retail or warehouse analytics

Multi-camera object detection, 4–8 streams, moderate power → Jetson Orin Nano or Hailo-8 often emerge as the best balance of throughput and deployment practicality.

// Example 03

High-throughput multi-model deployment

Edge server, multi-model pipeline, 16 streams, high power → Jetson AGX Orin is typically recommended when maximum headroom and production-grade scaling are required.

// Example machine-readable output

{
  "schema": "edgeaistack/decision/v1",
  "inputs": {
    "scenario": "multi_camera",
    "model": "object_detection",
    "streams": 8,
    "power_constraint": "moderate",
    "environment": "industrial"
  },
  "recommendation": {
    "device": "Hailo-8",
    "device_id": "hailo8",
    "match_confidence": 89,
    "alternatives": ["jetson_orin_nano", "jetson_agx_orin"]
  }
}

// FAQ

When should I choose Jetson over Coral TPU?

Choose Jetson when you need broader model support, CUDA-based workflows, robotics stack compatibility, or more general-purpose vision flexibility. Choose Coral TPU when ultra-low power and TensorFlow Lite deployment are the main constraints.

Does higher stream count always require a larger platform?

Usually yes. More concurrent streams increase compute requirements, sustained thermal load, and memory bandwidth pressure, which is why stream count is a primary sizing input.

Is fanless deployment treated as a hard constraint?

Yes. Fanless or sealed deployments favor platforms with lower sustained thermal output and more realistic passive-cooling envelopes.

Does this tool assume quantized models?

The recommendation logic assumes practical deployment fit by platform class and workload type. In real deployments, quantization strategy, framework support, and model conversion constraints should still be validated before final hardware purchase.