How to Choose the Best Edge AI Platform: Jetson, Kria, Coral and Others

In 2025, selecting the right embedded AI hardware platform is crucial for product success. OEMs and engineering teams building edge devices—from autonomous robots and industrial cameras to smart sensors—must balance performance, power, ecosystem support, and compliance. Platforms like NVIDIA Jetson, Xilinx Kria, Google Coral, and others compete in different niches. This article compares them to help you make an informed choice.

Why hardware choice matters for edge AI projects

Edge devices must deliver real-time inference under size, power, and latency constraints. Speed alone isn’t enough; maintainability, integration, security, and certification also matter. The right platform empowers teams to deploy models more reliably and scale products faster.

Overview of leading platforms

NVIDIA Jetson (AGX Orin, Xavier NX, Orin Nano)

Jetson provides high GPU acceleration for compute-intensive models. For example, the AGX Orin delivers up to 275 TOPS and can process 4K video inference at realtime speeds, depending on optimization. Jetson excels at complex workloads but consumes 15–60 W and is relatively expensive.

Google Coral Dev Board / Edge TPU

Coral Edge TPU handles quantized TFLite models at very low power (2–4 W). In test setups, Coral boards achieved real-time inference on MobileNet V2 with under 50 ms latency at full 400 fps image streams. Ideal for battery-powered vision IoT devices.

Xilinx Kria (KV260 Vision AI module & SOM platforms)

Kria combines FPGA reconfiguration with CPU cores to build low-latency pipelines. In industrial inspection deployments, Kria modules processed four camera streams at under 20 ms latency, drawing <10 W while incorporating custom filtering logic in FPGA fabric.

Other platforms worth noting

There are alternatives like Qualcomm Robotics RB5 SoCs or Raspberry Pi 5 paired with AI accelerators (e.g. Coral or NCS2). These may cost under $200–$300 and offer mixed performance—appropriate for rapid prototyping or early-stage pilots.

Latest benchmarking insights

Independent benchmarking in 2025 showed Coral devices repeatedly outperformed others on lightweight models like MobileNet and EfficientNet, achieving faster average inference times. Jetson led in versatility, especially for ResNet or full-precision model inference. Kria wasn’t included in those benchmarks, but remains competitive in tailored vision processing environments thanks to hardware-level custom pipelines.

Core comparison criteria

Performance & throughput

Jetson: yes for full-scale GPU inference
Coral: best efficiency for quantized models
Kria: custom pipelines with predictable latency

Power consumption

Coral < 4 W
Kria typically < 10 W
Jetson ranges from 5 W to 60 W

Development ecosystem

Jetson: full AI SDK + CUDA support
Coral: TFLite tooling, streamlined dev setup
Kria: FPGA/RTL toolchain and custom IP support

Certification & industrial readiness

Jetson and Coral are product-ready; Kria excels in low-latency, industrial automation use cases. All need integration work for OTA updates, secure boot, CRA or ISO frameworks.

Cost & availability

Coral: ~$130–150 per Dev Board
Jetson Nano: entry-level ~$120; Orin modules range $500–1000+
Kria: enterprise pricing, variable by configuration

Community & support

Jetson: large developer base and long-term support
Coral: backed by Google’s AI ecosystem
Kria: smaller but technically focused FPGA/raw AI userbase

Choosing by use case

Robotics/autonomous systems: Jetson AGX Orin or Xavier NX
Low-power, battery image inference: Coral Dev Board or USB Edge TPU
Real-time industrial vision pipelines: Kria KV260 or custom SOM boards
Prototyping/projects on budget: Raspberry Pi + accelerator (Coral or Jetson Nano)

Evaluation checklist for teams

Define model architecture and framework support
Check quantization requirements (e.g. INT8 for Coral)
Power budget (battery vs mains)
Required sensors and connectivity
Certification or security needs (OTA, secure boot, CRA)
Hardware cost and availability

Often, hybrid combinations—Jetson handling compute logic, Coral for low-power sensing, Kria for camera pipelines—achieve the best balance.

Promwad case study

Promwad developed a defect inspection system using Kria KV260 with FPGA logic. It processed four camera feeds with sub‑20 ms latency and stayed under 10 W. Other platforms were tested: Coral hardware was accurate, but latency and neural net size limited sophistication; Jetson variants exceeded thermal limits in the enclosure design.

FAQs (Targeting long-tail questions)

Which platform is best for low-power vision? Coral Dev Board, due to Edge TPU efficiency.
Is Jetson worth it for Robotic use cases? Yes, for GPU-intensive models and flexibility.
Can Kria help with ultra-low latency vision pipelines? Yes. Its FPGA handles pipelined processing deterministically.

Summary comparison

Platform	Strengths	Limitations	Best use case
Jetson AGX Orin	High performance, robust ecosystem	Higher power and cost	Advanced robotics and AI systems
Coral Dev Board	Extreme efficiency on quantized models	Limited inference scope	Low-power sensors and vision systems
Kria KV260	Deterministic processing, custom pipelines	FPGA complexity, higher BOM cost	Industrial vision and edge robotics

Final advice

Start by defining your model type, power constraints, latency requirements, and certification needs. Prototype on multiple platforms to validate real-world performance. In many cases, a hybrid design best balances efficiency and flexibility. Edge AI hardware requires deep alignment between model architecture, deployment constraints, and lifecycle support.

If you’d like support choosing or integrating a platform for your embedded edge AI project, Promwad can assist with both evaluation and optimization.