Is Hailo worth it for robotics engineers?

Yes, if you prioritize ultra-low power (<5W) and DRAM-free design. Hailo-8 delivers 26 TOPS at just 2.5W, ideal for drones and autonomous vehicles. However, if you need broad community support, NVIDIA Jetson may be better.

Does Hailo integrate with Raspberry Pi?

Yes, the Raspberry Pi AI HAT+ 2 (released Jan 2026) integrates Hailo-10H for on-device GenAI. It supports LLMs, vision transformers, and works with Raspberry Pi 5.

How does Hailo compare to NVIDIA Jetson?

Hailo offers lower power (<5W vs 10-30W for Jetson) and DRAM-free design, but Jetson has a larger ecosystem, broader community support, and on-chip training. Choose Hailo for power-constrained applications.

What's the cheapest Hailo accelerator?

The Hailo-8L is the entry-level AI accelerator; pricing is not publicly listed. Contact sales for quotes. The Raspberry Pi AI HAT+ 2 is $80, offering a low-cost entry point for GenAI.

What are Hailo's biggest limitations?

Pricing is opaque (contact sales), ecosystem smaller than NVIDIA Jetson, no on-chip training, and software maturity is evolving. Some advanced model support may require vendor engagement.

Can Hailo replace NVIDIA Jetson?

For low-power, vision-only inference, yes. Hailo excels at power efficiency and DRAM-free design. But it cannot replace Jetson for high-power training or applications needing CUDA ecosystem support.

How long does Hailo take to set up?

Hardware integration (e.g., M.2 module) takes 1-2 hours. Software setup with Model Zoo: 2-4 hours. Full custom model deployment: 1-2 weeks.

How do I migrate from NVIDIA Jetson to Hailo?

You need to rewrite CUDA kernels to Hailo's Dataflow Compiler format (SNC). Use Hailo Model Zoo for common models. Expect 1-4 weeks depending on model complexity.

Is Hailo good for generative AI on edge devices?

Yes, with Hailo-10H and Raspberry Pi AI HAT+ 2, you can run LLMs (e.g., 7B models) at ~20 tokens/sec, suitable for private AI assistants and smart cockpits.

Is Hailo still active in 2026?

Yes — Hailo is active in 2026 with a liveness score of 86/100 (healthy), last verified June 26, 2026. Its main site responds to our weekly automated probes, though 1 secondary page failed the last check.

Developer Infrastructure

Hailo

Q: Does Hailo integrate with Raspberry Pi?

Yes, the Raspberry Pi AI HAT+ 2 (released Jan 2026) integrates Hailo-10H for on-device GenAI. It supports LLMs, vision transformers, and works with Raspberry Pi 5.

Q: How does Hailo compare to NVIDIA Jetson?

Hailo offers lower power (<5W vs 10-30W for Jetson) and DRAM-free design, but Jetson has a larger ecosystem, broader community support, and on-chip training. Choose Hailo for power-constrained applications.

Q: What's the cheapest Hailo accelerator?

The Hailo-8L is the entry-level AI accelerator; pricing is not publicly listed. Contact sales for quotes. The Raspberry Pi AI HAT+ 2 is $80, offering a low-cost entry point for GenAI.

Q: What are Hailo's biggest limitations?

Pricing is opaque (contact sales), ecosystem smaller than NVIDIA Jetson, no on-chip training, and software maturity is evolving. Some advanced model support may require vendor engagement.

Q: Can Hailo replace NVIDIA Jetson?

For low-power, vision-only inference, yes. Hailo excels at power efficiency and DRAM-free design. But it cannot replace Jetson for high-power training or applications needing CUDA ecosystem support.

Q: How long does Hailo take to set up?

Hardware integration (e.g., M.2 module) takes 1-2 hours. Software setup with Model Zoo: 2-4 hours. Full custom model deployment: 1-2 weeks.

Q: How do I migrate from NVIDIA Jetson to Hailo?

You need to rewrite CUDA kernels to Hailo's Dataflow Compiler format (SNC). Use Hailo Model Zoo for common models. Expect 1-4 weeks depending on model complexity.

Q: Is Hailo good for generative AI on edge devices?

Yes, with Hailo-10H and Raspberry Pi AI HAT+ 2, you can run LLMs (e.g., 7B models) at ~20 tokens/sec, suitable for private AI assistants and smart cockpits.

Q: Is Hailo still active in 2026?

Yes — Hailo is active in 2026 with a liveness score of 86/100 (healthy), last verified June 26, 2026. Its main site responds to our weekly automated probes, though 1 secondary page failed the last check.

On-device GenAI and vision processors for low-power edge inference.

86/100Safe BetCustom pricingContact Sales

Hailo delivers the most cost-efficient edge AI accelerators for low-power vision and GenAI, but the ecosystem is smaller than NVIDIA Jetson. Choose Hailo when power and DRAM-free design are critical; skip if you need broad community support or on-chip training.

Verified 16d ago · liveness 86/100 · cite: rightaichoice.com/tools/hailo

Best for

Edge AI developers building vision-based security, detection, or OCR systems
Robotics and drone manufacturers needing on-device inference under 5W
Automotive ADAS/AD systems requiring real-time, low-latency AI processing
Smart retail and point-of-sale analytics on the edge

Not ideal for

Cloud-only inference workloads with no edge requirement
On-chip training — inference-focused only
High-volume general-purpose computing without AI acceleration

Visit Website

IntermediateFor hardware integration (M.2 or HAT): 1-2 hours for first boot. Software setup with Model Zoo: 2-4 hours. Full custom model deployment: 1-2 weeks.No public API6.3k viewsVerified 16d ago

Pricing

Custom pricing

Contact Sales1 hidden cost

Learning curve

Intermediate

For hardware integration (M.2 or HAT): 1-2 hours for first boot. Software setup with Model Zoo: 2-4 hours. Full custom model deployment: 1-2 weeks.

Who it's for

Robotics engineerSmart retail integratorHobbyist AI developer

Live sentiment

Is Hailo actually worth it?

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Skip it if

Skip Hailo if you need broad community support or on-chip training.

The 30-second take

Biggest gripe

Pricing requires contacting sales; no public pricing available.

Price reality

Hailo's pricing is opaque and vendor-controlled—best for enterprise buyers who can negotiate volume deals. Smaller teams may find it hard to evaluate cost vs. NVIDIA Jetson or Google Coral.

In short

Hailo — On-device GenAI and vision processors for low-power edge inference. Best for Edge AI developers building vision-based security, detection, or OCR systems, Robotics and drone manufacturers needing on-device inference under 5W, Automotive ADAS/AD systems requiring real-time, low-latency AI processing. Contact Sales pricing.

What's new in Hailo

Checked 16 days ago

Across the latest 2 updates: 1 launch and 1 news mention.

NewsBlog·Mar 24Newest

The Revolution of Physical Intelligence Belongs at the Edge

Blog post by Yaniv Sulkes arguing that AI progression from prediction to generative to agentic converges on edge hardware, positioning Hailo accelerators.

LaunchBlog·Feb 19

Bringing On-device Generative AI to the PI: When and why you’ll need the Raspberry PI AI HAT+ 2

Article detailing Raspberry Pi AI HAT+ 2 with Hailo-10H, released Jan 15, 2026, for on-device GenAI on Pi 5.

Viability Score

86/100

Safe Bet

How likely is Hailo to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.

momentum

funding runway

website health

wrapper dependency

100

Last calculated: July 2026

How we score →

Key Features

DRAM-free AI accelerators (Hailo-8, Hailo-10H, Hailo-8L)
AI vision processors with integrated ISP (Hailo-15L, Hailo-15H)
Generative AI on edge (LLMs, vision transformers)
Low power consumption (typically <5W)
Dataflow Compiler for model optimization
HailoRT runtime for efficient deployment
Model Zoo with pre-trained vision and GenAI models
Vision Model Explorer for model selection
GenAI Example Applications (Raspberry Pi, ASUS)
Hailo OS for vision processor systems
Hailo Media Library for video processing
HailoDSP for signal processing
Hailo Camera Applications for camera integration
Raspberry Pi AI HAT+ 2 compatibility
ASUS UGen300 USB AI Accelerator support

About Hailo

Contact SalesIntermediateNo API

Hailo designs breakthrough AI processors for executing deep learning and generative AI on edge devices, balancing performance with ultra-low power consumption. Its portfolio spans DRAM-free AI accelerators (Hailo-8L, Hailo-8, Hailo-10H, Hailo-8R, Hailo-8 Century) and AI vision SoCs (Hailo-15L, Hailo-15H) with integrated ISPs, targeting robotics, ADAS, smart security, retail, and personal compute. The Hailo-10H specifically enables on-device LLMs and vision transformers, as seen in the Raspberry Pi AI HAT+ 2 and ASUS UGen300 USB accelerator. The Hailo AI Software Suite — Dataflow Compiler, HailoRT runtime, Model Zoo, and GenAI Example Applications — streamlines model optimization and deployment. Compared to NVIDIA Jetson, Hailo emphasizes lower power (typically <5W) and DRAM-free design for cost-sensitive edge deployments, though its community ecosystem is smaller.

Behind the Verdict

Hailo fills a clear niche: edge inference where power and cost per watt matter more than raw throughput or community breadth. Its DRAM-free design reduces BOM and complexity, a real advantage for volume hardware products. The Hailo-10H's GenAI capabilities — demonstrated on a Raspberry Pi 5 HAT — make it unique for on-device LLMs without cloud calls. However, the model zoo and developer resources are thinner than NVIDIA's Jetson ecosystem, and there's no on-chip training. In practice, we'd pick Hailo for a custom security camera, an industrial AOI rig, or a smart retail terminal where every milliwatt counts. Pass if you need PyTorch or TensorFlow examples out of the box — you'll need to use the Dataflow Compiler for optimization, which adds steps. Pricing is custom per volume, typical for hardware silicon.

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Real-world workflow fit

Concrete scenarios for the personas Hailo actually fits — and what changes day-one when you adopt it.

Robotics engineer

Integrate Hailo-8 M.2 module into a drone for real-time object detection.

Outcome: Achieve sub-5W power draw while running YOLOv8 at 30 FPS, enabling longer flight times.

Smart retail integrator

Deploy Hailo-15H camera with integrated ISP for point-of-sale analytics.

Outcome: Reduce cloud bandwidth 80% via on-device processing, with face detection and transaction logging.

Hobbyist AI developer

Use Raspberry Pi AI HAT+ 2 with Hailo-10H to run local LLMs.

Outcome: Run a 7B parameter model at 20 tokens/sec on a $80 HAT, enabling private AI assistants.

Use Cases

AI-driven HVAC building management (e.g., Automata Nexus, Apr 2026)
Real-time object detection for drones
Smart retail point-of-sale analytics (e.g., Elo POS, Mar 2026)
Automatic optical inspection in manufacturing
Vehicle access control with license plate recognition
Generative AI smart cockpit for automotive
On-device GenAI via Raspberry Pi HAT+ 2 or ASUS UGen300
AI-powered X-ray baggage screening (Evolv eXpedite)

Models Under the Hood

Hailo-8Hailo-10HHailo-8LHailo-8RHailo-15LHailo-15H

as of 2026-07-05

Limitations

Pricing is not publicly disclosed; you must contact sales for quotes, which may delay procurement for smaller teams.
The ecosystem of third-party hardware modules is less extensive than NVIDIA Jetson.
Software maturity is evolving, and some advanced features (e.g., specific model support) may require direct vendor engagement.

as of 2026-06-26

Hidden costs & gotchas

What the public pricing page doesn't put in bold. Captured from pricing-page footnotes, contract terms, and recurring complaints.

Pricing requires contacting sales; no public pricing available.

Where the pricing makes sense

The company stage and team size where Hailo's pricing actually pencils out — and where peers do it cheaper.

Hailo's pricing is opaque and vendor-controlled—best for enterprise buyers who can negotiate volume deals. Smaller teams may find it hard to evaluate cost vs. NVIDIA Jetson or Google Coral.

Setup time & first value

How long it actually takes to get something useful out of Hailo — broken out by persona, not the marketing-page minute.

For hardware integration (M.2 or HAT): 1-2 hours for first boot. Software setup with Model Zoo: 2-4 hours. Full custom model deployment: 1-2 weeks.

Switching to or from Hailo

How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.

Migrating in

→From NVIDIA Jetson: rewrite CUDA kernels to Hailo's Dataflow Compiler (SNC format), retune models.
→From Google Coral: export TFLite models, optimize with Hailo Model Zoo.
→From Intel Movidius: convert OpenVINO IR to Hailo's format using provided tools.

Migrating out

↗To NVIDIA Jetson: export Hailo SNC models to TensorRT (requires manual reimplementation).
↗To Google Coral: use TFLite directly, but lose DRAM-free power benefits.

Integrations

Raspberry PiASUSHusqvarna AutomowerVicon NEXTAutomata NexusBlue White RoboticsGitHubOpenClaw Claude Code

Resources & Guides

Official links

Official Website Changelog

Popular in Developer Infrastructure

Frequently Asked Questions

Best-of guides

Best AI Tools for Retail & Merchandising

Topics

Automation

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