HailoRT
Edge AI platforms software
- Features
- Ease of use
- Ease of management
- Quality of support
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What is HailoRT
HailoRT is a runtime software component used to deploy and execute neural network inference on Hailo edge AI accelerators. It provides APIs and tooling to load compiled models, manage device resources, and run inference pipelines on embedded and edge systems. Typical users include developers building computer vision and other AI applications on Linux-based edge devices that integrate Hailo hardware. It differentiates by being tightly coupled to Hailo’s compiler output and hardware scheduling model rather than serving as a general-purpose edge device management platform.
Hardware-optimized inference runtime
HailoRT is designed specifically to run inference efficiently on Hailo accelerators using the vendor’s execution model. This tight integration can reduce integration work compared with adapting generic runtimes to specialized hardware. It supports common edge deployment patterns where inference must run locally with constrained CPU and power budgets.
Developer APIs and tooling
The product exposes runtime APIs that developers can embed into applications to control model loading, inference execution, and device interaction. This fits teams building custom edge applications rather than only using a managed cloud-to-edge workflow. It also aligns with a broader vendor software stack where models are compiled for the target accelerator and then executed via the runtime.
Edge-friendly deployment focus
HailoRT targets embedded Linux and edge compute environments where applications need deterministic local inference. It is suited to computer vision pipelines that run near sensors and cameras, including multi-stream scenarios depending on the underlying hardware configuration. This focus complements edge AI platform needs where inference must continue without continuous cloud connectivity.
Tied to Hailo hardware
HailoRT is primarily useful when deploying on Hailo accelerators and does not function as a hardware-agnostic inference layer. Organizations standardizing across multiple accelerator vendors may need additional runtimes and abstraction layers. This can increase operational complexity for heterogeneous edge fleets.
Not a full edge platform
HailoRT focuses on inference execution and device/runtime interaction, not end-to-end edge orchestration. Capabilities commonly expected in broader edge platforms—such as device provisioning, OTA updates, fleet monitoring, and policy-based deployment—typically require separate tooling. Teams may need to integrate it with other edge management and IoT components.
Model workflow constraints
Deployment generally depends on models being compiled and packaged in formats compatible with the Hailo toolchain. This can limit portability of models and pipelines compared with more general runtimes that accept multiple model formats without vendor-specific compilation. It may also require additional validation work when updating models or changing target hardware variants.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| HailoRT (runtime) | Free / Open-source | Production-grade runtime library (C/C++ & Python APIs), GStreamer plugin, ONNX runtime support; available as open-source on Hailo GitHub. Prebuilt packages and additional software are available via Hailo Developer Zone (sign-in required). |
Seller details
Hailo Technologies Ltd.
Tel Aviv, Israel
2017
Private
https://hailo.ai/
https://x.com/hailo_ai
https://www.linkedin.com/company/hailo-ai/
