Horovod
Horovod: Horovod adds multi-host, multi-GPU training to supported deep learning frameworks. Ranked #20 of 35 in Deep Learning Software by our editors (5.5/10); pricing: Free plan; best for teams adding multi-host training to existing frameworks.
At a glance
- Editor score5.5 / 10
- PricingFree plan
- Best forTeams adding multi-host training to existing frameworks
- Free planYes
- Paid fromNone
- Training modeBoth
- Facts checked23 Sep 2026
Where it wins
- Coordinates distributed training across multiple GPUs and hosts.
- Supports TensorFlow, Keras, PyTorch, and Apache MXNet.
- Provides collective operations and an elastic, fault-tolerant API via Gloo.
Where it doesn't
- Windows is unsupported; installation is listed for Linux and macOS.
- Its role focuses on coordinating distributed training, not a broader deep learning suite.
- Support is through community channels and documentation.
Our verdict on Horovod
Horovod is an open-source framework for scaling deep learning training across multiple GPUs and hosts. It is aimed at teams with existing TensorFlow, Keras, PyTorch, or Apache MXNet training scripts that need to coordinate workers and average gradients across machines. The project is hosted by the LF AI & Data Foundation, and its published platform support covers Linux and macOS, with cloud and self-hosted deployment options. It is a focused addition to a training workflow rather than a general-purpose deep learning environment.
Its main strength is framework and operations breadth within that focused role. Horovod supports collective tensor operations including allreduce, allgather, broadcast, and alltoall, and distributed optimizers for gradient averaging. GPU operations use NCCL on CUDA or ROCm GPUs; MPI and Gloo coordinate workers. An elastic, fault-tolerant API is available via Gloo, and Apache Spark is also supported. These options make it relevant to teams choosing among existing frameworks and distributed execution setups, rather than requiring one framework-specific approach.
Horovod is presented as open-source software, so the product is available without a paid plan structure. The trade-off is scope: it helps scale training, but the supplied description does not position it as a broader platform for the full deep learning lifecycle. Installation is listed for Linux and macOS, and Windows is explicitly unsupported. Support channels are community and documentation. Teams seeking multi-host training across supported frameworks may find the scope appropriate; those needing Windows support or a more comprehensive deep learning environment should consider another tool.
Horovod pricing
Horovod fact sheet
| Free plan | Yes |
|---|---|
| Paid from | None |
| Training mode | Both |
| Deployment targets | Multiple |
| GPU acceleration | Yes |
| Distributed training | Yes |
| Supported languages | Python |
| Model formats | Not verified |
| Deployment | Cloud, Self-hosted |
| Platforms | Linux, macOS |
| Support | Community, Docs |
| Built for | Small business, Mid-market, Enterprise (editorial estimate) |
| Integrations | 8 integrations: TensorFlow, Keras, PyTorch, Apache MXNet, Apache Spark, MPI … |
| Pricing | Free plan |
| Website | horovod.ai |
| Facts checked | 23 Sep 2026 |
Horovod integrations
Horovod lists 8 integrations on its own site.
- TensorFlow
- Keras
- PyTorch
- Apache MXNet
- Apache Spark
- MPI
- Gloo
- NCCL
Alternatives to Horovod
- Amazon SageMaker AIA comprehensive AWS-managed ML workflow suite for teams that accept usage-based billing.9.0
- Azure Machine LearningA paid Azure-native ML lifecycle service spanning automated training, deployment, and MLOps.7.8
- CaffeA free, established framework for teams maintaining Caffe-based model workflows.7.7
See all Horovod alternatives →
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Reviewed by iTechGuides Editors · Editorial team · Updated Sep 2026
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