Apache SINGA
Apache SINGA: A compact, free Python framework for neural networks and distributed training. Ranked #17 of 35 in Deep Learning Software by our editors (5.7/10); pricing: Free plan; best for teams seeking a compact Python neural-network framework.
At a glance
- Editor score5.7 / 10
- PricingFree plan
- Best forTeams seeking a compact Python neural-network framework
- Free planYes
- Paid fromNone
- Training modeLocal
- Facts checked23 Sep 2026
Where it wins
- Free, open-source framework with Python and C++ support
- Automatic differentiation with SGD, Adam, RMSProp, and AdaGrad
- Data-parallel training across multiple GPUs and nodes
Where it doesn't
- Integration options center on ONNX and RDBMS
- Deployment is self-hosted and focused on local or on-premises use
- Verified integrations and workflow breadth are comparatively limited
Our verdict on Apache SINGA
Apache SINGA is an open-source deep-learning library for teams building, training, and running neural-network models. Its Python model and layer APIs support construction of neural networks, while C++ is also supported. The framework fits small, mid-market, and enterprise teams that want a compact, self-hosted tool for local or on-premises workloads across Linux, Windows, and macOS. CPU and CUDA-enabled GPU execution are supported, giving developers a choice of hardware for model development and operation.
SINGA is available as free open-source software rather than a tiered commercial product. The published installation options include pip, Docker, Conda, and source builds, so teams can align setup with their existing environment. Its ecosystem connections are focused: SINGA can load ONNX models for inference or retraining and export models created through its APIs to ONNX format; RDBMS is also listed as an integration. This makes it a practical fit for ONNX-centered workflows, while teams seeking a wider set of verified integrations may need to assess alternatives.
The standout capability is distributed training. SINGA supports data-parallel models across multiple GPUs and nodes, synchronizing distributed gradients with all-reduce. Automatic reverse-mode differentiation calculates gradients, and optimizers include SGD with momentum, Adam, RMSProp, and AdaGrad. Buffered-graph profiling exposes operator execution time for performance analysis. Documentation, examples, and community support are available for implementation and scaling. Choose SINGA when free licensing, Python APIs, GPU support, and distributed training are priorities; consider another framework if hosted deployment, broader workflow coverage, or a larger integration ecosystem matters more.
Apache SINGA pricing
Apache SINGA fact sheet
| Free plan | Yes |
|---|---|
| Paid from | None |
| Training mode | Local |
| Deployment targets | On-prem |
| GPU acceleration | Yes |
| Distributed training | Yes |
| Supported languages | Python, C++ |
| Model formats | ONNX |
| Deployment | Self-hosted |
| Platforms | Linux, Windows, macOS |
| Support | Community, Docs |
| Built for | Small business, Mid-market, Enterprise (editorial estimate) |
| Integrations | 2 integrations: ONNX, RDBMS |
| Pricing | Free plan |
| Website | singa.apache.org |
| Facts checked | 23 Sep 2026 |
Apache SINGA integrations
Apache SINGA lists 2 integrations on its own site.
- ONNX
- RDBMS
Alternatives to Apache SINGA
- 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 Apache SINGA alternatives →
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Reviewed by iTechGuides Editors · Editorial team · Updated Sep 2026
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