The Best Deep Learning Software in 2026
We researched deep learning software using vendors’ official websites, including pricing pages, plan tables, and product documentation. Rankings focus on the core job of building, training, and deploying deep neural networks, alongside value for money and verified features, to help AI engineers and data science teams compare suitable tools.
Our top picks
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Top ranked
Amazon SageMaker AI#1 of 359.0/10A comprehensive AWS-managed ML workflow suite for teams that accept usage-based billing.
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Runner-up
Azure Machine Learning#2 of 357.8/10A paid Azure-native ML lifecycle service spanning automated training, deployment, and MLOps.
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Top-ranked free plan
Caffe#3 of 357.7/10A free, established framework for teams maintaining Caffe-based model workflows.
Free plan
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The full ranking 35 tools, best first
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Best forAWS teams needing managed end-to-end ML workflows
A comprehensive AWS-managed ML workflow suite for teams that accept usage-based billing.
9.0/10★★★★☆Visit SageMaker AI -
Best forAzure teams managing cloud ML workflows
A paid Azure-native ML lifecycle service spanning automated training, deployment, and MLOps.
7.8/10★★★★☆Visit Azure ML -
Best forTeams maintaining Caffe-based model workflows
A free, established framework for teams maintaining Caffe-based model workflows.
- Distributed training
- GPU acceleration
7.7/10★★★★☆Visit Caffe -
Best forTeams optimizing large-model training and inference
A free, open-source specialist for scaling large-model training and inference.
- Distributed training
- GPU acceleration
7.4/10★★★★☆Visit DeepSpeed -
Best forJava and Scala teams building neural networks
A JVM-native, open-source stack with GPU, Spark, and Keras/TensorFlow import.
- Distributed training
- GPU acceleration
7.1/10★★★★☆Visit Deeplearning4j -
Best forTeams serving trained models across frameworks
A multi-framework serving layer for teams running production inference, not developing models.
6.9/10★★★☆☆Visit NVIDIA Triton -
Best forTeams optimizing NVIDIA GPU inference
A free NVIDIA-focused SDK for compiling trained models into optimized inference engines.
- GPU acceleration
6.8/10★★★☆☆Visit NVIDIA -
Best forTeams fine-tuning and deploying vision models
A free Linux toolkit spanning vision training, optimization, and NVIDIA deployment.
- Distributed training
- GPU acceleration
6.6/10★★★☆☆Visit NVIDIA TAO -
Best forMATLAB users building and deploying deep-learning models
A MATLAB-centered deep-learning environment with broad model, training, and deployment support.
- Distributed training
- GPU acceleration
6.6/10★★★☆☆Visit MathWorks -
Best forDevelopers needing NVIDIA deep-learning GPU primitives
A free, low-level NVIDIA GPU library for accelerating deep-learning operations.
- GPU acceleration
6.5/10★★★☆☆Visit NVIDIA -
Best forTeams needing a broad training-to-deployment framework
A broad, free framework spanning model development, distributed training, and deployment.
- Distributed training
- GPU acceleration
6.3/10★★★☆☆Visit TensorFlow -
Best forPractitioners seeking high-level model training workflows
A free, high-level PyTorch toolkit covering major model-training workflows.
- Distributed training
- GPU acceleration
6.2/10★★★☆☆Visit fastai -
Best forPython teams wanting flexible framework backends
Free, open-source Keras gives Python teams one API across JAX, TensorFlow, and PyTorch.
- Distributed training
- GPU acceleration
6.1/10★★★☆☆Visit Keras -
Best forTeams building general-purpose deep-learning models
A free, flexible framework for developing and deploying models across CPUs, GPUs, and clouds.
- Distributed training
- GPU acceleration
6.0/10★★★☆☆Visit PyTorch -
Best forTeams managing distributed experiments and GPU resources
A self-hosted platform for distributed training, experiment tracking, and GPU scheduling.
- Distributed training
- GPU acceleration
5.8/10★★★☆☆Visit Determined -
Best forTeams wanting collaborative cloud AI workspaces
A collaborative cloud workspace with compute, storage, and monitored deployment for AI teams.
5.7/10★★★☆☆Visit Lightning AI -
Best forTeams seeking a compact Python neural-network framework
A compact, free Python framework for neural networks and distributed training.
- Distributed training
- GPU acceleration
5.7/10★★★☆☆Visit Apache SINGA -
Best forDevelopers maintaining define-by-run Python models
A free Python framework for dynamic graphs, GPU execution, and distributed training.
- Distributed training
- GPU acceleration
5.7/10★★★☆☆Visit Chainer -
Best forTensorFlow teams seeking focused distributed training
A focused, self-hosted option for distributing TensorFlow training across CPU and GPU clusters.
- Distributed training
- GPU acceleration
5.6/10★★★☆☆Visit Tarantella -
Best forTeams adding multi-host training to existing frameworks
Horovod adds multi-host, multi-GPU training to supported deep learning frameworks.
- Distributed training
- GPU acceleration
5.5/10★★★☆☆Visit Horovod -
Best forTeams training and running pretrained transformer models
A free, open-source toolkit for training, serving, and exporting pretrained transformer models.
- Distributed training
- GPU acceleration
5.5/10★★★☆☆Visit Hugging Face -
Best forPython teams needing accelerated numerical computation
A free Python foundation for compiled, differentiated, and distributed numerical workloads.
- Distributed training
- GPU acceleration
5.4/10★★★☆☆Visit JAX -
Best forOrganizations governing multi-cloud ML lifecycles
A governed MLOps workspace spanning experiments, pipelines, data, models, and multi-cloud compute.
5.4/10★★★☆☆Visit Valohai -
Best forTeams evaluating parallel training across varied hardware
A free, open-source framework for parallel training and deployment across varied hardware.
- Distributed training
- GPU acceleration
5.3/10★★★☆☆Visit MindSpore -
Best forTeams deploying optimized models on Intel hardware
A free, open-source inference stack for serving optimized models on Intel hardware.
- GPU acceleration
5.3/10★★★☆☆Visit OpenVINO -
Best forUsers wanting hosted GPU notebooks and model deployment
A hosted Jupyter-and-GPU workspace with workflows and model deployment endpoints.
5.2/10★★★☆☆Visit site -
Best forTeams scaling training across frameworks and clusters
A free, open-source training orchestrator for distributed Python workflows.
- Distributed training
- GPU acceleration
5.2/10★★★☆☆Visit Ray Train -
Best forApple Silicon users training models with shared memory
A free, self-hosted framework for Apple-silicon training with unified memory and distributed APIs.
- Distributed training
- GPU acceleration
5.2/10★★★☆☆Visit MLX -
Best forExperimenters exploring lightweight tensor operations
A free, open-source tensor framework for local deep-learning experiments across many runtimes.
- Distributed training
- GPU acceleration
5.1/10★★★☆☆Visit tinygrad -
Best forTeams running ONNX models across devices
A free, open-source runtime for executing and optimizing ONNX models across devices.
- GPU acceleration
5.1/10★★★☆☆Visit ONNX Runtime -
Best forTeams building multimodal models with declarative configs
A configurable, multimodal deep-learning framework spanning training through serving.
- Distributed training
- GPU acceleration
5.0/10★★☆☆☆Visit Ludwig -
Best forTeams needing training and inference across device types
A free framework spanning model development, conversion, and multi-device inference.
- Distributed training
- GPU acceleration
5.0/10★★☆☆☆Visit MegEngine -
Best forTeams optimizing trained models for varied hardware
A free compiler and deployment framework for optimizing models across varied hardware.
- GPU acceleration
5.0/10★★☆☆☆Visit Apache TVM -
Best forTeams running distributed training on Kubernetes
Free, open-source orchestration for multi-node training on Kubernetes.
- Distributed training
- GPU acceleration
4.9/10★★☆☆☆Try Kubeflow Trainer -
Best forTeams needing open-source training and inference
Free, open-source deep learning framework for training, inference, and deployment.
- Distributed training
- GPU acceleration
4.9/10★★☆☆☆Visit PaddlePaddle
No tools match those filters.
Compare at a glance
| # | Tool | Free plan | Paid from | Training mode | Deployment targets | GPU acceleration | Distributed training | Score |
|---|---|---|---|---|---|---|---|---|
| 1 | Amazon SageMaker AI | No | — | — | — | — | — | 9.0 |
| 2 | Azure Machine Learning | No | — | — | — | — | — | 7.8 |
| 3 | Caffe | Yes | None | Local | On-prem | Yes | Yes | 7.7 |
| 4 | DeepSpeed | Yes | None | Local | Multiple | Yes | Yes | 7.4 |
| 5 | Deeplearning4j | Yes | None | Both | Multiple | Yes | Yes | 7.1 |
| 6 | NVIDIA Triton Inference Server | Yes | None | — | — | — | — | 6.9 |
| 7 | NVIDIA TensorRT | Yes | None | Local | Multiple | Yes | No | 6.8 |
| 8 | NVIDIA TAO Toolkit | Yes | None | Both | Multiple | Yes | Yes | 6.6 |
| 9 | MATLAB Deep Learning Toolbox | No | — | Both | Multiple | Yes | Yes | 6.6 |
| 10 | NVIDIA cuDNN | Yes | None | Local | Multiple | Yes | — | 6.5 |
| 11 | TensorFlow | Yes | None | Local | Multiple | Yes | Yes | 6.3 |
| 12 | fastai | Yes | None | Local | — | Yes | Yes | 6.2 |
| 13 | Keras | Yes | None | Local | Multiple | Yes | Yes | 6.1 |
| 14 | PyTorch | Yes | None | Both | Multiple | Yes | Yes | 6.0 |
| 15 | Determined | Yes | None | Both | Multiple | Yes | Yes | 5.8 |
| 16 | Lightning AI Studios | Yes | — | — | — | — | — | 5.7 |
| 17 | Apache SINGA | Yes | None | Local | On-prem | Yes | Yes | 5.7 |
| 18 | Chainer | Yes | None | Local | — | Yes | Yes | 5.7 |
| 19 | Tarantella | Yes | None | Local | On-prem | Yes | Yes | 5.6 |
| 20 | Horovod | Yes | None | Both | Multiple | Yes | Yes | 5.5 |
| 21 | Hugging Face Transformers | Yes | None | Local | Multiple | Yes | Yes | 5.5 |
| 22 | JAX | Yes | None | Local | — | Yes | Yes | 5.4 |
| 23 | Valohai | No | — | — | — | — | — | 5.4 |
| 24 | MindSpore | Yes | None | Local | Multiple | Yes | Yes | 5.3 |
| 25 | OpenVINO | Yes | None | Local | Multiple | Yes | — | 5.3 |
| 26 | Paperspace Gradient | Yes | $8/mo | — | — | — | — | 5.2 |
| 27 | Ray Train | Yes | None | Both | Multiple | Yes | Yes | 5.2 |
| 28 | MLX | Yes | None | Both | Multiple | Yes | Yes | 5.2 |
| 29 | tinygrad | Yes | None | Local | Multiple | Yes | Yes | 5.1 |
| 30 | ONNX Runtime | Yes | None | Local | Multiple | Yes | — | 5.1 |
| 31 | Ludwig | Yes | None | Both | Multiple | Yes | Yes | 5.0 |
| 32 | MegEngine | Yes | None | Local | Multiple | Yes | Yes | 5.0 |
| 33 | Apache TVM | Yes | None | — | Multiple | Yes | — | 5.0 |
| 34 | Kubeflow Trainer | Yes | None | Both | Multiple | Yes | Yes | 4.9 |
| 35 | PaddlePaddle | Yes | None | Local | Multiple | Yes | Yes | 4.9 |
Head-to-head All 27 comparisons →
- Amazon SageMaker AI vs Caffe
- Amazon SageMaker AI vs DeepSpeed
- Amazon SageMaker AI vs Deeplearning4j
- Amazon SageMaker AI vs NVIDIA Triton Inference Server
- Amazon SageMaker AI vs NVIDIA TensorRT
- Amazon SageMaker AI vs NVIDIA TAO Toolkit
- Azure Machine Learning vs Caffe
- Azure Machine Learning vs DeepSpeed
- Azure Machine Learning vs Deeplearning4j
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How we rank deep learning software
Every tool on this page was researched by iTechGuides Editors from its official website — pricing pages, plan tables and product documentation. We rank on how well each one does this category's core job, what the free or entry plan includes, and where it falls short. Where we have enough verified facts, the score out of 10 is a rubric — job fit, value and how much we could verify — shown with its breakdown on every tool's page; a tool we have not verified enough to score yet shows its rank without a number. Scores are re-checked when a product changes its plans. Read the full editorial policy, or submit a tool we missed.
Reviewed by iTechGuides Editors · Editorial team · Updated Sep 2026
Last updated · How we research and update