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Head to head · 2026

Apache SINGA vs Caffe

Pricing, free plans, ratings, features, pros and cons, side by side. Where one comes out ahead, we mark it.

Apache SINGA

Open-source distributed deep learning library - automatic gradient calculation and multi-GPU training, in real-world use at…

3.6Free

Visit Apache SINGA (opens Apache SINGA in a new tab)

Caffe

Berkeley's deep learning framework, still maintained in 2026 - built for expression, speed, and modularity, processing…

3.5Free

Visit Caffe (opens Caffe in a new tab)

Who comes out ahead

Based only on the facts in our directory. The right pick still depends on the job you need done.

  • Lower starting price

    Not comparable

    Not comparable: Apache SINGA does not list a price

  • Free plan

    Tie

    Both have one

  • Higher rating

    Apache SINGA

    3.6 against 3.5

  • Same job?

    Yes

    Both are AI Infrastructure & Agent Tooling tools

Side by side

Compared onApache SINGACaffe
Our rating3.6Better3.5
Starts atFreeFree
Free planYesYes
CategoryAI Infrastructure & Agent ToolingAI Infrastructure & Agent Tooling
PlansFreeFree
Key features
  • Distributed deep learning training across multiple GPUs
  • Automatic gradient calculation and computational graph optimization
  • Standard optimizer support (Adam, SGD, RMSProp, AdaGrad)
  • ONNX format interoperability
  • Real-world deployment at NUS, Alibaba, and Citigroup
  • Deep learning framework for neural network design, training, and deployment
  • Built for expression, speed, and modularity
  • Handles image classification, fine-tuning, and feature extraction
  • Proven at scale - 60M+ images/day processed in production
  • Ongoing ONNX compatibility work
Pros
  • Genuine enterprise and academic deployment, not just an unused research project
  • Actively maintained Apache Top Level Project with current releases
  • Free and open-source with multiple installation options
  • Still actively maintained despite predating the current AI boom by a decade
  • Speed and modularity design goals remain relevant for performance-sensitive use cases
  • Free, open-source, with over 1,000 historical contributors
Cons
  • A code library, not a hosted product - no dashboard or signup
  • Requires real ML/deep-learning infrastructure expertise to use effectively
  • No managed support beyond community/Apache project channels
  • A code library, not a hosted product - no dashboard or signup
  • Newer frameworks (PyTorch, TensorFlow) have far larger current ecosystems
  • Requires real ML infrastructure expertise to use effectively
CompanyApache SINGABerkeley AI Research (BAIR)

Prices come from each vendor's pricing page, last checked 20260824. Check the vendor before you buy.

FAQ

Questions people ask

Is Apache SINGA better than Caffe?

In our reviews Apache SINGA scores 3.6 and Caffe scores 3.5. The better pick depends on the job. Apache SINGA: Open-source distributed deep learning library - automatic gradient calculation and multi-GPU training, in real-world use at NUS, Alibaba, and Citigroup. Caffe: Berkeley's deep learning framework, still maintained in 2026 - built for expression, speed, and modularity, processing 60M+ images a day on GPU hardware.

Which is cheaper, Apache SINGA or Caffe?

Apache SINGA: free. Caffe: free.

Do Apache SINGA and Caffe have free plans?

Yes, both have a free plan.

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