📖 The AI Tool Bible

Apache SINGA vs Together AI

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

 
Apache SINGA
Fine-tuning
Together AI
Fine-tuning
TaglineApache-licensed distributed deep learning library focused on scalable training across GPUs and nodes.Fine-tune & serve open-weight models (Llama, Mistral, DeepSeek).
CategoryFine-tuningFine-tuning
PricingFree· Free, Apache 2.0 licensedPaid· Pay-per-token; fine-tuning per-token
Model—Llama / Mistral / Qwen / DeepSeek and others
Editorial score—8.6 / 10
Use cases
distributed trainingdeep learning researchONNX interoperabilitymodel serving
open modelsfine-tuninginference
Pros
  • Apache 2.0 licensed with active top-level project governance
  • First-class distributed training across multi-GPU and multi-node setups
  • ONNX support plus automatic gradient/computation-graph optimization
  • Adopted by serious users (Alibaba, NetEase, Citigroup, universities)
  • Wide open-model catalogue
  • Competitive inference pricing
  • Fine-tune + serve in one place
  • Dedicated endpoints for production
Cons
  • Smaller ecosystem and community than PyTorch or TensorFlow
  • Library only — no managed service, hosting, or UI
  • Requires self-managed GPU infrastructure and MLOps tooling
  • Latency varies by model
  • Less polish than OpenAI
Websitesinga.apache.orgwww.together.ai
Pick Apache SINGA if
  • ✅ Apache 2.0 licensed with active top-level project governance
  • ✅ First-class distributed training across multi-GPU and multi-node setups
  • ✅ ONNX support plus automatic gradient/computation-graph optimization
  • ✅ Adopted by serious users (Alibaba, NetEase, Citigroup, universities)
Pick Together AI if
  • ✅ Wide open-model catalogue
  • ✅ Competitive inference pricing
  • ✅ Fine-tune + serve in one place
  • ✅ Dedicated endpoints for production
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