📖 The AI Tool Bible

Paperspace Gradient vs Together AI

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

 
Paperspace Gradient
Fine-tuning
Together AI
Fine-tuning
TaglineEnd-to-end MLOps platform with GPU notebooks, training jobs, and model deployment, now folded into DigitalOcean.Fine-tune & serve open-weight models (Llama, Mistral, DeepSeek).
CategoryFine-tuningFine-tuning
PricingFreemium· Free notebook tier; paid Pro/Growth plans + per-second GPU billingPaid· Pay-per-token; fine-tuning per-token
ModelBring-your-own (PyTorch, TensorFlow, Hugging Face)Llama / Mistral / Qwen / DeepSeek and others
Editorial score—8.6 / 10
Use cases
model-trainingfine-tuninggpu-notebooksmodel-deploymentmlops
open modelsfine-tuninginference
Pros
  • Notebooks, training, and deployment in one workspace
  • Per-second GPU billing across a wide range of NVIDIA cards
  • Free notebook tier lowers the barrier to experimentation
  • GitHub-backed projects keep experiments reproducible
  • Now backed by DigitalOcean's infra and support footprint
  • Wide open-model catalogue
  • Competitive inference pricing
  • Fine-tune + serve in one place
  • Dedicated endpoints for production
Cons
  • Product roadmap unclear post-DigitalOcean acquisition
  • Smaller managed-service surface than SageMaker or Vertex AI
  • Free-tier GPUs are frequently capacity-constrained
  • Latency varies by model
  • Less polish than OpenAI
Websitewww.paperspace.comwww.together.ai
Pick Paperspace Gradient if
  • ✅ Notebooks, training, and deployment in one workspace
  • ✅ Per-second GPU billing across a wide range of NVIDIA cards
  • ✅ Free notebook tier lowers the barrier to experimentation
  • ✅ GitHub-backed projects keep experiments reproducible
Pick Together AI if
  • ✅ Wide open-model catalogue
  • ✅ Competitive inference pricing
  • ✅ Fine-tune + serve in one place
  • ✅ Dedicated endpoints for production
Compare a different pair →