Together AI vs Paperspace
Side-by-side comparison to help you choose the best tool.
Together AI
freemiumTogether AI is an AI cloud platform for training and running open-source models at enterprise scale. It provides high-throughput inference for LLaMA, Mistral, FLUX, and other models, along with fine-tuning as a service. Together is used by AI startups and enterprises that want the economics of open-source models with the reliability of managed cloud infrastructure.
Paperspace
freemiumPaperspace (now part of DigitalOcean) is a cloud platform for AI and machine learning that offers GPU-powered Jupyter notebooks, the Gradient managed ML platform for experiment tracking and model deployment, and virtual desktop environments for GPU-intensive applications. Gradient provides full MLOps features including dataset management, training job orchestration, and model deployment, while Paperspace's notebook environments offer free GPU access tiers ideal for learning and experimentation. It serves a wide audience from students learning deep learning to professional teams running production ML pipelines.
| Feature | Together AI | Paperspace |
|---|---|---|
| Pricing | freemium | freemium |
| Category | - | - |
| Rating | 4.4 | 4.2 |
| Best For | AI startups and enterprises wanting high-throughput open-source LLM inference with fine-tuning features at competitive cloud pricing | Students, researchers, and ML teams who want an integrated cloud environment for both experimentation and production ML workflows. |
| Views | 7 | 6 |
Pros
- Best open-source LLM inference price-performance
- Fine-tuning as a service is turnkey
- High throughput for production workloads
Cons
- Requires model knowledge — not plug-and-play like OpenAI
- Support response times vary
Pros
- Free GPU notebook tier is excellent for learning and prototyping
- Integrated MLOps platform reduces tool sprawl
- Part of DigitalOcean ecosystem for seamless cloud integration
Cons
- Free GPU tier has limited availability and session time
- Gradient platform less feature-rich than dedicated MLOps tools like MLflow or Weights & Biases
- High-throughput open-source LLM inference
- Fine-tuning as a service
- Serverless & dedicated deployments
- LLaMA, Mistral & FLUX APIs
- Batch inference
- GPU-powered Jupyter notebooks with free tier
- Gradient MLOps platform for training and deployment
- Virtual desktop environments for GPU workloads
- Persistent storage and dataset management
- Team collaboration and project sharing