Paperspace vs Together AI Comparison
Detailed comparison of features, pricing, and capabilities
Last updated May 1, 2026
Overview
Compare key metrics and features at a glance
Paperspace
https://www.paperspace.com
Paperspace is a cloud computing platform specializing in GPU-accelerated virtual machines and machine learning infrastructure, enabling developers and data scientists to build, train, and deploy AI/ML models at scale. It offers products including Gradient, a MLOps platform for running Jupyter notebooks and ML pipelines, and Core, which provides on-demand GPU cloud instances. Paperspace was acquired by DigitalOcean in 2023, integrating its GPU cloud capabilities into DigitalOcean's broader cloud services portfolio.
Together AI
https://www.together.ai
Together AI is a cloud platform that enables developers and enterprises to run, fine-tune, and deploy open-source large language models (LLMs) at scale with high performance and cost efficiency. The platform provides access to a wide range of open-source models including LLaMA, Mistral, and others through a unified API, along with tools for custom model fine-tuning and inference optimization. Together AI also conducts AI research and has developed its own inference infrastructure designed to deliver fast and affordable generative AI capabilities.
Quick Comparison
| Detail | Paperspace | Together AI |
|---|---|---|
| Category | AI Cloud Infrastructure | AI Cloud Infrastructure |
| Starting Price | Free | Free |
| Plans Available | 8 | 6 |
| Features Tracked | 15 | 15 |
| Founded | 2014 | 2022 |
| Headquarters | New York, USA | San Francisco, USA |
Features
Detailed feature-by-feature comparison
Feature Comparison
| Feature | | |
|---|---|---|
| api | ||
| Full API Access | ||
| OpenAI-Compatible APIs | ||
| core | ||
| Autoscaling GPU Clusters | ||
| Collaboration Tools | ||
| Dedicated Model Inference | ||
| Fine-Tuning Workflows | ||
| Full-Stack Observability | ||
| GPU Instances | ||
| High-Performance Inference | ||
| High-Speed Networking | ||
| Instant GPU Clusters | ||
| Instant Provisioning | ||
| Jupyter Notebooks | ||
| Kubernetes & Slurm | ||
| ML Monitoring | ||
| Model Deployments | ||
| NVIDIA GPU Support | ||
| Pay-As-You-Go Pricing | ||
| Per-Second Billing | ||
| Persistent Storage | ||
| Pre-configured Frameworks | ||
| Self-Healing Clusters | ||
| Serverless Inference | ||
| Windows Machines | ||
| Workflows | ||
| Zero Egress Fees | ||
| integration | ||
| Kubernetes Support | ||
| Open-Source Model Hub | ||
| SDK Support | ||
| support | ||
| Hands-on Support | ||
Pricing
Compare pricing plans and value for money
Paperspace
From $0/mo
Price Components
- base_fee: $0/month
- storage: $0/GB (5 included)
- base_fee: $8/month
- storage: $0/GB (15 included)
- base_fee: $39/month
Best For
ML engineers and data scientists needing cost-efficient, GPU-accelerated development environments with integrated MLOps tools and flexible per-second billing.
Together AI
From $0/mo
Price Components
- GLM-5.1 Input Tokens: $1.4/1M tokens
- GLM-5.1 Output Tokens: $4.4/1M tokens
- Llama 3.3 70B: $0.88/1M tokens
- 1x H100 80GB: $3.99/hour
- 1x H200 141GB: $5.49/hour
Best For
Developers and enterprises needing fast, cost-efficient deployment and fine-tuning of open-source LLMs with flexible GPU clusters and serverless APIs.
Integrations
See which third-party services are supported
Supported Integrations
Coming Soon
Integration comparison data for Paperspace, Together AI is being collected and will be available soon.
Strengths & Limitations
Key strengths and limitations of each service
Paperspace
ML engineers and data scientists needing cost-efficient, GPU-accelerated development environments with integrated MLOps tools and flexible per-second billing.
- Per-second billing with no hourly minimums enables precise cost control for variable GPU workloads compared to competitors' hourly models
- Integrated MLOps platform (Gradient) combines managed Jupyter notebooks, automated pipelines, and model deployment in one interface without switching tools
- Access to enterprise-grade GPUs (H100, A100) with 10 Gbps backend networking optimized specifically for AI/ML training at scale
- Limited market presence and brand recognition post-DigitalOcean acquisition compared to established competitors like AWS SageMaker or Google Colab
- Smaller global data center footprint than hyperscalers, potentially limiting geographic redundancy and latency optimization for distributed teams
Together AI
Developers and enterprises needing fast, cost-efficient deployment and fine-tuning of open-source LLMs with flexible GPU clusters and serverless APIs.
- Serverless inference with OpenAI-compatible APIs and up to 4x faster performance via custom optimizations differentiates from generic cloud providers.
- Instant self-service GPU clusters up to 64 NVIDIA H100/H200 GPUs deploy in minutes with zero egress fees and autoscaling.
- Fine-tuning for 200+ open-source models like LLaMA and Mistral using proprietary data, with dedicated $2,872/month inference options.
- Full-stack observability via Grafana dashboards and pay-as-you-go token-based pricing for cost-efficient scaling.
- Young company founded in 2022 with 51-200 employees may lack the enterprise maturity and global scale of hyperscalers like AWS.
- Focus on open-source models limits access to proprietary LLMs from providers like OpenAI or Anthropic.
- High entry for dedicated options at $2,872/month suits enterprises but may deter small teams preferring fully serverless.
Company Info
Company details and background
Paperspace
Together AI
Comparison FAQ
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