FluidStack vs Paperspace Comparison
Detailed comparison of features, pricing, and capabilities
Last updated May 1, 2026
Overview
Compare key metrics and features at a glance
FluidStack
https://www.fluidstack.io
FluidStack is a cloud GPU infrastructure provider that aggregates underutilized GPU capacity from data centers worldwide to offer on-demand and reserved GPU compute at competitive prices. The platform enables AI companies, researchers, and developers to access large-scale GPU clusters for training and inference workloads, including support for high-performance interconnects like InfiniBand. FluidStack differentiates itself by sourcing capacity from a distributed network of partner data centers, providing cost-effective alternatives to hyperscale cloud providers for AI/ML workloads.
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.
Quick Comparison
| Detail | FluidStack | Paperspace |
|---|---|---|
| Category | AI Cloud Infrastructure | AI Cloud Infrastructure |
| Starting Price | Contact Sales | Free |
| Plans Available | 1 | 8 |
| Features Tracked | 16 | 15 |
| Founded | 2019 | 2014 |
| Headquarters | London, United Kingdom | New York, USA |
Features
Detailed feature-by-feature comparison
Feature Comparison
| Feature | | |
|---|---|---|
| api | ||
| Full API Access | ||
| core | ||
| Collaboration Tools | ||
| Dedicated GPU Clusters | ||
| Fully Managed Clusters | ||
| GPU Instances | ||
| H100/H200/B200/GB200 Support | ||
| High-Speed Networking | ||
| InfiniBand Interconnects | ||
| Instant Provisioning | ||
| Jupyter Notebooks | ||
| Kubernetes Support | ||
| Low-Latency Inference | ||
| ML Monitoring | ||
| Model Deployments | ||
| Per-Second Billing | ||
| Persistent Storage | ||
| Pre-configured Frameworks | ||
| Rapid Deployment | ||
| Slurm Support | ||
| Transparent Pricing | ||
| Windows Machines | ||
| Workflows | ||
| custom | ||
| Custom Data Centers | ||
| integration | ||
| Distributed Data Access | ||
| Kubernetes Support | ||
| security | ||
| Secure Access Controls | ||
| Single-Tenant Isolation | ||
| support | ||
| 15-Minute Response SLA | ||
| 99% Uptime SLA | ||
| Hands-on Support | ||
| Proactive Monitoring | ||
Pricing
Compare pricing plans and value for money
FluidStack
Contact Sales
Best For
AI companies and researchers needing rapid, cost-effective, fully managed large-scale dedicated GPU clusters for training without hyperscaler lock-in.
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.
Integrations
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Supported Integrations
Coming Soon
Integration comparison data for FluidStack, Paperspace is being collected and will be available soon.
Strengths & Limitations
Key strengths and limitations of each service
FluidStack
AI companies and researchers needing rapid, cost-effective, fully managed large-scale dedicated GPU clusters for training without hyperscaler lock-in.
- Rapid deployment of multi-thousand GPU clusters in as little as 48 hours with zero-setup management.
- Single-tenant isolation at hardware, network, and storage levels eliminates noisy neighbors unlike hyperscalers.
- Supports latest NVIDIA H100/H200/B200/GB200 GPUs with InfiniBand and 99% uptime SLA.
- 24/7 engineering support via Slack with 15-minute response times and proactive monitoring.
- Enterprise-only pricing requires contacting sales, lacking transparent pay-as-you-go rates.
- Small team of 11-50 employees and seed funding may limit scalability versus larger competitors.
- Aggregated capacity from partner data centers could introduce variability in global availability.
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
Company Info
Company details and background
FluidStack
Paperspace
Comparison FAQ
Common questions about comparing FluidStack and Paperspace
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