Compute Comparison

Paperspace

Specialist Cloud

Paperspace (now part of DigitalOcean) offers A100, RTX 4000 ADA, and RTX 5000 ADA GPU instances alongside Gradient, its managed ML platform with Jupyter notebooks, experiment tracking, and one-click model deployment. On-demand and monthly billing options make it accessible for individuals and small teams exploring AI training and fine-tuning without complex infrastructure setup. A beginner-friendly on-demand GPU cloud with a polished notebook-centric experience.

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Cheapest On-Demand

$0.400/hr

Cheapest Spot

GPU Listings

5

Billing

On-demand, Monthly

Performance Benchmarks

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Provider Info

Headquarters

New York, NY

Founded

2014

Regions

US-East, US-West, EU-West

Min Commitment

None

Support

Community → Growth

Strengths

  • Managed ML platform
  • Jupyter notebooks
  • Simple UI
  • DigitalOcean integration

Limitations

  • Acquired by DigitalOcean — product direction uncertain
  • Limited GPU catalog vs specialist providers
  • Notebook-centric UX not ideal for production pipelines

Best For

ML beginnersNotebook-based workflowsSmall teams

Full GPU Catalog

GPU ModelvRAMOn-DemandSpotAvailabilityRegion
A10G24 GB$0.400HighUS-East
RTX 409024 GB$0.820MedUS-East
A100 40GB40 GB$0.880HighUS-East
A100 80GB80 GB$1.29HighUS-East
H100 80GB80 GB$3.03MedUS-East

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Paperspace GPU pricing overview

Paperspace is a specialist GPU cloud provider headquartered in New York, NY. Paperspace (now part of DigitalOcean) offers A100, RTX 4000 ADA, and RTX 5000 ADA GPU instances alongside Gradient, its managed ML platform with Jupyter notebooks, experiment tracking, and one-click model deployment. On-demand and monthly billing options make it accessible for individuals and small teams exploring AI training and fine-tuning without complex infrastructure setup. A beginner-friendly on-demand GPU cloud with a polished notebook-centric experience. Billing is On-demand, Monthly with a minimum commitment of None. Available regions include US-East, US-West, EU-West. On-demand GPU instances can be provisioned in minutes with no upfront cost, making Paperspace suitable for both short-duration experiments and sustained production workloads.

Paperspace vs other GPU providers

Paperspace competes with providers including Lambda Labs, CoreWeave, RunPod, Paperspace, Vast.ai, and the major hyperscalers (AWS, Google Cloud, Azure) for GPU compute workloads spanning LLM training, fine-tuning, and inference serving. Key differentiators include: Managed ML platform; Jupyter notebooks; Simple UI. Use the side-by-side comparison tool above to see Paperspace pricing against any other provider across shared GPU models. For a broader market view, the live GPU prices table shows all 5 Paperspace listings alongside 94+ providers in a single sortable view.

Best use cases for Paperspace

Paperspace is best suited for: ML beginners, Notebook-based workflows, Small teams. Support tiers range from Community → Growth, making it viable for both individual researchers and enterprise teams with SLA requirements. There are currently 5 active GPU listings on Paperspace, covering H100 80GB, A100 80GB, A100 40GB, RTX 4090 and more. For workloads requiring the highest single-GPU throughput, H100 SXM5 instances with NVLink interconnect deliver the best performance per dollar at scale. For cost-sensitive fine-tuning or inference of models up to 13B parameters, A100 40GB or RTX 4090 instances typically offer the best value.

Paperspace billing model and cost structure

Paperspace uses On-demand, Monthly pricing. On-demand instances are billed per second or per hour depending on the instance type, with no termination fees. Spot pricing is not currently available on this provider — all instances are on-demand. Reserved instance pricing, where available, can reduce costs by 30–60% for predictable long-running workloads. Always compare the effective hourly rate including egress, storage, and networking costs when evaluating total cost of ownership across providers.

Choosing the right GPU on Paperspace

GPU selection depends on model size, precision, and whether your workload is compute-bound or memory-bandwidth-bound. For LLM training above 30B parameters, H100 80GB SXM5 instances with NVLink are the standard choice — the 3,350 GB/s HBM3 bandwidth and 989 TFLOPS FP16 throughput make them 2–2.5× faster than A100 for transformer workloads. For inference of 7B–13B models in FP16 or BF16, A100 40GB offers the best cost-per-token on most providers. RTX 4090 instances are ideal for fine-tuning, prototyping, and quantized inference (INT4/INT8) of models up to 70B. Read the H100 vs A100 guide or the GPU benchmarks for ML guide for a full breakdown.

How Paperspace pricing data is collected

Prices shown are sourced from Paperspace's public pricing API or pricing page and refreshed every 15 minutes. On-demand rates reflect the current list price for a single GPU instance in the cheapest available region. Spot prices, where available, reflect interruptible instance rates at the time of the last snapshot. All prices are in USD per hour. Daily snapshots are retained for 90 days and visualised in the GPU price history charts — useful for identifying seasonal pricing patterns and evaluating whether current rates are above or below the 30-day average.

Evaluating managed LLM inference APIs as an alternative to self-hosted GPU compute? Compare live LLM token prices across OpenAI, Anthropic, Google, Groq, and 14+ other providers. The cheapest GPU cloud guide covers the break-even analysis between self-hosted and managed inference at different request volumes.

Compare Paperspace with other providers

Side-by-side GPU pricing, spot rates, and available models. View all 102 provider comparisons →

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On-demand from $0.400/hr — 5 GPU configurations available. On-demand, Monthly billing.

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