Paperspace (Gradient) vs Vast.ai
A detailed comparison to help you choose between Paperspace (Gradient) and Vast.ai.
Quick Verdict
3.4/5
Paperspace (Gradient)
0 reviews
3.7/5
Vast.ai
0 reviews
Vast.ai is rated higher (3.7 vs 3.4). M4000 GPU machines from $0.45/hour P2P GPU marketplace connecting researchers and developers with spare compute capacity. Offers rented GPUs at lower rates than centralized cloud providers, ideal for ML training, rendering, and batch processing.
Paperspace (Gradient) ML platform and GPU cloud by DigitalOcean | Vast.ai Rent GPUs from individuals for 2-10x cheaper compute | |
|---|---|---|
| Overview | ||
| Rating | 3.4 (0 reviews) | 3.7 (0 reviews)✓ |
| Pricing model | freemium | usage-based |
| Starting price | Free tier available✓ | From $0.03/mo |
| Best for | Individuals, students and existing users wanting managed GPU notebooks and cheap older-generation GPU machines without building infrastructure. | ML researchers, rendering studios, and budget-conscious teams running checkpointable training, fine-tune, and batch workloads who are willing to vet hosts themselves |
| Features | ||
| IPv6 | ||
| DDoS protection | ||
| Automated backups | ||
| Snapshots | ||
| Managed option | ||
| Bare metal | ||
| GPU available | ||
| S3-compatible | ||
| Hourly billing | ||
| Free tier | ✓ | |
| Data-center locations | ||
| Regions | — | Global — distributed hosts |
| Tags | ||
| Tags | free tierhourly billinggpu availableus datacenterapi access | hourly billinggpu availableeu datacenterus datacenterapac datacenter |
| Visit Paperspace (Gradient) → | Visit Vast.ai → | |
Paperspace (Gradient)
Pros
- + Cheap GPU access on older cards: M4000 at $0.45/hour, P4000 at $0.51/hour
- + Free tier plus $8/month Pro plan makes managed GPU notebooks accessible
- + Machines include persistent storage and free unlimited bandwidth
- + Notebook-to-deployment ML workflow (notebooks, workflows, deployments) in one product
- + Amsterdam region gives EU machine placement
Cons
- - Being sunset into DigitalOcean's platform — standalone roadmap and APIs already replaced
- - ML platform (Notebooks/Deployments/Workflows) runs in NY2 only; no APAC presence
- - Newest GPUs (A100/H100/RTX5000) are NY2-only; rate cards have changed recently
- - Team tiers cap at 2-5 members; storage overages at $0.29/GB
Vast.ai
Pros
- + Cheapest public GPU-hour rates anywhere; per-second billing with no minimum charge
- + Always-on capacity across 81 GPU types and hosts in 52 countries — no 'capacity coming soon'
- + Interruptible bidding and reserved discounts give real pricing levers per workload
- + Dense, useful search filters: reliability score, DLPerf, network speed, host tier
- + SOC 2 Type II and HIPAA attestations at the platform level, per third-party comparisons
Cons
- - No SLA, no owned datacenters, and no recourse when an independent host disappears
- - Host variance is real: measured ±11.7% throughput spread on identical H100 SXM specs
- - Storage bills continue on stopped instances; bandwidth is host-set and can be expensive
- - Interruptible preemption risk demands checkpointing discipline
- - Verification and reliability-score formulas are only partially disclosed
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