Vast.ai vs Beam Cloud
A detailed comparison to help you choose between Vast.ai and Beam Cloud.
Vast.ai Rent GPUs from individuals for 2-10x cheaper compute | Beam Cloud Serverless GPU infrastructure with per-second billing and instant scaling | |
|---|---|---|
| Overview | ||
| Rating | 4.2 (65 reviews) | 4.3 (307 reviews)✓ |
| Pricing model | usage-based | usage-based |
| Starting price | Free tier available | Free tier available |
| Best for | Machine learning researchers, indie game developers, and budget-conscious teams running non-critical batch workloads who can tolerate occasional interruptions. | Teams deploying AI inference APIs, batch ML jobs, or GPU-accelerated workloads that need cost-efficient scaling without long-term commitments. |
| Specifications (entry plan) | ||
| CPU cores | 0 vCPU | — |
| RAM | 0 GB | — |
| Storage | 0 GB | — |
| Bandwidth | 0 TB/mo | — |
| SLA uptime | — | — |
| Data-center count | 0 | — |
| 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 | hourly billinggpu availableeu datacenterus datacenterapac datacenter | free tiergpu availableus datacenterapi access |
| Visit Vast.ai → | Visit Beam Cloud → | |
Vast.ai
Pros
- + Achieve significant cost savings compared to major cloud providers
- + Access diverse GPU types without long-term commitments
- + Deploy instances in seconds with minimal setup
- + Bid competitively to secure even lower rates
Cons
- - Provider uptime and reliability vary; some instances may disconnect unexpectedly
- - Network speeds and hardware quality inconsistent across providers
- - Limited enterprise support and SLAs compared to traditional cloud
Beam Cloud
Pros
- + Pay only for compute used with per-second granularity, no minimum charges
- + Scale to zero automatically between requests, reducing idle infrastructure costs
- + Deploy containerized workloads with no vendor lock-in using standard Docker images
- + Integrate GPU-accelerated inference models directly into Python applications
Cons
- - Limited regional availability compared to major cloud providers
- - Requires containerization knowledge; less suitable for simple HTTP endpoints
- - Per-request cold start latency may exceed 5 seconds on first invocation
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