Lambda Labs vs Vultr
A detailed comparison to help you choose between Lambda Labs and Vultr.
Lambda Labs On-demand GPU cloud for ML training and inference | Vultr High-performance cloud infrastructure with global data centers and competitive pricing | |
|---|---|---|
| Overview | ||
| Rating | 4.0 (158 reviews) | 4.2 (499 reviews)✓ |
| Pricing model | usage-based | paid |
| Starting price | Free tier available✓ | From €5/mo |
| Best for | ML researchers and engineers who need affordable, powerful GPU compute for training and experimentation without lock-in to larger cloud platforms. | Developers and DevOps teams building applications requiring low latency across multiple regions or those prioritizing cost-effective, API-first infrastructure management. |
| Specifications (entry plan) | ||
| CPU cores | 0 vCPU | 1 vCPU✓ |
| RAM | 0 GB | 1 GB✓ |
| Storage | 0 GB | 25 GB✓ |
| Bandwidth | 0 TB/mo | 1 TB/mo✓ |
| SLA uptime | 99.9% | 99.99%✓ |
| Data-center count | 3 | 32✓ |
| €/vCPU/mo | — | €5.00 |
| €/GB RAM/mo | — | €5.00 |
| Performance | ||
| CPU score (sysbench) | — | 4,100 |
| Disk read (fio) | — | 1,800 MB/s |
| Disk IOPS (4K random) | — | 38,000 |
| Network out | — | 1 Gbps |
| Latency (TTFB) | — | 24 ms |
| Features | ||
| IPv6 | ✓ | |
| DDoS protection | ✓ | |
| Automated backups | ✓ | |
| Snapshots | ✓ | |
| Managed option | ||
| Bare metal | ||
| GPU available | ||
| S3-compatible | ||
| Hourly billing | ||
| Free tier | ||
| Data-center locations | ||
| Regions | United States | United StatesUnited KingdomGermanyNetherlandsFranceAustralia+6 |
| Tags | ||
| Tags | hourly billinggpu availableus datacenterapi access | hourly billingnvme storageipv6ddos protectionbackups includedsnapshotsbare metalgpu availables3 compatibleeu datacenterus datacenterapac datacenterterraform providerapi accesswindows available |
| Visit Lambda Labs → | Visit Vultr → | |
Lambda Labs
Pros
- + Access high-end GPUs (A100, H100) at competitive hourly rates
- + Run bare-metal instances with minimal virtualization overhead
- + Get transparent, simple pricing without hidden fees
- + Deploy pre-configured ML environments in minutes
- + Benefit from high-speed GPU interconnects for multi-GPU training
Cons
- - Limited geographic availability compared to major cloud providers
- - Smaller ecosystem and fewer integrated services (databases, storage) than AWS/GCP
- - Less mature support and documentation than established competitors
Vultr
Pros
- + Deploy servers in 32 global locations with minimal latency
- + Access affordable pricing with hourly billing and no setup fees
- + Manage infrastructure via intuitive dashboard or REST API
- + Scale resources on-demand without contract commitments
Cons
- - Smaller ecosystem of pre-built applications compared to AWS or DigitalOcean
- - Support limited to ticketing system; no phone support on lower plans
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