CoreWeave vs TensorDock
A detailed comparison to help you choose between CoreWeave and TensorDock.
Quick Verdict
3.6/5
CoreWeave
0 reviews
3.8/5
TensorDock
0 reviews
TensorDock is rated higher (3.8 vs 3.6). CoreWeave is a purpose-built GPU cloud for AI — 40,000+ NVIDIA GPUs including H100, A100, and RTX. Used by OpenAI, Mistral, and Cohere. Kubernetes-native and connected to NVIDIA. TensorDock is a GPU compute marketplace with per-second billing — H100 SXM5 from $2.80/hour and A100 from $1.50/hour (A100 PCIe 80GB) on its published rate card. 100+ locations in 20+ countries, on-demand provisioning.
CoreWeave Purpose-built cloud for AI and GPU workloads | TensorDock Affordable GPU cloud compute without long-term contracts | |
|---|---|---|
| Overview | ||
| Rating | 3.6 (0 reviews) | 3.8 (0 reviews)✓ |
| Pricing model | usage-based | usage-based |
| Starting price | Free tier available | From $0.1/mo |
| Best for | AI labs and ML teams running sustained training or high-throughput inference where dedicated GPUs, NVLink/InfiniBand and a real uptime SLA justify premium pricing | Cost-conscious ML engineers, researchers and studios running training with checkpointing, batch inference or rendering, especially where consumer GPUs like the RTX 4090 fit the workload |
| Specifications (entry plan) | ||
| CPU cores | — | 2 vCPU |
| RAM | — | 4 GB |
| Storage | — | 20 GB |
| Bandwidth | — | — |
| SLA uptime | 99.9% | 99.99%✓ |
| Data-center count | 50 | 100✓ |
| €/vCPU/mo | — | €0.05 |
| €/GB RAM/mo | — | €0.03 |
| Features | ||
| IPv6 | ||
| DDoS protection | ||
| Automated backups | ||
| Snapshots | ||
| Managed option | ||
| Bare metal | ||
| GPU available | ||
| S3-compatible | ||
| Hourly billing | ||
| Free tier | ||
| Data-center locations | ||
| Regions | 50+ data centers across the United States and Europe | 100+ marketplace locations in over 20 countries (US, EU, Asia-Pacific); tier 3/4 data centers plus community hosts |
| Tags | ||
| Tags | hourly billinggpu availableus datacenterapi accesskubernetes support | hourly billinggpu availableeu datacenterus datacenter |
| Visit CoreWeave → | Visit TensorDock → | |
CoreWeave
Pros
- + Published public pricing for the main GPU lines — H100 node $49.24/hr on-demand, $19.71/hr spot — which is more transparency than most GPU clouds offer
- + Broad NVIDIA catalog (L40 to GB300 NVL72) with managed Kubernetes and Slurm control planes included and InfiniBand at up to 3,200 Gbps
- + Contractual reliability: 99.9% cross-region SLA (99% single region) with service credits, plus SOC 2 and ISO posture
- + 50+ data centers across the US and Europe, and S3-compatible object storage for training data and checkpoints
Cons
- - Premium per-GPU rates versus spot resellers — often 2-3x higher per GPU-hour for comparable silicon
- - Whole 8-GPU nodes are the default unit of sale; some configurations still route through sales and component pricing (classic line) complicates the bill
- - No cheap entry tier and no general-purpose cloud story — the fit is poor for anything that is not GPU-bound
- - Counterparty concentration: heavy NVIDIA dependence plus the debt-financed build-out of a young public company
TensorDock
Pros
- + Among the lowest published GPU rates anywhere: RTX 4090 at $0.35/hr, H100 SXM5 at $2.85/hr typical
- + A la carte, per-second billing for vCPU, RAM and NVMe — pay only for the configuration you need
- + Breadth: 45 GPU models across 100+ locations in 20+ countries, with an API for automation
- + Published rate card and transparent explanation of why the same GPU has multiple prices
- + Host vetting, host SSH access revoked, and a 99.99% uptime standard for listed hosts
Cons
- - Marketplace variance: host redundancy and quality differ, with cheaper listings often least redundant
- - Email-only support with multi-hour response windows; no contractual SLA with credits
- - Prepaid balance model deletes servers when the balance reaches zero
- - Published rate card is stamped July 2024 and carries a no-guarantee disclaimer
- - Fewer managed niceties (templates, serverless) than RunPod, Lambda or hyperscalers
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