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FluidStack vs TensorDock

A detailed comparison to help you choose between FluidStack and TensorDock.

Quick Verdict

3.6/5

FluidStack

0 reviews

3.8/5

TensorDock

0 reviews

TensorDock is rated higher (3.8 vs 3.6). FluidStack aggregates GPU capacity from multiple providers to offer H100, A100, and consumer GPU instances. Pay-as-you-go and reserved capacity. EU and US datacenters with competitive pricing. 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.

FluidStack

FluidStack

Enterprise GPU cloud at competitive pricing

TensorDock

TensorDock

Affordable GPU cloud compute without long-term contracts

Overview
Rating3.6 (0 reviews)3.8 (0 reviews)✓
Pricing modelusage-basedusage-based
Starting priceFrom $1.3/moFrom $0.1/mo✓
Best forAI labs and platform teams committing to 8-10k+ GPUs for training or heavy inference who want managed Slurm/Kubernetes, per-GPU-hour transparency, and a hands-on infrastructure partnerCost-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—99.99%
Data-center count—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
Canada, Netherlands, Norway, Sweden
100+ marketplace locations in over 20 countries (US, EU, Asia-Pacific); tier 3/4 data centers plus community hosts
Tags
Tags
hourly billinggpu availableeu datacenterus datacenter
hourly billinggpu availableeu datacenterus datacenter
Visit FluidStack →Visit TensorDock →

FluidStack

Pros

  • + Published per-GPU-hour rate card with no egress or ingress fees
  • + Fully managed Slurm and Kubernetes on bare metal with 24/7 engineering support
  • + Scales from 8 GPUs on-demand to 10k+ reserved — cluster sizes most clouds cannot match quickly
  • + EU capacity in the Netherlands and Nordics for data-residency-sensitive training
  • + Engineering response times measured in hours by SemiAnalysis during ClusterMAX testing

Cons

  • - Aggregated third-party capacity means hardware and day-one polish vary by deployment
  • - Contract-first commercial model — 8-GPU on-demand floor, 256-GPU reserved floor, no free tier
  • - No public uptime SLA, datacenter list, or documentation library
  • - Reactive software supply chain security — unpatched NVIDIA container toolkit found at cluster handover in evaluation
  • - Track record is short relative to the scale of its recent customer commitments
View full FluidStackreview →

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
View full TensorDockreview →

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