Fluidstack
Civilization-scale infrastructure for AI
Last reviewed Mar 14, 2026
AI cloud platform that also acquires power, designs, builds, and operates its own data centers. Runs bare-metal GPU clusters for AI labs, governments, and enterprises, and was selected by Anthropic to deliver custom data centers in New York and Texas.
Fluidstack does not publish public hourly pricing.
Contact the provider directly for a quote.
Pros & Cons
Advantages
- Purpose-built infrastructure for AI workloads with enterprise partnerships
- Large-scale GPU availability with rapid deployment capabilities
- Fully managed SLURM or Kubernetes orchestration on bare metal
- Vertically integrated: designs, builds, and operates its own data centers
- No data transfer fees and included local storage
- 24/7 engineering support with 15-minute response SLA
- Single-tenant infrastructure with no noisy neighbors
- Flexible scaling from 8 GPUs to 10,000+ GPU clusters
- Performance-tested hardware with 95%+ efficiency guarantee
Limitations
- No published pricing — requires contacting sales for a quote
- Enterprise-focused platform may be complex for small-scale users
- Primary focus on AI and ML workloads may not suit general compute needs
- Newer player compared to established hyperscale cloud providers
- Custom solutions may require longer setup times than self-service options
Key Features
Data Center Development and Operation
Acquires power, designs and builds data centers, and operates them, with in-house hardware and software teams
Managed Kubernetes
Bare-metal container orchestration with NVIDIA GPU Operator and Network Operator support, passive and active health checks, and job/cluster observability
Managed Slurm
Bare-metal batch orchestration with user management, Pyxis/Enroot, health checks, and topology-aware scheduling to reduce collective communication latency
Lighthouse
Monitoring and optimization system that catches problems before they impact workloads
Single-Tenant by Default
Fully isolated infrastructure at hardware, network, and storage levels with no shared clusters
24/7 Engineering Support
Direct engineering support with 15-minute response SLA and secure access controls
No Hidden Fees
No egress or ingress fees, with on-node NVMe storage included
Performance Guarantee
Clusters tested to deliver 95%+ of theoretical performance from day one
Compute Services
GPU Clusters
Dedicated, high-performance GPU clusters that are fully isolated, fully managed, and always available.
Pricing Options
| Option | Details |
|---|---|
| Reserved Clusters | Designed for large-scale training and inference, deployed on fully managed cloud infrastructure. 256-10,000+ GPUs with monthly or annual terms and discounted rates. |
| On Demand | Launch GPU instances in under 5 minutes and seamlessly scale to 100s of GPUs on-demand. 8-4,000+ GPUs with hourly billing. |
| Private Cloud | Custom dedicated clusters for complex needs with flexible terms and region-specific deployments. |
Getting Started
- 1
Contact expert
Talk to a Fluidstack expert to discuss your specific AI infrastructure needs
- 2
Request pricing
Get custom pricing for your GPU cluster requirements
- 3
Deploy infrastructure
Launch your dedicated GPU cluster with fully managed support
Frequently Asked Questions
What GPU types does Fluidstack offer?
Check the pricing table above for Fluidstack's current GPU availability and pricing.
How do I get started with Fluidstack?
Contact expert, Request pricing, Deploy infrastructure
What are Fluidstack's main advantages?
Fluidstack's main advantages include: Purpose-built infrastructure for AI workloads with enterprise partnerships, Large-scale GPU availability with rapid deployment capabilities, Fully managed SLURM or Kubernetes orchestration on bare metal, Vertically integrated: designs, builds, and operates its own data centers, No data transfer fees and included local storage, 24/7 engineering support with 15-minute response SLA, Single-tenant infrastructure with no noisy neighbors, Flexible scaling from 8 GPUs to 10,000+ GPU clusters, Performance-tested hardware with 95%+ efficiency guarantee.
What are Fluidstack's limitations?
Fluidstack's main limitations include: No published pricing — requires contacting sales for a quote, Enterprise-focused platform may be complex for small-scale users, Primary focus on AI and ML workloads may not suit general compute needs, Newer player compared to established hyperscale cloud providers, Custom solutions may require longer setup times than self-service options.