Fluidstack GPU Cloud Compute
Reviews
Star rating
Rank in Infrastructure as a Service (IaaS)
Pros and cons
Pros
Cons
Traffic & LLM citations
Source: G2 review data
Key stats
Adoption by company size
| Segment | Share of reviews |
|---|---|
| Small Business | 69.2% |
| Mid-Market | 30.8% |
| Enterprise | 0.0% |
Quarterly history
| Quarter | Reviews | Star rating |
|---|
Satisfaction ratings
What reviewers say
Fast Bare-Metal Networking, But Requires DIY Managed Services Setup
“The biggest operational advantage is the direct bare-metal access coupled with the high-performance networking. When provisioning multi-node clusters for distributed LLM training or heavy batch inference, the inter-node communication bandwidth is fast and reliable. The spin-up ti”
Dedicated Single-Tenant Clusters with Hands-On Managed Support
“Dedicated, single-tenant clusters with managed support: Customers get isolated hardware that isn’t shared with other tenants. Fluidstack also handles the orchestration (managed Kubernetes or Slurm), monitoring, and 24/7 engineering support, so teams don’t have to run the infrastr”
Fast, Affordable Access to High-Spec GPUs for Scalable ML Compute
“What I like most is having instant access to high-spec GPUs (like H100s and A100s) at a fraction of hyperscaler prices. Provisioning nodes is quick, and SSH access is seamless. Overall, it makes it easy to scale up compute for heavy ML model training and inference, without runnin”