G2

Amir K.

Information Technology Manager

Small-Business (50 or fewer emp.)

10/20/2026

"Fast, Intuitive GPU Clusters with Seamless Kubernetes/Slurm Integration"

4/5

What do you like best about Fluidstack GPU Cloud Compute?

I regularly use their on-demand NVIDIA H100 and A100 GPU clusters to process my deep learning models and run LLM inference. It saves me at least 10–15 hours of waiting time each week when scheduling our training jobs. Their web console UI is intuitive, and the integration with Kubernetes and Slurm is seamless, which lets us run large datasets quickly without any noticeable network lag. One unexpected but genuinely valuable benefit is the absence of egress fees; it removes the worry about surprise charges when syncing terabytes of checkpoints or vector embeddings out of the platform. During onboarding, their support engineers helped us with cluster configuration in about 15 minutes, so our pipeline could go live right away without any technical hurdles. Review collected by and hosted on G2.com.

What do you dislike about Fluidstack GPU Cloud Compute?

When reserving very large, custom clusters, the web dashboard’s real-time, granular view of instance availability can sometimes be slow to refresh. If they added a dynamic calendar-style scheduler directly in the console that shows upcoming GPU slots along with region-specific quotas, it would make resource planning much easier. Review collected by and hosted on G2.com.

What problems is Fluidstack GPU Cloud Compute solving and how is that benefiting you?

Previously, we struggled with traditional hyperscalers because hardware provisioning took too long, data transfer costs were unpredictable, and GPU quotas were limited. Now we can spin up high-capacity, dedicated GPU nodes in minutes, which has accelerated our model development cycles by 35%. With infrastructure costs roughly 50% lower than major cloud providers and no data transfer fees at all our team has seen an immediate, clearly positive ROI. Review collected by and hosted on G2.com.