FluidStack
An AI cloud platform providing large-scale GPU clusters for training and inference.
What is FluidStack?
Fluidstack is a leading AI cloud platform designed for large-scale machine learning workloads, specialized in training foundation models and running inference at scale. Operating as a premier choice for high-end compute, the Fluidstack startup provides instant access to thousands of NVIDIA GPUs (including H100s, H200s, and upcoming GB200s) on its fully managed infrastructure. Often utilized by major AI labs—including instances like Anthropic Fluidstack collaborations—the platform helps enterprises deploy massive GPU clusters with dedicated orchestration layers like Atlas OS, Slurm, and Kubernetes.
Best FluidStack use cases by task, role, industry, and platform
These use cases show where FluidStack fits best, ranked by fit score before popularity or pricing.
FluidStack Pricing Plans
Compare FluidStack free options, FluidStack paid pricing plans, and usage notes before you choose the best way to use this AI tool in 2026.
Starts at $1.30/hr
48GB VRAM, Max 24 vCPUs per GPU, Max 64GB RAM per GPU
80GB VRAM, Max 32 vCPUs per GPU, Max 128GB RAM per GPU
80GB VRAM, Max 48 vCPUs per GPU, Max 256GB RAM per GPU
80GB VRAM, Max 48 vCPUs per GPU, Max 256GB RAM per GPU
141GB VRAM, Max 48 vCPUs per GPU, Max 256GB RAM per GPU
Requires 12+ month commitments of large, InfiniBand connected H100 clusters. Term starts at 30 days or longer for 8 to 10K+ GPUs.
Pricing updated:Jun 12, 2026
FluidStack AI Features
FluidStack Pros and Cons
Pros
- Saves up to 70% on cloud bills compared to traditional hyperscalers
- Industry-leading 99% uptime with automated remediation
- Highly scalable from single on-demand instances to 10,000+ GPU clusters
- Fully managed infrastructure team handles physical operations, letting engineers focus on models
Limitations
- Reserved clusters require a minimum commitment of 30 days or longer
- Pricing for top-tier hardware like the Nvidia H200 is only available upon request