FluidStack vs Google Cloud: Pricing, Specs, Strengths & Weaknesses
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for FluidStack and Google Cloud. Rates sourced from public APIs — verify current availability and billing terms before purchasing.
Overview
Strengths & Best For
FluidStack has pivoted from a GPU cloud aggregator to a vertically-integrated AI infrastructure builder with a mission to ensure democracy wins the AI race. The company is building hundreds of gigawatts of solar-powered compute, constructing 250 MW data centers in six months from breaking ground, and onshoring manufacturing for compute and power infrastructure. Closed-loop cooling (water recirculates indefinitely), millions invested in local communities, and off-grid solar development. No public GPU pricing page — contact for enterprise capacity.
- Vertically-integrated AI infrastructure at gigawatt scale
- Closed-loop cooling — water recirculates indefinitely
- Off-grid solar development for sustainable compute
- 250 MW data centers built in 6 months from breaking ground
- Onshored manufacturing for compute and power infrastructure
- No self-serve on-demand pricing — enterprise contracts only
- Pivoted away from consumer/SMB GPU cloud
- Limited public information on current GPU catalog and pricing
No reviews yet — add yours ↗
Google Cloud's 2026 GPU lineup spans five accelerator-optimized machine series: A4X (NVIDIA GB200 Grace Blackwell Superchips, 720 GB HBM3e per 4-GPU node), A4 (NVIDIA B200, 1,440 GB HBM3e per 8-GPU node, 3,600 Gbps networking), A3 Ultra (NVIDIA H200, 1,128 GB HBM3e), A3 Mega/High/Edge (NVIDIA H100), and G4 (NVIDIA RTX PRO 6000 Blackwell, 96 GB GDDR7, GPU P2P communication). A2 series (A100 40/80 GB) and G2 (L4) remain available. AI Hypercomputer provides GKE and Slurm-integrated dense clusters. TPU v5 and v5e accelerators are available for TensorFlow/JAX workloads. Vertex AI provides managed ML pipelines. 30+ regions with sustained use and committed use discounts.
- A4X GB200 and A4 B200 — latest Blackwell hardware in cloud
- G4 RTX PRO 6000 with GPU P2P communication for inference
- AI Hypercomputer: GKE + Slurm-integrated dense clusters
- TPU v5/v5e for TensorFlow and JAX workloads
- Sustained use discounts and committed use contracts
- Vertex AI managed ML pipelines
- A4X and A4 require capacity reservation — not self-serve on-demand
- Premium pricing vs specialist GPU clouds
- Spot (preemptible) instances can be interrupted with little notice
No reviews yet — add yours ↗
Live GPU Pricing
Region Coverage
Popular Comparisons
Provider pages
More FluidStack comparisons
Key differences
FluidStack
Building compute faster than anyone — hundreds of GW solar, 250 MW data centers in 6 months
Google Cloud
A4X (GB200), A4 (B200), A3 Ultra (H200), G4 (RTX PRO 6000) — plus TPU v5 and AI Hypercomputer