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FluidStack

FluidStack: On-demand GPU servers for ML, rendering, and general compute tasks.

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What is FluidStack?

FluidStack provides on-demand GPU servers sourced from a global network of underutilised data centre capacity. The platform aggregates GPUs from Tier 2-4 data centres worldwide, offering access to NVIDIA hardware like A100 80GB, RTX A6000, and RTX 3090 at significantly lower costs than mainstream cloud providers. It's designed for machine learning engineers, researchers, and companies running compute-intensive workloads who need flexible, scalable GPU resources without long-term commitments. FluidStack handles provisioning through a simple web interface and API, with one-click deployment using custom images. The service includes an AI compliance agent called Asenion for monitoring and security purposes.

Key features

On-demand GPU access

Provision NVIDIA GPUs within minutes from a global pool of over 47,000 servers

Multiple GPU options

Choice of A100 80GB, RTX A6000, RTX 3090, and other enterprise-grade GPUs

API and web dashboard

One-click deployment with custom image support and programmatic access

Transparent pricing

All-inclusive rates with no hidden charges or surprise billing

Compliance monitoring

Asenion AI agent provides security and compliance oversight

24/7 support

Direct access to technical support through multiple channels

Pros & cons

Advantages

  • Significantly cheaper than AWS, Google Cloud, or Azure GPU offerings, typically 3-5x lower cost
  • Fast provisioning and high uptime guarantees (99.995%) with Tier 2-4 data centre infrastructure
  • Flexible consumption model: pay only for what you use without locked-in commitments
  • Wide range of GPU types available in one place, simplifying hardware selection
  • Direct support access rather than ticketing systems used by larger providers

Limitations

  • Smaller network of servers means less redundancy and potential availability constraints during peak demand
  • Less established brand recognition compared to hyperscale cloud providers, which may affect trust for mission-critical workloads
  • Reliance on secondary data centre capacity means availability of specific GPU models can fluctuate

Use cases

Machine learning model training and fine-tuning, especially for teams with budget constraints

Large-scale rendering tasks for animation studios or visual effects companies

LLM training and inference workloads for research organisations and startups

Data processing pipelines requiring temporary compute bursts

GPU-accelerated scientific computing and simulations

Ready to try FluidStack?

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Pricing

Pay-as-you-go

Varies by GPU type and region

Hourly billing for GPU compute resources; transparent pricing; minimum commitment not specified

Get started with FluidStack

Click through to FluidStack and start using it now.

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