FluidStack
Fluidstack · Other
Fluidstack is an AI cloud platform that gives teams on-demand access to large NVIDIA GPU clusters for training and running AI models, with flexible contracts and no long lock-ins.
About FluidStack
What Is Fluidstack
Fluidstack is a cloud computing company that builds and operates GPU infrastructure for AI workloads, and it's one AI GPU cloud provider among a growing field. Founded in 2017 and headquartered in New York, it runs data centers filled with high-end NVIDIA hardware and rents that capacity by the hour to AI labs, startups, and enterprises. If you need a few GPUs for a week or thousands for a year, Fluidstack is set up to handle both ends of that range.
The platform exists because getting serious compute is still hard. Big cloud providers sell general-purpose capacity and often ask for long commitments, while a research team might only need a cluster for a couple of months. GPU rental on these shorter terms is exactly the gap Fluidstack fills, and building its own data centers helps it deploy faster than traditional providers.
The trade-off is scope. This is a purpose-built AI infrastructure company, not a one-stop shop. You get GPUs, the networking around them, and the tooling to manage work, but you won't find the hundreds of managed services that come with the major hyperscalers. Teams that want a broad platform will still reach for something else.
Getting Started
- Go to the Fluidstack website and talk to the team about the GPU type, cluster size, and duration you need.
- Pick a hardware configuration and a contract length that matches your project timeline.
- Set up access to your provisioned cluster and load your training or inference workloads.
- Scale capacity up or down as your project moves from experimentation to production.
Product Information
A quick look at FluidStack's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- AI research labs
- AI startups
- Enterprise ML teams
Tasks
- Training large language models
- Running inference at scale
- Short-term research projects
- Fine-tuning open models
Scenarios
- A startup preparing to train its first foundation model
- An enterprise moving AI workloads off general-purpose clouds
- A lab running an experiment that needs thousands of chips for a month
Key features
Large-scale GPU clusters
Fluidstack assembles clusters that range from a single GPU to tens of thousands, all built on NVIDIA hardware. This scale matters for training runs that can't finish on a handful of cards, and it lets a team grow from a small proof of concept to a full production cluster without switching providers.
Fast deployment
The company designs and builds its own data centers, which it says lets it stand up capacity faster than conventional providers. For teams racing to hit a training deadline, the difference between weeks and months of setup can decide whether a project ships on time.
Flexible contracts
You can rent capacity for periods as short as hours or on longer reserved terms, depending on the project. That flexibility suits both a quick experiment and a long production run, and it means you aren't forced into a rigid multi-year plan for work that might change shape.
High-performance networking
Clusters are wired with the high-speed interconnects that distributed training depends on, so data moves between GPUs without becoming the bottleneck. Fast links matter most when a single model is spread across many nodes, since slow communication there can waste the compute you paid for.
Purpose-built AI infrastructure
Everything here is aimed at AI workloads rather than general web hosting. That focus shows up in the GPU selection, the cooling and power design, and the way capacity is managed. Think of it as machine learning infrastructure you don't have to assemble yourself, and it means the hardware isn't carrying overhead meant for unrelated services.
Capacity for training and inference
You can use the same provider for both training new models and serving them once they're ready. Keeping both phases under one roof cuts the friction of moving a finished model to a separate hosting platform.
Pros and cons
Pros
- Access to top-tier NVIDIA GPUs at a range of cluster sizes, from one card to tens of thousands.
- Flexible rental terms let teams match cost to project length instead of signing long commitments.
- Building its own data centers helps the company deploy capacity quickly when demand spikes.
- Works for both training and inference, so teams can keep both phases in one place.
- Longer reserved deals can bring the per-hour cost down compared with short-term rentals, which makes cloud GPU pricing easier to predict.
- The underlying AI training infrastructure is built around NVIDIA's data center lineup, so teams won't have to cobble together their own cluster.
Cons
- No published, self-serve pricing, so you have to contact sales before you know what you'll pay, which slows down budget planning.
- It's a focused AI compute provider rather than a full cloud platform, so teams expecting a wide menu of managed services will need another vendor for those.
- There's no free tier, which means individuals and hobbyists can't test the platform without a paid arrangement.
- Public documentation on the exact API and tooling is limited, so integration planning may require a direct conversation with the team.
Frequently asked questions
Fluidstack rents out large GPU clusters for AI work. Teams use it to train models, fine-tune open models, and run inference, especially when they need serious compute for a limited period rather than a permanent setup.
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