How much does FluidStack cost?

FluidStack's website does not publish self-serve instance pricing. Access is arranged through a sales engagement, and the tools directory lists it as a paid, contact-for-pricing product. Teams with large training or inference requirements should reach out directly for a quote.

Who is FluidStack designed for?

It is aimed at AI labs, model developers and enterprises that need large blocks of GPU capacity for training and inference, from single clusters up to very large multi-site deployments.

What hardware does FluidStack provide?

The company describes building GPU clusters with modern NVIDIA accelerators for AI training and inference, and its public materials emphasise designing compute, power and cooling from first principles at very large scale.

Where is FluidStack's infrastructure located?

Its clusters are built and operated in the United States, with supporting manufacturing onshored and off-grid solar being developed to power compute as it scales.

FluidStack 的月流量和增长趋势如何？

FluidStack 目前每月获得 15.46万访问量，月度环比流量变化为 +1.05万（增长率 +7.3%）。其主要来源市场是美国（95.7% 份额），包括每月 4.79万次直接访问和每月 — 次自然搜索访问。

FluidStack 免费吗？它的定价模式是什么？

FluidStack 采用付费，定价请咨询定价模式。Enterprise and contact-for-pricing. FluidStack does not publish per-GPU-hour rates or a self-serve sign-up on its website; capacity is scoped and quoted through a direct sales conversation based on cluster size, duration and hardware. Catalogue listings classify it as a paid product requiring contact for pricing. 支持的计费和结账平台包括：stripe。

FluidStack — 月访问量 (15.46万/月)、定价、API 与平替对比

Open AI Tools

FluidStack — 月访问量 (15.46万/月)、定价、API 与平替对比 | Open AI Tools

关于 FluidStack

Datacenter-scale compute built in-house

FluidStack designs and builds large AI data centers, including high-density campuses with significant per-site power capacity, rather than reselling capacity from other clouds.

GPU clusters for training and inference

Clusters are architected for large-scale model training and inference, with high-bandwidth interconnect and orchestration suitable for distributed workloads.

Power and cooling engineered together

The company develops off-grid solar and recirculating closed-loop cooling, treating power and water efficiency as core engineering problems.

Managed operations and reliability

Smart hands, cluster provisioning and operations support let research teams focus on models while FluidStack runs the physical and orchestration layers.

US-built and onshored supply chain

Data centers are built in the United States with onshored manufacturing of compute and power infrastructure, a deliberate supply-chain and sovereignty choice.

FluidStack builds and operates large-scale compute infrastructure for AI, including gigawatt-class data centers and GPU clusters used for training and inference. The company designs its own power, cooling and data center systems, onshores manufacturing and deploys solar generation to supply the power its clusters need. Rather than a self-serve cloud console, FluidStack works with AI labs and enterprises to provision large reserved clusters, with orchestration tooling for the ML frameworks and schedulers those teams already use.

FluidStack 的高影响力用例

ML platform engineer

Train large models at scale

Reserve large, dedicated GPU clusters for multi-week or multi-month runs where network topology and uninterrupted availability dominate the decision.

Infrastructure lead

Serve models in production

Stand up capacity for serving models in production with managed operations, instead of assembling colocation, power and cooling yourself.

AI lab operations

Plan power-constrained expansion

Work with a partner that can design power generation and cooling alongside compute, so energy availability does not become the bottleneck to scaling.

Enterprise technology strategist

Meet sovereignty and location requirements

Evaluate a dedicated, US-located AI data center option for workloads with data-residency or sovereignty requirements.

顶级 12 个 FluidStack 替代方案与竞争对手

同一分类下同类 AI 工具的流量并排对比

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排名	AI 工具	月访问量	月度环比增长	直接流量占比	定价	头对头
●	FluidStack	15.46万	+7.3%	31% 直接流量	付费，定价请咨询	当前产品
1	RunPod 提供灵活的按需 GPU 租赁和无服务器端点，但缺乏 FluidStack 那样的超大规模企业级 GPU 集群。	288.1万	+5.9%	57% 直接流量	付费	对比 RunPod
2	Nebius 提供专属的 NVIDIA GPU 集群和托管云基础设施，直接在企业级训练工作负载上展开竞争。	114.57万	+10.7%	39% 直接流量	付费，定价请咨询	对比 Nebius
3	Vast.ai 提供用于低成本实验的点对点 GPU 租赁市场，不过企业级基础设施的可预测性相对较低。	125.76万	+2.8%	75% 直接流量	付费	对比 Vast.ai
4	Together AI 专注于优化的无服务器推理 API 和托管模型托管，而不是单纯的裸集群租赁。	164.59万	+64.9%	54% 直接流量	免费增值，付费	对比 Together AI
5	Modal 提供用于云端任务的无服务器 Python 容器运行环境，但缺乏原始多节点集群的部署控制。	119.6万	+30.2%	71% 直接流量	免费增值，定价请咨询	对比 Modal
6	Cerebras 采用独家晶圆级处理器以实现极端的训练速度，有别于标准的 NVIDIA GPU 集群设置。	70.1万	-12.6%	42% 直接流量	定价请咨询	对比 Cerebras