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 的每月流量與成長趨勢為何？

FluidStack 目前每月有 15.46萬次造訪，月流量變化為 +1.05萬（成長率 +7.3%）。其主要來源市場為美國（佔比 95.7%），其中包含每月 4.79萬次直接造訪與每月 — 次自然搜尋造訪。

FluidStack 是免費的嗎？其定價模式為何？

FluidStack 採用付費、需聯絡報價定價模式。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. 支援的帳務與結帳平台包括：stripe。

FluidStack — 月造訪量 (15.46萬/月)、定價、API 與替代方案

Open AI Tools

FluidStack — 月造訪量 (15.46萬/月)、定價、API 與替代方案 | Open AI Tools

關於 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 的高效使用情境

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.

前 12 大 FluidStack 替代方案與競爭對手

同一分類下同類 AI 工具的流量並排比較

查看所有 FluidStack 替代方案 →探索所有 AI 開發者工具工具 →

排名	AI 工具	每月造訪次數	月增幅	直接流量占比	價格	正面對決
●	FluidStack	15.46萬	+7.3%	31% 直接流量	付費、需聯絡報價	目前產品
1	RunPod 提供靈活的隨需 GPU 租賃與無伺服器端點，但缺乏 FluidStack 的超大規模企業級 GPU 叢集。	288.1萬	+5.9%	57% 直接流量	付費	對比 RunPod
2	Nebius 提供專屬的 NVIDIA GPU 叢集與託管雲端基礎設施，直接在企業級訓練工作負載上競爭。	114.57萬	+10.7%	39% 直接流量	付費、需聯絡報價	對比 Nebius
3	Vast.ai 提供用於低成本實驗的點對點 GPU 租賃市場，但企業級基礎設施的穩定預測性較低。	125.76萬	+2.8%	75% 直接流量	付費	對比 Vast.ai
4	Together AI 專注於優化的無伺服器推論 API 與託管模型代管，而非單純的裸叢集租賃。	164.59萬	+64.9%	54% 直接流量	免費增值、付費	對比 Together AI
5	Modal 提供用於雲端作業的無伺服器 Python 容器執行環境，但缺乏裸多節點叢集佈署控制。	119.6萬	+30.2%	71% 直接流量	免費增值、需聯絡報價	對比 Modal
6	Cerebras 採用獨家晶圓級處理器以實現極端訓練速度，有別於標準的 NVIDIA GPU 叢集設置。	70.1萬	-12.6%	42% 直接流量	需聯絡報價	對比 Cerebras