兩者的成長率相比如何？

FluidStack 的月增率為 +7.3%；Thunder Compute 為 -29%。

價格相比如何？

FluidStack：付費、需聯絡報價。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. Thunder Compute：Paid (per-minute instances, per-second sandboxes)。Thunder Compute 的執行個體在執行時按分鐘計費，沙箱按秒計費。公開執行個體費率：RTX A6000 每 GPU 小時 0.35 美元、L40 0.79 美元、A100 80GB 1.09 美元、H100 SXM 3.20 美元，額外 vCPU 為每 vCPU 小時 0.04 美元。沙箱費率為 H100 SXM 每 GPU 小時 2.95 美元、A100 80GB 每 GPU 小時 1.95 美元，另加 vCPU、記憶體與儲存費用。執行個體磁碟儲存為每 GB 每月 0.219 美元（每顆 GPU 前 100 GB 免費），快照為每 GB 每月 0.05 美元，資料傳出免費。特別提醒：費率會變動，請以定價頁為準。

FluidStack 與 Thunder Compute 有哪些替代方案？

相關分類中的其他工具包括 fal.ai, RunPod, Together AI, Vast.ai, Modal。

FluidStack vs Thunder Compute — 流量 (15.46萬 vs 10.21萬)、定價與選型結論

Open AI Tools

FluidStack vs Thunder Compute — 流量 (15.46萬 vs 10.21萬)、定價與選型結論 | Open AI Tools

流量與規格並排比較表

面向／指標	FluidStack	Thunder Compute
官方網域	fluidstack.io	thundercompute.com
定價模式	付費、需聯絡報價	Paid (per-minute instances, per-second sandboxes)
每月造訪次數	15.46萬 (154,620)	10.21萬 (102,147)
月流量變化量	+1.05萬 (+7.3%)	-4.17萬 (-29%)
主要市場與市佔	🇺🇸 美國 (95.7%)	🇺🇸 美國 (53.5%)
每月直接造訪	4.79萬	4.07萬
使用者評分	4.4	4.4
支援平台	網站	網站

FluidStack

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.

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.