# FluidStack vs RunPod

FluidStack 记录了 154,620 月访问量，月度环比增长为 +7.3%，定价为付费，定价请咨询。RunPod 记录了 2,881,043 月访问量，增长率为 +5.9%，定价为付费。

| 指标 | [FluidStack](https://openaitools.app/zh/tool/fluidstack) | [RunPod](https://openaitools.app/zh/tool/runpod) |
| :--- | :--- | :--- |
| 官网地址 | https://fluidstack.io | https://runpod.io |
| 分类 | AI Developer Tools, AI Models, Large Language Models (LLMs) | AI Developer Tools, AI Models, Large Language Models (LLMs), AI API |
| 定价模式 | 付费，定价请咨询 | 付费 |
| 月访问量 | 154,620 | 2,881,043 |
| 月度环比增长率 | +7.3% | +5.9% |
| 主要市场 | 美国 | 美国 |
| 直接访问量 | 47,869 | 1,634,834 |
| 评价得分 | 4.4 | 4.7 |
| 收藏数 | 1 | 4 |
| 支持平台 | 网站 | 网站 |

## 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.

**定价与套餐:** 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.

- **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.

**优势:** Purpose-built AI infrastructure rather than generic cloud resale; Power, cooling and data center design handled in-house, which matters at scale; Managed provisioning and operations for large training and inference clusters; Explicit positioning on US-built, onshored infrastructure

**局限:** No published self-serve pricing or instant sign-up; access requires a sales conversation; Enterprise-scale focus means very small individual workloads may not be a fit; Capacity is concentrated in US locations, which may matter for latency-sensitive or data-residency-bound teams

[工具详情（Markdown）](https://openaitools.app/zh/tool/fluidstack.md)

## RunPod

平台围绕三类产品组织。Pods 提供带自有容器、框架与代码的持久 GPU 实例，既有保障性的预留容量，也有价格更低的抢占式实例。Serverless 把容器变成可自动扩缩的 HTTP 或队列驱动端点，随负载启停 worker。Clusters 跨节点连接大量 GPU，用于分布式训练与大规模推理，并可挂载共享存储。围绕这些产品还有可部署模板的 Model Hub 以及托管图像、音频、文本模型的公共端点。

在运行层面，RunPod 强调快速供给与归零成本。一个 GPU Pod 不到一分钟即可运行，工作负载分布在 31 个区域，Serverless 端点无需手写编排即可自动从 0 扩展到数百个并发 worker。存储具备持久性并按层级定价，适合在多轮运行之间保留检查点与数据集。

RunPod 受到开发者、研究团队和 AI 初创公司欢迎，因为它无需与超大规模云厂商谈长期合约，同时仍提供 SOC 2 Type II 合规、99.9% 可用性目标与故障转移等企业特性。它最适合熟悉容器、希望对 GPU 成本与配置拥有直接掌控力的团队。

**定价与套餐:** 付费，按量计费。GPU Pods 按显卡类型按秒或按小时计费，Serverless 端点在空闲时归零、仅为实际算力付费，Clusters 按每 GPU 每小时收费。存储约从每月 $0.05/GB 起。另有免费额度和企业预留容量合约。

- **按需 GPU Pods:** 按需、可中断或预留方式租用 GPU 实例，覆盖 30 多种 GPU 型号，从 RTX 4090 到 B200、B300 级别加速卡，部署于全球 31 个区域。
- **自动扩缩 Serverless 端点:** 把任意容器部署为 API 端点，按需从 0 扩展到数千个 worker，仅为实际使用的算力付费。
- **多节点 Clusters:** 启动多节点 GPU 集群用于分布式训练与大规模推理，可挂载共享存储，且无需长期承诺。
- **Model Hub 与公共端点:** 从精选中枢部署热门开源模型，或使用公共端点调用图像、音频和文本模型，无需自建基础设施。
- **持久化存储:** 标准与高性能分层的持久网络存储、容器盘与卷盘，且完整 AI 流水线不收取出网费。

**优势:** GPU 型号覆盖极广，从消费级到数据中心级显卡齐备; 按秒计费加 Serverless 归零，空闲成本几乎为零; 借助模板或自有容器可快速配置环境; 全球区域覆盖，支持持久存储且免出网费

**局限:** 高端显卡在不同地区和时段的可租用数量会有波动; Serverless 冷启动与弹性伸缩行为需要一定调优; Pod 若长期空转，成本会迅速上升; 容器与 handler 配置的学习曲线比全托管模型 API 更陡

[工具详情（Markdown）](https://openaitools.app/zh/tool/runpod.md)

## 常见问题

### FluidStack 和 RunPod 在月流量上如何比较？

FluidStack 有 154,620 月访问量，而 RunPod 有 2,881,043。流量估算描述的是受众规模，而非产品质量。

### 它们的增长率如何比较？

FluidStack 的月度环比增长率为 +7.3%；RunPod 为 +5.9%。

### 价格如何比较？

FluidStack: 付费，定价请咨询。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. RunPod: 付费。付费，按量计费。GPU Pods 按显卡类型按秒或按小时计费，Serverless 端点在空闲时归零、仅为实际算力付费，Clusters 按每 GPU 每小时收费。存储约从每月 $0.05/GB 起。另有免费额度和企业预留容量合约。

## 替代工具

| # | 工具名称 | 月访问量 | 月度环比增长率 | 定价模式 | 简介 | 工具详情（Markdown） |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [OpenRouter](https://openaitools.app/zh/tool/openrouter-ai) | 19,941,437 | -6.2% | 付费 | Provides a unified API gateway across hundreds of models, but entirely abstracts away underlying GPU infrastructure management. | [.md](https://openaitools.app/zh/tool/openrouter-ai.md) |
| 2 | [fal.ai](https://openaitools.app/zh/tool/fal-ai) | 3,093,684 | -3.7% | Paid, pay-as-you-go usage-based pricing | Specializes in lightning-fast serverless execution for generative media models, but lacks general-purpose GPU pod rental options. | [.md](https://openaitools.app/zh/tool/fal-ai.md) |
| 3 | [Together AI](https://openaitools.app/zh/tool/together-ai) | 1,645,930 | +64.9% | 免费增值，付费 | 提供托管的无服务器模型推理与 GPU 集群，以简化的 API 访问取代了裸机容器的自由定制。},{de: | [.md](https://openaitools.app/zh/tool/together-ai.md) |
| 4 | [Vast.ai](https://openaitools.app/zh/tool/vast-ai) | 1,257,554 | +2.8% | 付费 | 提供点对点的 GPU 租赁市场，价格通常更便宜，但硬件稳定性比 RunPod 更不稳定。 | [.md](https://openaitools.app/zh/tool/vast-ai.md) |
| 5 | [Modal](https://openaitools.app/zh/tool/modal-com) | 1,196,021 | +30.2% | 免费增值，定价请咨询 | 提供无服务器 Python 执行与即时容器扩展，但缺乏像 RunPod 那样直接的持久化 SSH 容器控制。},{de: | [.md](https://openaitools.app/zh/tool/modal-com.md) |
| 6 | [Nebius](https://openaitools.app/zh/tool/nebius) | 1,145,711 | +10.7% | 付费，定价请咨询 | 提供高性能的企业级 NVIDIA GPU 集群，但比起灵活的抢占式容器，更专注于专用裸机基础设施。},{de: | [.md](https://openaitools.app/zh/tool/nebius.md) |
| 7 | [Replicate AI](https://openaitools.app/zh/tool/replicate-ai) | 904,038 | -8.4% | 付费 | Offers a polished API and cloud platform for running open models, but provides less direct infrastructure-level control than RunPod. | [.md](https://openaitools.app/zh/tool/replicate-ai.md) |
| 8 | [Salad](https://openaitools.app/zh/tool/salad-gpu-cloud) | 713,519 | +10.4% | 免费试用，付费，定价请咨询 | Utilizes distributed consumer hardware to drastically lower inference costs, but cannot match enterprise GPU availability or SLA guarantees. | [.md](https://openaitools.app/zh/tool/salad-gpu-cloud.md) |
| 9 | [Cerebras](https://openaitools.app/zh/tool/cerebras) | 701,013 | -12.6% | 定价请咨询 | 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。 | [.md](https://openaitools.app/zh/tool/cerebras.md) |
| 10 | [DeepInfra](https://openaitools.app/zh/tool/deep-infra) | 457,810 | -8.9% | 付费 | 提供极其平价的无服务器模型推理，但不提供用于自定义训练工作负载的原始 GPU 容器租用。},{de: | [.md](https://openaitools.app/zh/tool/deep-infra.md) |

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来源：https://openaitools.app/zh/compare/fluidstack-vs-runpod

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