# FluidStack vs General Compute

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。General Compute 记录了 13,828 月访问量，增长率为 +7.2%，定价为免费试用，定价请咨询。

| 指标 | [FluidStack](https://openaitools.app/zh/tool/fluidstack) | [General Compute](https://openaitools.app/zh/tool/general-compute) |
| :--- | :--- | :--- |
| 官网地址 | https://fluidstack.io | https://www.generalcompute.com |
| 分类 | AI Developer Tools, AI Models, Large Language Models (LLMs) | AI API, AI Models, Large Language Models (LLMs), AI Developer Tools |
| 定价模式 | 付费，定价请咨询 | 免费试用，定价请咨询 |
| 月访问量 | 154,620 | 13,828 |
| 月度环比增长率 | +7.3% | +7.2% |
| 主要市场 | 美国 | 美国 |
| 直接访问量 | 47,869 | 6,732 |
| 评价得分 | 4.4 | 4.2 |
| 收藏数 | 1 | 0 |
| 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)

## General Compute

General Compute is an inference cloud built around alternative AI silicon rather than standard GPU fleets. The company buys racks of accelerators, racks them in colocation data centres, and handles the cooling, power, and networking itself. On top of that hardware it runs a disaggregated inference stack that splits each request: prompt processing runs on NVIDIA GPUs for prefill, while token generation runs on faster decode chips such as Cerebras, SambaNova, Positron AI, and d-Matrix. Customers get bare metal access or a tokens-per-second SLA.

**定价与套餐:** Pricing is contract-based and requires a call; it is quoted per engagement according to term, volume, and site rather than published as self-serve rates. Indications of free trial availability exist, but no public price list is shown.

- **Disaggregated prefill and decode:** Prompt processing runs on NVIDIA GPUs while token generation runs on faster ASICs from Cerebras, SambaNova, Positron AI, and d-Matrix.
- **Bare metal or SLA endpoint:** Buy racks of GPUs for prefill and ASICs for decode, with General Compute handling cooling, power, networking, and the orchestration software.
- **Per-chip specifications:** Inspect each accelerator in an interactive 3D view with its SRAM, power draw, cooling type, and the workloads it suits best.
- **Software for non-standard silicon:** The platform abstracts bespoke hardware so models run without customers having to learn a new chip toolchain.
- **Throughput SLA and failover:** Multi-provider redundancy and automatic failover for production inference, backed by contracted throughput guarantees.

**优势:** Purpose-built for very low token latency rather than generic GPU rental; Handles cooling, power, networking, and orchestration so customers don't have to; Mix of GPU prefill and ASIC decode can improve throughput per rack; Well funded ($100M Series A) with named chip and partner ecosystem

**局限:** Pricing is contract-based rather than published per-token rates, which slows down self-serve evaluation; No instant public signup for compute; access begins with a sales conversation; Newest deployments are scheduled from Q1 2027, so capacity is in ramp-up; Workloads need to fit the disaggregated prefill/decode model to get the full speed benefit

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

## 常见问题

### FluidStack 和 General Compute 在月流量上如何比较？

FluidStack 有 154,620 月访问量，而 General Compute 有 13,828。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%；General Compute 为 +7.2%。

### 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. General Compute: 免费试用，定价请咨询。Pricing is contract-based and requires a call; it is quoted per engagement according to term, volume, and site rather than published as self-serve rates. Indications of free trial availability exist, but no public price list is shown.

## 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 | [Groq](https://openaitools.app/zh/tool/groq) | 3,987,936 | +10% | 付费 | 基于 LPU 与 LPX 平台的超高速 AI 推理 | [.md](https://openaitools.app/zh/tool/groq.md) |
| 2 | [fal.ai](https://openaitools.app/zh/tool/fal-ai) | 3,093,684 | -3.7% | Paid, pay-as-you-go usage-based pricing | Delivers developer-first serverless endpoints optimized for generative media models rather than general-purpose infrastructure. | [.md](https://openaitools.app/zh/tool/fal-ai.md) |
| 3 | [RunPod](https://openaitools.app/zh/tool/runpod) | 2,881,043 | +5.9% | 付费 | 面向 AI 开发者的 GPU Pod、Serverless 推理与集群云平台 | [.md](https://openaitools.app/zh/tool/runpod.md) |
| 4 | [Together AI](https://openaitools.app/zh/tool/together-ai) | 1,645,930 | +64.9% | 免费增值，付费 | Together AI is the AI Native Cloud — a full-stack platform for running, fine-tuning and training open-source models. It spans serverless inference APIs, dedicated endpoints, GPU clusters, post-training and storage, all b | [.md](https://openaitools.app/zh/tool/together-ai.md) |
| 5 | [Vast.ai](https://openaitools.app/zh/tool/vast-ai) | 1,257,554 | +2.8% | 付费 | 从全球市场按秒租用云 GPU | [.md](https://openaitools.app/zh/tool/vast-ai.md) |
| 6 | [Modal](https://openaitools.app/zh/tool/modal-com) | 1,196,021 | +30.2% | 免费增值，定价请咨询 | 面向 AI 与数据负载的无服务器云基础设施 | [.md](https://openaitools.app/zh/tool/modal-com.md) |
| 7 | [Nebius](https://openaitools.app/zh/tool/nebius) | 1,145,711 | +10.7% | 付费，定价请咨询 | Nebius is an AI-focused cloud platform that gives teams non-virtualized NVIDIA GPU clusters, managed MLOps tooling, and a serverless inference layer so they can move from experiment to production on one stack. | [.md](https://openaitools.app/zh/tool/nebius.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 | 编排全球闲置消费级 GPU 的分布式 GPU 云 | [.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 | 全球最快的 AI 推理，由全球最大的芯片驱动 | [.md](https://openaitools.app/zh/tool/cerebras.md) |
| 10 | [DeepInfra](https://openaitools.app/zh/tool/deep-infra) | 457,810 | -8.9% | 付费 | Specializes in cost-effective serverless API inference for open-weights models instead of raw infrastructure rentals. | [.md](https://openaitools.app/zh/tool/deep-infra.md) |

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

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