# FluidStack vs TensorDock

FluidStack records 154,620 monthly visits with +7.3% month-over-month growth and Paid, Contact for Pricing pricing. TensorDock records 19,689 monthly visits with -23.3% growth and Paid pricing.

| Metric | [FluidStack](https://openaitools.app/tool/fluidstack) | [TensorDock](https://openaitools.app/tool/tensordock) |
| :--- | :--- | :--- |
| Website URL | https://fluidstack.io | https://tensordock.com |
| Categories | AI Developer Tools, AI Models, Large Language Models (LLMs) | AI Developer Tools, AI Research Tool |
| Pricing Model | Paid, Contact for Pricing | Paid |
| Monthly Visits | 154,620 | 19,689 |
| MoM Growth Rate | +7.3% | -23.3% |
| Primary Market | United States | United States |
| Direct Visits | 47,869 | 6,262 |
| Review Score | 4.4 | 4.2 |
| Saved Count | 1 | 0 |
| Supported Platforms | Website | Website |

## FluidStack

FluidStack is an AI compute provider that builds the physical layer underneath frontier model work: data centers, power, cooling and very large GPU clusters. Its public positioning is deliberately infrastructural rather than product-led — it describes building gigawatt-scale compute, bringing new sites online in months, and operating tens of millions of accelerators reliably. For teams training or serving large models, that translates into reserved, single-tenant-class capacity that is provisioned and operated for you rather than an instant, self-serve VM catalogue.

A distinguishing theme is the pairing of power and compute. FluidStack writes about redesigning compute and power infrastructure from first principles, developing off-grid solar to supply clusters, and using closed-loop cooling that fills tanks once and recirculates water indefinitely. It also emphasises building in the United States with onshored manufacturing, framing infrastructure ownership as a strategic question about which values shape the technology being built. For AI organisations, these are increasingly relevant because energy availability, not chip supply alone, is the binding constraint on scaling.

If you need a small number of GPUs for a day, FluidStack is probably not the fastest route; capacity is engaged at enterprise scale through a sales process. If you are planning a multi-month training run or a production inference fleet and want dedicated, purpose-built infrastructure with managed operations, it is a strong candidate to evaluate alongside the major hyperscalers and specialised GPU clouds.

**Pricing & Plans:** 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.

**Strengths:** 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

**Limitations:** 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

[Profile (Markdown)](https://openaitools.app/tool/fluidstack.md)

## TensorDock

TensorDock operates a marketplace model: independent hosts supply servers that TensorDock vets, standardizes and exposes through its own dashboard and API. That structure gives customers a wide spread of hardware options — from budget RTX 3090s and 4090s for inference, rendering or gaming up through A100 SXM4 and H100 SXM5 cards for serious training — with the pricing set by competing hosts rather than by a single cloud provider. Deployments are hourly and on demand, with no quotas or reserved commitments required.

Technically, the platform is built around KVM virtualization, so each instance is a fully isolated virtual machine with root access, dedicated GPUs, full OS control and the ability to manage your own drivers. Windows 10 is supported alongside Linux, and Docker ships on standard templates. The whole fleet is driven by a documented API for provisioning, metadata and availability management, making it practical to script large numbers of short-lived instances.

Reliability is handled through host vetting and policy rather than sheer scale: TensorDock says it holds hosts to a 99.99% uptime standard, requires maintenance to be scheduled weeks ahead, and revokes host SSH access so operators cannot reach customer data. Billing is pay-as-you-go from a deposited balance — machines stop and are removed when the balance hits zero, which suits bursty experimentation better than fixed monthly contracts.

**Pricing & Plans:** Pay-as-you-go from a deposited balance, with entry around $5. Published on-demand rates include H100 SXM5 from about $2.25/hr, A100 SXM4 from about $1.80/hr, RTX 4090 from about $0.35/hr and CPU-only instances from roughly $0.012/hr. Reserved and enterprise pricing are available on request.

- **On-demand GPU marketplace:** Dozens of GPU models from multiple independent hosts compete on price and location, so you can pick the best rate for a given workload instead of accepting a single vendor's list price.
- **Full KVM virtual machines:** Each deployment is a real VM with root access, a dedicated GPU, complete OS control, custom driver support, Docker on standard templates and Windows 10 compatibility.
- **Deploy via dashboard or API:** A documented REST API with machine metadata and live availability lets you automate provisioning, teardown and fleet management from your own tooling.
- **No quotas, hourly billing:** Fund a balance and pay continuously while instances run; servers are reclaimed automatically at zero balance, so short experiments never lock you into a term.
- **Vetted hosts and uptime standards:** Hosts are screened on hardware, expertise and responsiveness, held to a 99.99% uptime standard, and denied SSH access to customer machines for data protection.

**Strengths:** Aggressively low hourly rates driven by host competition rather than vendor list pricing; Unusually broad hardware range, from consumer cards up to H100 SXM5; True KVM isolation with root access and custom drivers, not just sandboxed containers; Simple pay-as-you-go model with no quotas or long-term commitments

**Limitations:** Host-contributed supply means availability for specific GPU models can fluctuate; Billing is prepaid — instances are deleted when your balance reaches zero; Windows and driver setups can require more hands-on administration than a managed notebook cloud

[Profile (Markdown)](https://openaitools.app/tool/tensordock.md)

## Frequently Asked Questions

### How do FluidStack and TensorDock compare in monthly traffic?

FluidStack has 154,620 monthly visits, while TensorDock has 19,689. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

FluidStack has a month-over-month growth rate of +7.3%; TensorDock has -23.3%.

### How does pricing compare?

FluidStack: Paid, Contact for Pricing. 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. TensorDock: Paid. Pay-as-you-go from a deposited balance, with entry around $5. Published on-demand rates include H100 SXM5 from about $2.25/hr, A100 SXM4 from about $1.80/hr, RTX 4090 from about $0.35/hr and CPU-only instances from roughly $0.012/hr. Reserved and enterprise pricing are available on request.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [fal.ai](https://openaitools.app/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/tool/fal-ai.md) |
| 2 | [RunPod](https://openaitools.app/tool/runpod) | 2,881,043 | +5.9% | Paid | The AI developer cloud for GPU pods, serverless inference and clustering | [.md](https://openaitools.app/tool/runpod.md) |
| 3 | [Together AI](https://openaitools.app/tool/together-ai) | 1,645,930 | +64.9% | Freemium, Paid | Focuses on optimized serverless inference APIs and managed model hosting rather than raw cluster rentals. | [.md](https://openaitools.app/tool/together-ai.md) |
| 4 | [Vast.ai](https://openaitools.app/tool/vast-ai) | 1,257,554 | +2.8% | Paid | Rent cloud GPUs by the second from a global marketplace | [.md](https://openaitools.app/tool/vast-ai.md) |
| 5 | [Modal](https://openaitools.app/tool/modal-com) | 1,196,021 | +30.2% | Freemium, Contact for Pricing | Serverless cloud infrastructure for AI and data workloads | [.md](https://openaitools.app/tool/modal-com.md) |
| 6 | [Nebius](https://openaitools.app/tool/nebius) | 1,145,711 | +10.7% | Paid, Contact for Pricing | An AI-native cloud built from silicon to API for training, tuning, and serving models | [.md](https://openaitools.app/tool/nebius.md) |
| 7 | [Salad](https://openaitools.app/tool/salad-gpu-cloud) | 713,519 | +10.4% | Free Trial, Paid, Contact for Pricing | Distributed GPU cloud that orchestrates the world's idle consumer GPUs | [.md](https://openaitools.app/tool/salad-gpu-cloud.md) |
| 8 | [Cerebras](https://openaitools.app/tool/cerebras) | 701,013 | -12.6% | Contact for Pricing | Utilizes proprietary wafer-scale processors for extreme training speeds, differing from standard NVIDIA GPU cluster setups. | [.md](https://openaitools.app/tool/cerebras.md) |
| 9 | [DeepInfra](https://openaitools.app/tool/deep-infra) | 457,810 | -8.9% | Paid | Specializes in cost-effective serverless API inference for open-weights models instead of raw infrastructure rentals. | [.md](https://openaitools.app/tool/deep-infra.md) |
| 10 | [Lightning AI](https://openaitools.app/tool/lightning-ai) | 272,562 | -12.3% | Freemium, Paid, Contact for Pricing | The AI cloud built by the creators of PyTorch Lightning | [.md](https://openaitools.app/tool/lightning-ai.md) |

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Source: https://openaitools.app/compare/fluidstack-vs-tensordock

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