# FluidStack vs Openfabric AI

FluidStack records 154,620 monthly visits with +7.3% month-over-month growth and Paid, Contact for Pricing pricing. Openfabric AI records 3,737 monthly visits with +83.4% growth and Contact for Pricing, Free pricing.

| Metric | [FluidStack](https://openaitools.app/tool/fluidstack) | [Openfabric AI](https://openaitools.app/tool/openfabric-ai) |
| :--- | :--- | :--- |
| Website URL | https://fluidstack.io | https://www.openfabric.ai |
| Categories | AI Developer Tools, AI Models, Large Language Models (LLMs) | AI Developer Tools, AI Tools Directory, Web3, Blockchain |
| Pricing Model | Paid, Contact for Pricing | Contact for Pricing, Free |
| Monthly Visits | 154,620 | 3,737 |
| MoM Growth Rate | +7.3% | +83.4% |
| Primary Market | United States | United States |
| Direct Visits | 47,869 | 1,463 |
| Review Score | 4.4 | 3.8 |
| Saved Count | 1 | 1 |
| 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)

## Openfabric AI

Openfabric AI positions itself as a foundation layer for artificial intelligence rather than a single product. The network is built on its own Layer-1 blockchain, and its stated purpose is to lower the infrastructure cost and technical know-how needed to build and run AI applications, while keeping execution scalable and preventing vendor lock-in. Participants join in different capacities: innovators publish and monetize algorithms, infrastructure providers rent out compute, data providers supply datasets, and end users consume the resulting intelligent services inside their own products.

The project ships practical substance beyond the protocol. A mainnet explorer and testnet explorer are live, the Openfabric token is tradable on KuCoin, MEXC and PancakeSwap, and the codebase has been assessed by Certik (91.24%) and Hacken (100%). A public application marketplace — the AI Xplorer — lists apps such as Artfabric, an Arbitrage Screener, an Image to 3D converter, an Avatar Creator, ArbiPro and an Image Upscaler.

For developers the entry points are documentation, grants and a builder track, while validators and ambassadors are being onboarded through the community channels. Because the platform sits at the intersection of blockchain and AI, it suits teams that want decentralized compute and data provenance rather than a conventional cloud AI API.

**Pricing & Plans:** Openfabric AI uses a mixed model: browsing and using apps in the marketplace is free, the protocol and documentation are openly available, and staking, grants and infrastructure participation have their own economics. Enterprise and partner engagements are handled through direct contact, so no public per-seat price list is published.

- **Layer-1 blockchain for AI:** A dedicated Layer-1 network designed around AI workloads, providing the settlement and coordination layer for decentralized AI applications.
- **Multi-party marketplace:** A single network where AI innovators, data providers, compute providers and end users can find each other and transact.
- **Live AI application suite:** AI Xplorer hosts working apps including Artfabric, Arbitrage Screener, Image to 3D, Avatar Creator, ArbiPro and Image Upscaler.
- **Staking, grants and node roles:** Token holders can stake, apply for grants, or take on validator and promoter roles to secure and grow the network.
- **Independently audited:** Security reviews by Certik (91.24%) and Hacken (100%) and a published whitepaper and research section.

**Strengths:** Backed by real on-chain infrastructure: mainnet, explorer, audited contracts and listed token; Four clearly defined participant roles make the ecosystem easy to understand; Working marketplace applications already ship, not just a whitepaper; Open documentation and a grants program lower the barrier for new developers

**Limitations:** Value depends heavily on ecosystem adoption and token liquidity; Several community sections are still marked as coming soon; Pricing for commercial or enterprise use is only available on request

[Profile (Markdown)](https://openaitools.app/tool/openfabric-ai.md)

## Frequently Asked Questions

### How do FluidStack and Openfabric AI compare in monthly traffic?

FluidStack has 154,620 monthly visits, while Openfabric AI has 3,737. 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%; Openfabric AI has +83.4%.

### 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. Openfabric AI: Contact for Pricing, Free. Openfabric AI uses a mixed model: browsing and using apps in the marketplace is free, the protocol and documentation are openly available, and staking, grants and infrastructure participation have their own economics. Enterprise and partner engagements are handled through direct contact, so no public per-seat price list is published.

## 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 | Delivers peer-to-peer GPU rental marketplaces for lower-cost experimentation, though with less enterprise infrastructure predictability. | [.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-openfabric-ai

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