# FluidStack vs Lium

FluidStack records 154,620 monthly visits with +7.3% month-over-month growth and Paid, Contact for Pricing pricing. Lium records 58,483 monthly visits with -13.9% growth and Paid pricing.

| Metric | [FluidStack](https://openaitools.app/tool/fluidstack) | [Lium](https://openaitools.app/tool/lium-io) |
| :--- | :--- | :--- |
| Website URL | https://fluidstack.io | https://lium.io |
| Categories | AI Developer Tools, AI Models, Large Language Models (LLMs) | AI Developer Tools |
| Pricing Model | Paid, Contact for Pricing | Paid |
| Monthly Visits | 154,620 | 58,483 |
| MoM Growth Rate | +7.3% | -13.9% |
| Primary Market | United States | United States |
| Direct Visits | 47,869 | 40,955 |
| Review Score | 4.4 | 4.3 |
| 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)

## Lium

Lium is a marketplace for renting GPU compute, positioned explicitly for autonomous agents as much as for humans. The catalogue spans small cards like the T4 through RTX 5090 and RTX 6000 Ada up to datacentre parts including H100, H200, B200 and B300, with listings showing live pricing, VRAM, CPU, RAM, disk, network speeds, uptime and a reliability score so you can judge a host before committing.

The distinctive idea is per-second billing plus an "agent quickstart": your agent can install the CLI, start a pod with a GPU type and a time-to-live, run the job, and have the pod remove itself automatically at the end. Budgets and TTLs give you a hard stop on spend. Because providers are independent, prices vary widely across the fleet, and the interface surfaces spot versus on-demand rates alongside past renter ratings.

On the supply side, Lium lets hardware owners list their GPUs and pays them the bulk of rental revenue plus bonuses, which is how the marketplace keeps capacity in many regions. For buyers this means a practical way to get short-lived GPU access for training, fine-tuning, inference or batch rendering without a long cloud commitment, all driven from the same interface your AI agent already uses.

**Pricing & Plans:** Lium is a paid marketplace with per-second billing; you pay the hourly rate shown on each pod listing. Prices vary by GPU model, availability and whether a listing is spot or on-demand — for example H100 listings appear around a few dollars per GPU-hour while T4 cards are a fraction of that. GPU owners set their own rates and earn most of the rental revenue.

- **Per-second GPU billing:** Start and stop pods on demand and pay for the seconds you actually use, rather than reserving capacity by the month.
- **Diverse catalogue from T4 to B300:** Pick from consumer cards, workstation GPUs and datacentre accelerators including H100, H200 and B200, with VRAM, CPU, RAM and disk shown per listing.
- **CLI, SDK and API access:** A single install command gives your agent or script the ability to launch pods with a GPU type, budget and auto-expiry.
- **Transparent host metrics:** Each listing reports reliability, uptime, network throughput and renter ratings so you can choose a dependable provider.
- **Supply-side earnings:** Hardware owners can register GPUs and earn the large majority of rental revenue plus bonuses for listing.

**Strengths:** Very broad hardware selection in one catalogue, including current-generation datacentre GPUs; Per-second billing with TTL and budget limits keeps costs predictable; Everything is scriptable via CLI, Python SDK and REST API; Listings expose reliability, uptime and renter ratings instead of hiding host quality

**Limitations:** Prices and availability change constantly because hosts are independent providers; Reliability scores vary noticeably between listings, so some hosts are better than others; You have to manage your own environment and images; it is raw infrastructure rather than a managed platform

[Profile (Markdown)](https://openaitools.app/tool/lium-io.md)

## Frequently Asked Questions

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

FluidStack has 154,620 monthly visits, while Lium has 58,483. 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%; Lium has -13.9%.

### 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. Lium: Paid. Lium is a paid marketplace with per-second billing; you pay the hourly rate shown on each pod listing. Prices vary by GPU model, availability and whether a listing is spot or on-demand — for example H100 listings appear around a few dollars per GPU-hour while T4 cards are a fraction of that. GPU owners set their own rates and earn most of the rental revenue.

## 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-lium-io

More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)