# FluidStack vs Salad

FluidStack records 154,620 monthly visits with +7.3% month-over-month growth and Paid, Contact for Pricing pricing. Salad records 713,519 monthly visits with +10.4% growth and Free Trial, Paid, Contact for Pricing pricing.

| Metric | [FluidStack](https://openaitools.app/tool/fluidstack) | [Salad](https://openaitools.app/tool/salad-gpu-cloud) |
| :--- | :--- | :--- |
| Website URL | https://fluidstack.io | https://salad.com |
| Categories | AI Developer Tools, AI Models, Large Language Models (LLMs) | AI Developer Tools, AI API, AI Transcription, Large Language Models (LLMs) |
| Pricing Model | Paid, Contact for Pricing | Free Trial, Paid, Contact for Pricing |
| Monthly Visits | 154,620 | 713,519 |
| MoM Growth Rate | +7.3% | +10.4% |
| Primary Market | United States | United States |
| Direct Visits | 47,869 | 440,915 |
| Review Score | 4.4 | 4.3 |
| 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)

## Salad

Salad attacks the cost of GPU compute from a different direction. Rather than financing new data-centre capacity, it coordinates supply that already exists: consumer GPUs sitting idle in PCs and workstations worldwide. Since 2018, more than 450,000 GPUs have contributed to the network, and it currently reports 16,000+ daily active GPUs spread across 190+ countries. Pricing starts around $0.015 per GPU-hour, which the company positions as up to 90% below hyperscaler rates.

The orchestration layer is the core product. Salad Container Engine schedules OCI/Docker containers onto nodes it has never seen before, verifies and isolates them behind a hypervisor, streams logs, and reallocates work the moment a node drops off the network — critical because interruptible consumer hardware leaves unpredictably. A proprietary trust rating scores node performance and availability, and priority pricing matches cheap interruptible supply to workloads designed to tolerate it. The same engine runs on Community Cloud, reserved Salad Dedicated hardware (RTX PRO 6000 Blackwell among the first), and on a Private Cloud inside your own organisation, where idle workstation GPUs are pooled into an OpenAI-compatible token service without company data leaving company devices.

Several managed products sit on top: an OpenAI-compatible AI Gateway for open-weight models, a Transcription API, and Gateway Service for residential IP tunnels. Published per-workload benchmarks back the marketing — for example 5,243 Flux.1-Schnell images per dollar, 95.1% CommonVoice accuracy from $0.16 per audio hour, and flat hourly GPU rates or per-token pricing from $0.06 per million input tokens for LLM inference. Production users include Civitai, Blend, Klyne.ai, Undetectable AI, Pareto and Datasembly.

**Pricing & Plans:** Pay-as-you-go with no contracts or minimums on SaladCloud: per-second billing on containers and per-token pricing on the AI Gateway, with GPU rates starting around $0.015 per GPU-hour and tiered volume discounts. Reserved capacity and enterprise contracts are available through Salad Dedicated and Private Cloud, quoted on request.

- **Salad Container Engine:** Schedules, monitors and reallocates Docker/OCI workloads across thousands of distributed GPU nodes, with failover when nodes disappear.
- **Community Cloud GPU pool:** On-demand access to a distributed pool of idle consumer GPUs across 190+ countries with priority pricing from about $0.015 per GPU-hour.
- **OpenAI-compatible AI Gateway:** A managed token endpoint for open-weight models priced per token, with your data never used for training.
- **Private Cloud on your hardware:** Run the same orchestration stack inside your organisation, pooling idle workstation GPUs into an internal OpenAI-compatible service.
- **Workload-tuned APIs:** Managed services including a Transcription API and virtual kubelet support for scheduling Kubernetes pods onto the network.

**Strengths:** Structurally low pricing — GPU rates start near $0.015 per GPU-hour; One orchestration layer spans on-demand, reserved and private infrastructure; Handles interruption natively, which matters on consumer hardware; OpenAI-compatible endpoints simplify migration from other providers

**Limitations:** Interruptible consumer nodes are not suited to latency-critical or always-on workloads as-is; Distributed hardware varies more in performance than uniform data-centre racks; No conventional free tier — costs scale directly with usage

[Profile (Markdown)](https://openaitools.app/tool/salad-gpu-cloud.md)

## Frequently Asked Questions

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

FluidStack has 154,620 monthly visits, while Salad has 713,519. 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%; Salad has +10.4%.

### 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. Salad: Free Trial, Paid, Contact for Pricing. Pay-as-you-go with no contracts or minimums on SaladCloud: per-second billing on containers and per-token pricing on the AI Gateway, with GPU rates starting around $0.015 per GPU-hour and tiered volume discounts. Reserved capacity and enterprise contracts are available through Salad Dedicated and Private Cloud, quoted 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 | Offers reliable dedicated and serverless GPU rentals, but lacks Salad's ultra-low-cost peer-to-peer consumer hardware pool. | [.md](https://openaitools.app/tool/runpod.md) |
| 3 | [Together AI](https://openaitools.app/tool/together-ai) | 1,645,930 | +64.9% | Freemium, Paid | Specializes in managed serverless inference APIs for open-source models, rather than raw GPU infrastructure rentals. | [.md](https://openaitools.app/tool/together-ai.md) |
| 4 | [Vast.ai](https://openaitools.app/tool/vast-ai) | 1,257,554 | +2.8% | Paid | Provides an extensive peer-to-peer GPU marketplace with lower prices, but requires more manual configuration. | [.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 | Delivers effortless Python serverless execution for AI pipelines, but focuses on function-as-a-service rather than raw VMs. | [.md](https://openaitools.app/tool/modal-com.md) |
| 6 | [Nebius](https://openaitools.app/tool/nebius) | 1,145,711 | +10.7% | Paid, Contact for Pricing | Supplies enterprise-grade NVIDIA GPU clusters and managed ML tooling, but at a higher professional price point. | [.md](https://openaitools.app/tool/nebius.md) |
| 7 | [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) |
| 8 | [DeepInfra](https://openaitools.app/tool/deep-infra) | 457,810 | -8.9% | Paid | Offers extremely cheap serverless model hosting via API, but does not provide raw containerized GPU instances. | [.md](https://openaitools.app/tool/deep-infra.md) |
| 9 | [Thunder Compute](https://openaitools.app/tool/thunder-compute) | 102,147 | -29% | Paid (per-minute instances, per-second sandboxes) | Features second-billed persistent SSH GPU instances, but lacks a distributed consumer hardware backbone. | [.md](https://openaitools.app/tool/thunder-compute.md) |
| 10 | [Anyscale](https://openaitools.app/tool/anyscale-com) | 49,779 | -23.2% | Paid, Contact for Pricing | Scales distributed Ray workloads effortlessly across clusters, but is tailored for heavy data engineering and training. | [.md](https://openaitools.app/tool/anyscale-com.md) |

---
Source: https://openaitools.app/compare/fluidstack-vs-salad-gpu-cloud

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