# Anyscale vs FluidStack

Anyscale records 49,779 monthly visits with -23.2% month-over-month growth and Paid, Contact for Pricing pricing. FluidStack records 154,620 monthly visits with +7.3% growth and Paid, Contact for Pricing pricing.

| Metric | [Anyscale](https://openaitools.app/tool/anyscale-com) | [FluidStack](https://openaitools.app/tool/fluidstack) |
| :--- | :--- | :--- |
| Website URL | https://anyscale.com | https://fluidstack.io |
| Categories | AI Developer Tools, Large Language Models (LLMs), AI App Builder, AI Workflow | AI Developer Tools, AI Models, Large Language Models (LLMs) |
| Pricing Model | Paid, Contact for Pricing | Paid, Contact for Pricing |
| Monthly Visits | 49,779 | 154,620 |
| MoM Growth Rate | -23.2% | +7.3% |
| Primary Market | United States | United States |
| Direct Visits | 28,629 | 47,869 |
| Review Score | 4.6 | 4.4 |
| Saved Count | 3 | 1 |
| Supported Platforms | Website | Website |

## Anyscale

Anyscale packages the open-source Ray project into a managed service so that engineering teams do not have to operate the cluster layer themselves. The pitch is production-scale AI on your own cloud GPUs: you bring the compute, Anyscale handles orchestration, scaling, and the operational work of running Ray workloads reliably. The company was founded by the creators of Ray and remains the primary commercial steward of the project.

The platform is organized around the workloads that dominate modern AI development rather than around a single product. Multimodal data curation runs large pipelines over video, images, text, and audio; distributed model training orchestrates GPU clusters with elastic scaling and last-mile preprocessing; batch embedding generation parallelizes embedding jobs for retrieval and search; and post-training supports RL-style frameworks such as SkyRL and veRL natively built on Ray. Existing libraries like PyTorch, vLLM, SGLang, and XGBoost can be scaled with Python APIs instead of rewrites.

Operationally, Anyscale emphasizes fine-grained hardware allocation, so different parts of a workload can claim CPUs, GPUs, TPUs, or accelerator racks independently, plus efficient distributed communication through Ray's in-memory object store or RDMA. Multi-cloud orchestration, compute governance, and workload observability round out the management story, and a free tier with starter credits lowers the cost of evaluating the platform.

**Pricing & Plans:** Anyscale is a paid, usage-based platform with no permanent free plan; it typically provides starter credits and a free trial for evaluation, and enterprise agreements are quoted through sales based on compute usage and support needs. Because pricing depends on cloud and GPU consumption, exact figures should be confirmed with the vendor.

- **Managed Ray clusters:** Run Ray jobs, services, and libraries on managed infrastructure across thousands of nodes without operating the cluster yourself.
- **Distributed training at scale:** Orchestrate multi-GPU and multi-node training with elastic scaling, last-mile data preprocessing, and GPU observability built in.
- **Multimodal data curation:** Build pipelines that download, filter, and transform video, image, text, and audio data ahead of training.
- **Batch inference and embeddings:** Parallelize embedding generation and batch inference for search, retrieval, and downstream training workloads.
- **Open-source library compatibility:** Scale PyTorch, vLLM, SGLang, and XGBoost workloads with Python APIs rather than rewriting code for the platform.

**Strengths:** Built by the creators of Ray, so the managed layer closely tracks the open-source project; Handles data, training, and inference workloads on one distributed runtime; Works with existing PyTorch, vLLM, SGLang, and XGBoost code; Runs in your own cloud account with multi-cloud orchestration and governance

**Limitations:** No permanent free tier, so evaluation is time-limited before real spend; Pricing is consumption-based and hard to forecast without modeling GPU usage; Most valuable to teams already committed to Ray and scale, less so for small projects

[Profile (Markdown)](https://openaitools.app/tool/anyscale-com.md)

## 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)

## Frequently Asked Questions

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

Anyscale has 49,779 monthly visits, while FluidStack has 154,620. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Anyscale has a month-over-month growth rate of -23.2%; FluidStack has +7.3%.

### How does pricing compare?

Anyscale: Paid, Contact for Pricing. Anyscale is a paid, usage-based platform with no permanent free plan; it typically provides starter credits and a free trial for evaluation, and enterprise agreements are quoted through sales based on compute usage and support needs. Because pricing depends on cloud and GPU consumption, exact figures should be confirmed with the vendor. 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.

## 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 flexible on-demand GPU rentals and serverless endpoints, but lacks FluidStack's ultra-scale enterprise GPU clusters. | [.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 | Provides serverless Python container execution for cloud jobs, but lacks raw multi-node cluster provisioning controls. | [.md](https://openaitools.app/tool/modal-com.md) |
| 6 | [Nebius](https://openaitools.app/tool/nebius) | 1,145,711 | +10.7% | Paid, Contact for Pricing | Provides dedicated NVIDIA GPU clusters and managed cloud infrastructure, competing directly on enterprise-grade training workloads. | [.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/anyscale-com-vs-fluidstack

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