# Cerebrium vs FluidStack

Cerebrium records 26,974 monthly visits with -50.8% month-over-month growth and Free Trial, Paid pricing. FluidStack records 154,620 monthly visits with +7.3% growth and Paid, Contact for Pricing pricing.

| Metric | [Cerebrium](https://openaitools.app/tool/cerebrium) | [FluidStack](https://openaitools.app/tool/fluidstack) |
| :--- | :--- | :--- |
| Website URL | https://www.cerebrium.ai | https://fluidstack.io |
| Categories | AI Developer Tools, AI API, AI Models, Large Language Models (LLMs) | AI Developer Tools, AI Models, Large Language Models (LLMs) |
| Pricing Model | Free Trial, Paid | Paid, Contact for Pricing |
| Monthly Visits | 26,974 | 154,620 |
| MoM Growth Rate | -50.8% | +7.3% |
| Primary Market | United States | United States |
| Direct Visits | 15,045 | 47,869 |
| Review Score | 4.3 | 4.4 |
| Saved Count | 0 | 1 |
| Supported Platforms | Website | Website |

## Cerebrium

Cerebrium is serverless GPU infrastructure built specifically for real-time AI. Rather than asking teams to manage Kubernetes clusters or reserve GPU capacity in advance, it runs whatever container you point it at — LLM servers, voice pipelines, diffusion models, video generators — and takes care of provisioning, autoscaling, and routing. The platform emphasises low-latency startup: memory and GPU snapshots restore containers in roughly 2–4 seconds, compared with tens of seconds on conventional providers.

The company targets workloads where the first response is as important as the thousandth. Its own benchmarks and case studies cover voice agents responding in around 500 ms, deepfake detection at Resemble AI, AI-native web search at Linkup, digital humans at bitHuman and Tavus, and 50% lower inference costs at DistilLabs. Because cold starts dominate perceived quality in these products, Cerebrium treats startup time as a headline metric.

Under the hood it offers GPU types spanning Hopper and other generations across multiple clouds and regions, with pay-per-second billing so idle time is not charged. Bring-your-own-code deployment supports plain scripts, Dockerfiles, vLLM, SGLang, and Triton with TensorRT-LLM. Security controls include SOC 2 Type II, HIPAA, GDPR alignment, gVisor isolation, and regional data residency, and there is a free tier for trials in addition to paid usage.

**Pricing & Plans:** Free tier plus usage-based, pay-per-second billing on your chosen GPU hardware, with no reservations or long-term commitments. Exact rates are listed on the official pricing page.

- **Sub-second to low-second cold starts:** Memory and GPU snapshotting restores containers in roughly 2–4 seconds, which is essential for voice agents and other interactive workloads.
- **Elastic GPU capacity:** Access thousands of GPUs across 12+ GPU types and multiple clouds with no reservations or lock-in, scaling automatically on traffic bursts.
- **Bring your own code:** Deploy LLMs, voice pipelines, and image or video models from your own code or Dockerfile, with REST, WebSocket, and streaming endpoints.
- **End-to-end observability:** Real-time logs, metrics, and scaling events with native OpenTelemetry support for plugging into existing monitoring tools.
- **Compliance and isolation:** SOC 2 Type II, HIPAA, GDхPR, and ISO-aligned controls, gVisor isolation, multi-region failover, and regional data residency options.

**Strengths:** Snapshot-based restores cut cold starts to a few seconds, a real advantage for voice and interactive video; No Kubernetes to manage and no GPU reservations or capacity planning; Bring your own code or Dockerfile with no rewrites or proprietary SDK; Pay-per-second billing means idle resources are not billed

[Profile (Markdown)](https://openaitools.app/tool/cerebrium.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 Cerebrium and FluidStack compare in monthly traffic?

Cerebrium has 26,974 monthly visits, while FluidStack has 154,620. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Cerebrium has a month-over-month growth rate of -50.8%; FluidStack has +7.3%.

### How does pricing compare?

Cerebrium: Free Trial, Paid. Free tier plus usage-based, pay-per-second billing on your chosen GPU hardware, with no reservations or long-term commitments. Exact rates are listed on the official pricing page. 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/cerebrium-vs-fluidstack

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