# Cerebrium vs Nebius

Cerebrium records 26,974 monthly visits with -50.8% month-over-month growth and Free Trial, Paid pricing. Nebius records 1,145,711 monthly visits with +10.7% growth and Paid, Contact for Pricing pricing.

| Metric | [Cerebrium](https://openaitools.app/tool/cerebrium) | [Nebius](https://openaitools.app/tool/nebius) |
| :--- | :--- | :--- |
| Website URL | https://www.cerebrium.ai | https://nebius.com |
| Categories | AI Developer Tools, AI API, AI Models, Large Language Models (LLMs) | AI Developer Tools, AI Models, AI API, Open Source AI Models |
| Pricing Model | Free Trial, Paid | Paid, Contact for Pricing |
| Monthly Visits | 26,974 | 1,145,711 |
| MoM Growth Rate | -50.8% | +10.7% |
| Primary Market | United States | United States |
| Direct Visits | 15,045 | 447,886 |
| Review Score | 4.3 | 4.5 |
| Saved Count | 0 | 0 |
| 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)

## Nebius

Nebius positions itself as "the ultimate AI cloud," an infrastructure stack engineered specifically for AI workloads rather than adapted from general-purpose cloud. The core of the platform is raw GPU capacity from NVIDIA — including GB300 NVL72, HGX B300, GB200 NVL72, HGX B200, H200, H100, RTX PRO 6000, and L40S — delivered as non-virtualized instances attached to InfiniBand networking. Above that sit managed services such as Kubernetes, Soperator (Slurm on Kubernetes), PostgreSQL, and MLflow, so teams get the orchestration and experimentation tooling that large training runs need without assembling it themselves.

On the inference side, Nebius offers Token Factory, a managed endpoint for running open and frontier models, alongside serverless compute options. Storage is available as shared filesystems, WEKA, block volumes, and tiered object storage, all priced transparently by the gigabyte. Consumption is flexible: customers can pay on-demand, use preemptible and spot capacity at market-driven prices, or reserve capacity through commitments for lower rates. Networking egress, public IPs, and Kubernetes management are listed as free, which simplifies capacity planning.

Nebius supports the full lifecycle — pre-training, post-training, and inference — and publishes customer stories spanning financial services, healthcare, robotics, media, and e-commerce. Because the company also operates its own data centers and hardware supply chain, it can offer capacity guarantees and support arrangements that are hard to match on repurposed public cloud. Pricing is usage-based and published on the site, with contact needed for large clusters and enterprise agreements.

**Pricing & Plans:** Usage-based pricing published on the site. On-demand GPU instances range from roughly $1.55/GPU-hour for L40S up to about $9.50/GPU-hour for HGX B300, with preemptible and live spot options starting around $0.79/GPU-hour. CPU-only instances start near $0.06/cpu-hour, managed Kubernetes and networking are free, and large-scale clusters or commitments are quoted through sales.

- **Non-virtualized GPU clusters:** Bare-metal access to NVIDIA GB300, B300, GB200, B200, H200, H100, RTX PRO 6000, and L40S GPUs connected over InfiniBand for training and inference at scale.
- **Nebius Token Factory:** A managed inference service for running popular open and frontier models through an API, so applications can add LLM capabilities without owning GPUs.
- **Managed MLOps services:** Built-in Kubernetes, Soperator for Slurm jobs, PostgreSQL, and MLflow give teams a ready-made environment for orchestration, tracking, and experiments.
- **Flexible consumption and spot pricing:** Choose on-demand, preemptible, or live market-driven spot capacity, or commit to reserved clusters for discounts on long-running workloads.
- **Scalable AI storage:** Shared filesystems, WEKA, block volumes, and tiered object storage sized for multi-hundred-terabyte training datasets.

**Strengths:** Transparent, publicly listed per-hour GPU and storage pricing; Broad NVIDIA lineup from L40S up to GB300 NVL72; Free Kubernetes management, networking egress, and public IPs; Integrated MLOps tooling including MLflow, Slurm via Soperator, and PostgreSQL

**Limitations:** Top-end clusters like GB300 and GB200 are quote-only, not self-serve; Spot and preemptible capacity is interruptible and prices move with demand; Requires meaningful ML infrastructure knowledge to get the most out of it

[Profile (Markdown)](https://openaitools.app/tool/nebius.md)

## Frequently Asked Questions

### How do Cerebrium and Nebius compare in monthly traffic?

Cerebrium has 26,974 monthly visits, while Nebius has 1,145,711. 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%; Nebius has +10.7%.

### 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. Nebius: Paid, Contact for Pricing. Usage-based pricing published on the site. On-demand GPU instances range from roughly $1.55/GPU-hour for L40S up to about $9.50/GPU-hour for HGX B300, with preemptible and live spot options starting around $0.79/GPU-hour. CPU-only instances start near $0.06/cpu-hour, managed Kubernetes and networking are free, and large-scale clusters or commitments are quoted through sales.

## 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 | Tailored specifically for high-throughput generative media inference pipelines, whereas Nebius targets general GPU compute 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 pods, but focuses more on flexible developer instances than turnkey enterprise clusters. | [.md](https://openaitools.app/tool/runpod.md) |
| 3 | [Together AI](https://openaitools.app/tool/together-ai) | 1,645,930 | +64.9% | Freemium, Paid | Provides robust serverless inference APIs and GPU clusters for open-source models, but with less emphasis on raw bare-metal control. | [.md](https://openaitools.app/tool/together-ai.md) |
| 4 | [Vast.ai](https://openaitools.app/tool/vast-ai) | 1,257,554 | +2.8% | Paid | Features a peer-to-peer GPU rental marketplace with lower pricing, though with variable hardware reliability compared to managed infrastructure. | [.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 | Executes Python workloads and model inference in serverless containers effortlessly, but lacks bare-metal cluster management. | [.md](https://openaitools.app/tool/modal-com.md) |
| 6 | [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) |
| 7 | [DeepInfra](https://openaitools.app/tool/deep-infra) | 457,810 | -8.9% | Paid | Provides exceptionally cheap serverless inference for open models via API, but does not offer raw bare-metal GPU rentals. | [.md](https://openaitools.app/tool/deep-infra.md) |
| 8 | [Lightning AI](https://openaitools.app/tool/lightning-ai) | 272,562 | -12.3% | Freemium, Paid, Contact for Pricing | Provides interactive development studios and on-demand GPUs for training, but focuses more on PyTorch workflows than raw infrastructure. | [.md](https://openaitools.app/tool/lightning-ai.md) |
| 9 | [Novita AI](https://openaitools.app/tool/novita-ai) | 271,371 | -10.4% | Paid | Combines model APIs and serverless GPU jobs, but lacks the enterprise-grade bare-metal infrastructure orchestration of Nebius. | [.md](https://openaitools.app/tool/novita-ai.md) |
| 10 | [FluidStack](https://openaitools.app/tool/fluidstack) | 154,620 | +7.3% | Paid, Contact for Pricing | Specializes in large-scale enterprise data center GPU clusters for heavy training, though less tailored for instant serverless functions. | [.md](https://openaitools.app/tool/fluidstack.md) |

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Source: https://openaitools.app/compare/cerebrium-vs-nebius

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