# Modal vs Nebius

Modal records 1,196,021 monthly visits with +30.2% month-over-month growth and Freemium, Contact for Pricing pricing. Nebius records 1,145,711 monthly visits with +10.7% growth and Paid, Contact for Pricing pricing.

| Metric | [Modal](https://openaitools.app/tool/modal-com) | [Nebius](https://openaitools.app/tool/nebius) |
| :--- | :--- | :--- |
| Website URL | https://modal.com | https://nebius.com |
| Categories | AI Developer Tools, AI API, Large Language Models (LLMs), AI Models | AI Developer Tools, AI Models, AI API, Open Source AI Models |
| Pricing Model | Freemium, Contact for Pricing | Paid, Contact for Pricing |
| Monthly Visits | 1,196,021 | 1,145,711 |
| MoM Growth Rate | +30.2% | +10.7% |
| Primary Market | United States | United States |
| Direct Visits | 845,636 | 447,886 |
| Review Score | 4.8 | 4.5 |
| Saved Count | 3 | 0 |
| Supported Platforms | Website | Website |

## Modal

Modal provides cloud infrastructure aimed squarely at AI and data teams. The core idea is that developers write ordinary Python, decorate functions or define container images, and Modal takes care of the underlying compute: scheduling containers, attaching GPUs, autoscaling from zero and back, and exposing endpoints over HTTPS. Infrastructure concerns like networking, storage, and capacity management are abstracted away, so a workload that runs on a laptop can run the same way across a fleet.

The platform is organised around a few primitives. Functions handle serverless compute and can fan out across thousands of GPUs for parallel or batch work. Sandboxes provide isolated environments for coding agents, reinforcement learning rollouts, and other untrusted code, with sub-second start times and support for very high concurrency. Training covers fine-tuning, reinforcement learning, and evaluation on single or multi-node GPU capacity. On the inference side, Modal offers OpenAI-compatible endpoints for open-weight models and lets teams deploy custom stacks and tune endpoints to match their traffic shape.

Underneath, Modal runs its own container runtime and image system designed for large images and fast GPU scheduling, along with a distributed filesystem for fast access to model weights and datasets. Capacity is drawn from a multi-cloud pool spanning many regions, with automatic routing to available hardware and continuous fleet health checks. Billing is usage-based, charged by the second while code runs, with granular cost controls. The company offers a free starting allowance and scalable paid usage, and markets to enterprises with SOC 2 Type II compliance and HIPAA readiness.

**Pricing & Plans:** Modal uses usage-based pricing billed by the second: a free monthly compute allowance gets you started, then you pay for the CPU, memory, and GPU resources your workloads consume. Enterprise plans and startup credits are available, and custom pricing can be discussed with sales.

- **Serverless Python functions:** Write plain Python, attach GPUs or containers, and deploy HTTPS endpoints that autoscale with traffic and scale to zero when idle.
- **Sandboxes for agents:** Launch isolated, secure environments for coding agents, RL rollouts, and untrusted code, with sub-second starts and support for over a million concurrent sandboxes.
- **Training and fine-tuning on demand:** Run fine-tunes, LoRA, full training, and reinforcement learning on elastic single or multi-node GPU capacity with no quota requests.
- **Model inference and endpoints:** Serve LLMs and image, video, or audio models through OpenAI-compatible endpoints, or bring your own inference stack and let Modal handle the infrastructure.
- **Fast storage and multi-cloud capacity:** A distributed filesystem loads large images and model weights at full bandwidth, while a pool spanning 20+ clouds routes workloads to the right hardware and region.

**Strengths:** Python-native developer experience with almost no infrastructure boilerplate; Scales from zero to thousands of GPUs with per-second, usage-based billing; Sandboxes are well suited to agentic and untrusted-code workloads; Multi-cloud capacity spans many regions with out-of-the-box observability and enterprise compliance

**Limitations:** Billed by real usage, so heavy GPU workloads can get expensive quickly without cost controls; Some advanced inference and enterprise features require working with the team rather than self-serve signup; The serverless model can feel less familiar to teams used to managing their own VMs or Kubernetes clusters

[Profile (Markdown)](https://openaitools.app/tool/modal-com.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 Modal and Nebius compare in monthly traffic?

Modal has 1,196,021 monthly visits, while Nebius has 1,145,711. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Modal has a month-over-month growth rate of +30.2%; Nebius has +10.7%.

### How does pricing compare?

Modal: Freemium, Contact for Pricing. Modal uses usage-based pricing billed by the second: a free monthly compute allowance gets you started, then you pay for the CPU, memory, and GPU resources your workloads consume. Enterprise plans and startup credits are available, and custom pricing can be discussed with sales. 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 | [Replicate AI](https://openaitools.app/tool/replicate-ai) | 904,038 | -8.4% | Paid | Easier model packaging via Cog, but offers less arbitrary serverless Python container execution than Modal. | [.md](https://openaitools.app/tool/replicate-ai.md) |
| 6 | [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) |
| 7 | [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) |
| 8 | [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) |
| 9 | [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) |
| 10 | [Anyscale](https://openaitools.app/tool/anyscale-com) | 49,779 | -23.2% | Paid, Contact for Pricing | Built on Ray for distributed model training and scaling workloads, but relies on underlying cloud providers rather than owned GPUs. | [.md](https://openaitools.app/tool/anyscale-com.md) |

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Source: https://openaitools.app/compare/modal-com-vs-nebius

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