# Modal vs RunComfy — Cloud ComfyUI with Fast GPUs & Serverless API

Modal records 1,196,021 monthly visits with +30.2% month-over-month growth and Freemium, Contact for Pricing pricing. RunComfy — Cloud ComfyUI with Fast GPUs & Serverless API records 364,336 monthly visits with -8.6% growth and Contact for Pricing pricing.

| Metric | [Modal](https://openaitools.app/tool/modal-com) | [RunComfy — Cloud ComfyUI with Fast GPUs & Serverless API](https://openaitools.app/tool/runcomfy) |
| :--- | :--- | :--- |
| Website URL | https://modal.com | https://www.runcomfy.com |
| Categories | AI Developer Tools, AI API, Large Language Models (LLMs), AI Models | AI Art Generator, AI Image Generator, AI Video Generator, AI Workflow |
| Pricing Model | Freemium, Contact for Pricing | Contact for Pricing |
| Monthly Visits | 1,196,021 | 364,336 |
| MoM Growth Rate | +30.2% | -8.6% |
| Primary Market | United States | United States |
| Direct Visits | 845,636 | 71,198 |
| Review Score | 4.8 | 4.5 |
| Saved Count | 3 | 2 |
| 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)

## RunComfy — Cloud ComfyUI with Fast GPUs & Serverless API

RunComfy targets creators and developers who use ComfyUI but do not want to manage local installs, custom nodes, model files and GPU hardware. The platform provides an unmodified, native ComfyUI instance in the cloud so workflows behave the same as they do locally, and it handles environment setup, dependency resolution and model downloads on your behalf. It advertises coverage of 1M+ creators, 200+ ready-to-run workflow templates, 99.9% uptime and 24/7 support.

A core differentiator is the reproducible workflow environment: a RunComfy workflow bundles the workflow JSON together with the OS, Python environment, ComfyUI version, custom nodes and models as a single snapshot. You can share that snapshot with a link, and teammates, students or followers open it into the exact same environment. GPU options range from 16 GB to 141 GB of VRAM, so both lightweight and very large models can be run on demand.

For production use, RunComfy offers a serverless API that converts any cloud-saved workflow into an HTTP endpoint, handling environment replication, GPU orchestration and autoscaling down to zero when idle. The site also offers a workflow auto-setup agent where you upload a workflow.json and the platform installs missing nodes and models for you. Pricing details are shown on a dedicated pricing page rather than the homepage.

**Pricing & Plans:** RunComfy offers a free starting tier and paid plans; pricing is listed on the RunComfy pricing page rather than the homepage. Check the site for current tiers and usage-based GPU rates.

- **Native cloud ComfyUI:** A fully configured, unmodified ComfyUI in the browser so you can move workflows between local and cloud with no surprises.
- **Reproducible workflow environments:** Save workflow JSON plus OS, Python, ComfyUI, custom nodes and models as one snapshot you can reopen or share by link.
- **Serverless API deployment:** Turn any saved workflow into a production API endpoint with environment replication and autoscaling to zero.
- **Model and node management:** Download models from Civitai, Hugging Face and Google Drive, and install nodes via ComfyUI-Manager.
- **Scalable GPU options:** Choose on-demand machines from 16 GB to 141 GB VRAM for models of any size.

**Strengths:** No local installation or GPU required; Native, unmodified ComfyUI experience; Reproducible environments and link-based sharing; Serverless API with autoscaling for production workloads

**Limitations:** Cloud GPU usage is metered, so heavy runs add cost; Best experience assumes some familiarity with ComfyUI concepts; Pricing details require checking the pricing page rather than the homepage

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

## Frequently Asked Questions

### How do Modal and RunComfy — Cloud ComfyUI with Fast GPUs & Serverless API compare in monthly traffic?

Modal has 1,196,021 monthly visits, while RunComfy — Cloud ComfyUI with Fast GPUs & Serverless API has 364,336. 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%; RunComfy — Cloud ComfyUI with Fast GPUs & Serverless API has -8.6%.

### 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. RunComfy — Cloud ComfyUI with Fast GPUs & Serverless API: Contact for Pricing. RunComfy offers a free starting tier and paid plans; pricing is listed on the RunComfy pricing page rather than the homepage. Check the site for current tiers and usage-based GPU rates.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [ComfyUI](https://openaitools.app/tool/comfyui) | 4,379,348 | +13.4% | Contact for Pricing, Free | Gives you complete local control and zero hosting costs, but requires you to manage your own high-end NVIDIA GPU hardware. | [.md](https://openaitools.app/tool/comfyui.md) |
| 2 | [RunningHub](https://openaitools.app/tool/runninghub) | 3,105,554 | -28.5% | Freemium | Provides a similar cloud ComfyUI execution and API deployment environment, but features a larger library of pre-built public workflows. | [.md](https://openaitools.app/tool/runninghub.md) |
| 3 | [fal.ai](https://openaitools.app/tool/fal-ai) | 3,093,684 | -3.7% | Paid, pay-as-you-go usage-based pricing | Delivers significantly faster raw API generation speeds for generative media, but trades away node-based workflow customizability. | [.md](https://openaitools.app/tool/fal-ai.md) |
| 4 | [RunPod](https://openaitools.app/tool/runpod) | 2,881,043 | +5.9% | Paid | The AI developer cloud for GPU pods, serverless inference and clustering | [.md](https://openaitools.app/tool/runpod.md) |
| 5 | [Together AI](https://openaitools.app/tool/together-ai) | 1,645,930 | +64.9% | Freemium, Paid | Together AI is the AI Native Cloud — a full-stack platform for running, fine-tuning and training open-source models. It spans serverless inference APIs, dedicated endpoints, GPU clusters, post-training and storage, all b | [.md](https://openaitools.app/tool/together-ai.md) |
| 6 | [Vast.ai](https://openaitools.app/tool/vast-ai) | 1,257,554 | +2.8% | Paid | Rent cloud GPUs by the second from a global marketplace | [.md](https://openaitools.app/tool/vast-ai.md) |
| 7 | [Nebius](https://openaitools.app/tool/nebius) | 1,145,711 | +10.7% | Paid, Contact for Pricing | Delivers robust enterprise GPU clusters and managed inference, requiring more infrastructure setup than Modal's serverless approach. | [.md](https://openaitools.app/tool/nebius.md) |
| 8 | [Replicate AI](https://openaitools.app/tool/replicate-ai) | 904,038 | -8.4% | Paid | Offers robust serverless APIs for deploying machine learning models, but requires custom code wrapping rather than visual ComfyUI nodes. | [.md](https://openaitools.app/tool/replicate-ai.md) |
| 9 | [DeepInfra](https://openaitools.app/tool/deep-infra) | 457,810 | -8.9% | Paid | Low-cost serverless inference APIs for 200+ open models | [.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 | Provides integrated development studios and training workflows, but with less granular serverless function orchestration. | [.md](https://openaitools.app/tool/lightning-ai.md) |

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

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