# ComfyOnline vs Modal

ComfyOnline records 19,465 monthly visits with -30.7% month-over-month growth and Freemium, Paid pricing. Modal records 1,196,021 monthly visits with +30.2% growth and Freemium, Contact for Pricing pricing.

| Metric | [ComfyOnline](https://openaitools.app/tool/comfyonline) | [Modal](https://openaitools.app/tool/modal-com) |
| :--- | :--- | :--- |
| Website URL | https://www.comfyonline.app | https://modal.com |
| Categories | AI API, AI Developer Tools, AI Image Generator, AI Video Generator | AI Developer Tools, AI API, Large Language Models (LLMs), AI Models |
| Pricing Model | Freemium, Paid | Freemium, Contact for Pricing |
| Monthly Visits | 19,465 | 1,196,021 |
| MoM Growth Rate | -30.7% | +30.2% |
| Primary Market | United States | United States |
| Direct Visits | 7,798 | 845,636 |
| Review Score | 4.3 | 4.8 |
| Saved Count | 1 | 3 |
| Supported Platforms | Website | Website |

## ComfyOnline

ComfyOnline hosts ComfyUI — the popular node-based interface for diffusion models — in the browser. You build or import node graphs using the same custom-node ecosystem people run locally, but the heavy compute happens on cloud GPUs such as the RTX 4090, A6000, L40S, A100 and H100. Editing a workflow is free; you are only charged for the seconds a workflow actually runs, which matches the serverless model the team advertises.

The distinguishing piece is deployment. Instead of leaving a graph as a one-off experiment, ComfyOnline can automatically generate an API endpoint for it, so a workflow becomes a callable service you can wire into your own product. The platform also brokers third-party model providers inside the same canvas: video generation through Kling, Runway, Luma, Pika and Wan; image generation through Flux Pro Ultra, Ideogram and Recraft; speech through ElevenLabs; and chat/LLM calls to Claude, Gemini, GPT and DeepSeek. That makes it a single control panel for mixing open-source diffusion pipelines with commercial model APIs.

Pricing is credit-based rather than flat. Top-ups start around $10 and scale up, and monthly membership tiers (Personal, Startups, Business) add larger credit bundles, relax-mode queueing and customization including private LoRAs and models. Because generation cost depends on the GPU class and runtime, teams should treat the per-second credit rates as the main planning input.

**Pricing & Plans:** Freemium-style: editing workflows is free and you pay only for runtime. Pay-as-you-go credit packs start around $10 (with bonus credits), and monthly membership tiers such as Personal, Startups and Business unlock larger credit bundles, unlimited relax-mode queues and custom deployment options.

- **Cloud ComfyUI workspace:** Build and run node graphs in the browser on 4090, A6000, L40S, A100 or H100 GPUs, with the common custom-node packs preinstalled.
- **One-click API deployment:** Turn any workflow into an auto-generated API endpoint so it can be called from your own apps and services without writing the serving layer yourself.
- **Built-in model brokers:** Access Kling, Runway, Luma, Pika and Wan for video, Flux Pro Ultra, Ideogram and Recraft for images, ElevenLabs for voice, and Claude, Gemini, GPT and DeepSeek for text inside the same canvas.
- **Pay-per-second billing:** Editing costs nothing; you are billed only for the seconds a workflow executes, with idle GPUs never charged.
- **Private models and scaling:** Upload private checkpoints and LoRAs, and let deployments scale automatically when traffic grows.

**Strengths:** No local GPU needed; runs on high-end cloud cards; Workflow-to-API generation removes a big chunk of backend work; Wide list of third-party video, image, audio and LLM integrations; Transparent per-second credit pricing with no idle charges

**Limitations:** Credit-per-second math takes some effort to forecast real spend; Some advanced GPU tiers require specific plan upgrades; Documentation and enterprise terms are thinner than larger cloud vendors

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

## 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)

## Frequently Asked Questions

### How do ComfyOnline and Modal compare in monthly traffic?

ComfyOnline has 19,465 monthly visits, while Modal has 1,196,021. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

ComfyOnline has a month-over-month growth rate of -30.7%; Modal has +30.2%.

### How does pricing compare?

ComfyOnline: Freemium, Paid. Freemium-style: editing workflows is free and you pay only for runtime. Pay-as-you-go credit packs start around $10 (with bonus credits), and monthly membership tiers such as Personal, Startups and Business unlock larger credit bundles, unlimited relax-mode queues and custom deployment options. 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.

## 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 | Extremely fast serverless execution optimized specifically for generative media models rather than general Python code. | [.md](https://openaitools.app/tool/fal-ai.md) |
| 2 | [BytePlus](https://openaitools.app/tool/byteplus) | 3,036,127 | -30% | Freemium, Paid | AI-native cloud for building and scaling enterprise AI | [.md](https://openaitools.app/tool/byteplus.md) |
| 3 | [RunPod](https://openaitools.app/tool/runpod) | 2,881,043 | +5.9% | Paid | Provides flexible GPU infrastructure and serverless endpoints, though less focused on pure Python function decorators. | [.md](https://openaitools.app/tool/runpod.md) |
| 4 | [Together AI](https://openaitools.app/tool/together-ai) | 1,645,930 | +64.9% | Freemium, Paid | Focuses heavily on managed open-source model inference APIs rather than arbitrary custom container execution. | [.md](https://openaitools.app/tool/together-ai.md) |
| 5 | [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) |
| 6 | [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) |
| 7 | [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) |
| 8 | [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) |
| 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 | [RunComfy — Cloud ComfyUI with Fast GPUs & Serverless API](https://openaitools.app/tool/runcomfy) | 364,336 | -8.6% | Contact for Pricing | Run native ComfyUI in the cloud with zero setup, fast GPUs, and one-click serverless APIs | [.md](https://openaitools.app/tool/runcomfy.md) |

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

More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)