# Replicate AI vs Runware

Replicate AI는 월간 방문 904,038, 전월 대비 성장률 -8.4%, 유료 가격 모델을 기록합니다. Runware는 월간 방문 402,862, 성장률 +27.1%, 유료 가격 모델을 기록합니다.

| 지표 | [Replicate AI](https://openaitools.app/ko/tool/replicate-ai) | [Runware](https://openaitools.app/ko/tool/runware) |
| :--- | :--- | :--- |
| 웹사이트 URL | https://replicate.com | https://runware.ai |
| 카테고리 | AI API, AI 모델, AI 개발자 도구, 오픈소스 AI 모델 | AI API, AI 이미지 생성기, Text to Image, AI 모델 |
| 가격 모델 | 유료 | 유료 |
| 월간 방문 수 | 904,038 | 402,862 |
| 전월 대비 성장률 | -8.4% | +27.1% |
| 주요 시장 | 미국 | 미국 |
| 직접 방문 | 577,501 | 189,796 |
| 리뷰 점수 | 4.6 | 4.5 |
| 저장 수 | 5 | 0 |
| 지원 플랫폼 | 웹사이트 | 웹사이트 |

## Replicate AI

Replicate is a cloud API for running, fine-tuning, and deploying machine learning models. Spin up open-source and hosted models — from image, video, and audio generation to speech synthesis, captioning, and LLM inference — with one line of code, or package your own model with Cog and put it behind a production-ready API.

**가격 및 요금제:** Paid, usage-based — you only pay for what you use. Most public models are billed by runtime at per-second hardware rates; some models are billed per input and output (for example, $0.04 per output image on Flux 1.1 Pro, $3.75 per million input tokens on DeepSeek R1). Published GPU hardware ranges from about $0.81/hr for a T4 up to roughly $43.92/hr for 8x H100. Private deployments bill for setup, idle, and active time on dedicated hardware, and you can start for free. Enterprise and volume discounts available.

- **Thousands of models behind one API:** Run image, video, audio, speech, and language models through a uniform API surface, with official models for FLUX, Nano Banana, Seedream, Grok Imagine, MiniMax, Wan, Seedance, and more.
- **One-line-of-code inference:** Set up a model in minutes and make an API call in Python, Node, or plain HTTP after authenticating with an API token.
- **Cog packaging and custom deployments:** Package your own model with Cog, an open-source tool for packaging machine learning models, and deploy it on dedicated hardware that scales up and down with demand.
- **Model fine-tuning:** Fine-tune models on your own data, including fast-booting fine-tunes that are billed only while actively processing requests rather than for idle time.
- **Published hardware and inference rates:** Transparent per-second hardware rates across CPU, T4, L40S, A100, H100, and H200 configurations, with committed-spend options for multi-GPU capacity.

**강점:** Very broad model catalog spanning image, video, audio, speech, and LLMs behind one consistent API; Usage-based billing with openly published per-second hardware and per-output model rates; Cog makes it straightforward to package and deploy your own private models; Automatic scaling handles traffic spikes without manual capacity planning

**한계:** Costs are consumption-based and can rise quickly with heavy traffic or always-on private deployments; Private models bill for idle and setup time, not just request processing, unless they are fast-booting fine-tunes; Long-running GPU models can queue on shared public capacity during peak periods; It is an infrastructure layer, so you still need your own prompts, pipelines, and application code

[Profile (Markdown)](https://openaitools.app/ko/tool/replicate-ai.md)

## Runware

Runware is an AI inference platform that exposes an enormous model catalog — images, video, audio, LLMs and 3D — through a single API endpoint, alongside serverless GPU compute and reserved capacity running on modular data centers the company designs and operates itself. It is aimed at developers and product teams who want to ship generative features without managing infrastructure.

**가격 및 요금제:** Pay-as-you-go with no subscription required. Managed inference is billed per generation, per second of video, per character of audio, or per 1M tokens for LLMs; published rates include examples such as FLUX 3 Image Klein from about $0.003 per image and GLM-5.3-Flash at $0.15 per 1M input tokens. Serverless GPU compute is metered by the second (RTX PRO 6000 listed around $1.99 per GPU-hour) and scales to zero; reserved capacity is advertised as low as $0.63 per GPU-hour on term commitments, with dedicated bare-metal clusters available from 72 GPUs. Failed or rate-limited requests are not charged, new users signing up with a business email receive $2 in free credits, and volume discounts are available via sales.

- **One API for every modality:** Image, video, audio, LLM and 3D tasks all POST to a single endpoint as an array, so mixed-modality batches can travel in one request with shared auth and billing.
- **400K+ model catalog:** Browse hosted, partner and community models through one addressing scheme, control parameters such as stacked LoRAs and swapped VAEs on open models, and add your own uploads.
- **Serverless and reserved GPU compute:** Bring your own container and weights to serverless GPUs billed by the second that scale to zero when idle, or commit to reserved capacity and bare-metal clusters for a lower per-hour rate.
- **Sonic Pods and the Sonic Inference Engine:** Purpose-built modular data centers plus an orchestration layer that keeps weights preloaded across regions and routes requests to pods that already hold the model in memory.
- **Developer tooling and agent integrations:** A hosted MCP server, CLI, skill files, playground, model schemas and llms.txt let you wire Runware into Claude Code, Cursor, Codex or any MCP-capable client with minimal setup.
- **Enterprise-grade trust and compliance:** SOC 2, ISO 27001 and GDPR compliance, EU and US data residency, SSO/SAML, configurable MFA, a strict no-training-on-customer-data policy and optional Zero Data Retention.

**강점:** Unusually broad catalog — hundreds of thousands of models behind one consistent endpoint and API key; Aggressive published pricing, with claims of up to 10x lower cost per generation than market rates; Both managed inference and raw compute in one platform, so you can migrate from API calls to your own containers without changing vendors; Real infrastructure differentiation through self-designed modular data centers rather than reselling someone else's capacity

**한계:** The breadth of the catalog and the mix of hosted, partner and community models can be overwhelming when choosing a model; Only a subset of models (currently around 300+) has published pricing on the rate sheet, so some community models require checking the playground for cost; LLM billing splits input, output and cache-read token dimensions, adding complexity to cost forecasting; Bare-metal and high-volume reserved capacity are sales-gated rather than self-serve

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

## 자주 묻는 질문

### Replicate AI과(와) Runware의 월간 트래픽은 어떻게 비교되나요?

Replicate AI의 월간 방문 수는 904,038이고, Runware는 402,862입니다. 트래픽 추정치는 제품 품질이 아니라 사용자 규모를 나타냅니다.

### 두 도구의 성장률은 어떻게 비교되나요?

Replicate AI의 전월 대비 성장률은 -8.4%이며, Runware는 +27.1%입니다.

### 가격은 어떻게 비교되나요?

Replicate AI: 유료. Paid, usage-based — you only pay for what you use. Most public models are billed by runtime at per-second hardware rates; some models are billed per input and output (for example, $0.04 per output image on Flux 1.1 Pro, $3.75 per million input tokens on DeepSeek R1). Published GPU hardware ranges from about $0.81/hr for a T4 up to roughly $43.92/hr for 8x H100. Private deployments bill for setup, idle, and active time on dedicated hardware, and you can start for free. Enterprise and volume discounts available. Runware: 유료. Pay-as-you-go with no subscription required. Managed inference is billed per generation, per second of video, per character of audio, or per 1M tokens for LLMs; published rates include examples such as FLUX 3 Image Klein from about $0.003 per image and GLM-5.3-Flash at $0.15 per 1M input tokens. Serverless GPU compute is metered by the second (RTX PRO 6000 listed around $1.99 per GPU-hour) and scales to zero; reserved capacity is advertised as low as $0.63 per GPU-hour on term commitments, with dedicated bare-metal clusters available from 72 GPUs. Failed or rate-limited requests are not charged, new users signing up with a business email receive $2 in free credits, and volume discounts are available via sales.

## 대안

| # | 도구 이름 | 월간 방문 수 | 전월 대비 성장률 | 가격 모델 | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [OpenRouter](https://openaitools.app/ko/tool/openrouter-ai) | 19,941,437 | -6.2% | 유료 | 하나의 API 키로 주요 제공사의 수백 개 모델을 사용 | [.md](https://openaitools.app/ko/tool/openrouter-ai.md) |
| 2 | [RunningHub](https://openaitools.app/ko/tool/runninghub) | 3,105,554 | -28.5% | 무료+유료 | RunningHub is a cloud platform for running ComfyUI workflows online, building AI apps, and generating images and videos with hosted models and APIs. | [.md](https://openaitools.app/ko/tool/runninghub.md) |
| 3 | [fal.ai](https://openaitools.app/ko/tool/fal-ai) | 3,093,684 | -3.7% | Paid, pay-as-you-go usage-based pricing | 초고속 생성형 미디어 추론을 위한 유사한 개발자 중심 클라우드를 제공하지만, 가격 티어가 다릅니다. | [.md](https://openaitools.app/ko/tool/fal-ai.md) |
| 4 | [Together AI](https://openaitools.app/ko/tool/together-ai) | 1,645,930 | +64.9% | 무료+유료, 유료 | 오픈소스 LLM 호스팅 및 전용 클러스터에 더 초점을 맞추지만, 즉각적인 이미지 생성에는 덜 최적화되어 있습니다. | [.md](https://openaitools.app/ko/tool/together-ai.md) |
| 5 | [Unsloth AI](https://openaitools.app/ko/tool/unsloth-ai) | 887,457 | -17.6% | 무료 | Specializes specifically in ultra-efficient model fine-tuning, rather than providing a general-purpose model hosting API. | [.md](https://openaitools.app/ko/tool/unsloth-ai.md) |
| 6 | [Atlas Cloud](https://openaitools.app/ko/tool/atlas-cloud) | 777,031 | -16.4% | 유료 | 생성형 미디어 모델에 대한 통합 접근을 제공하지만, 순수 추론 속도에 대한 Runware의 특화된 초점은 부족합니다. | [.md](https://openaitools.app/ko/tool/atlas-cloud.md) |
| 7 | [Fireworks](https://openaitools.app/ko/tool/fireworks-ai) | 659,687 | +6% | 가격 문의 | 오픈 모델에 대해 뛰어나게 빠른 추론 속도를 제공하지만, Replicate보다 모델 선택 폭이 좁습니다. | [.md](https://openaitools.app/ko/tool/fireworks-ai.md) |
| 8 | [DeepInfra](https://openaitools.app/ko/tool/deep-infra) | 457,810 | -8.9% | 유료 | 저비용 오픈 웨이트 모델 추론에 뛰어나지만, 복잡한 이미지 생성 파이프라인을 위한 특화된 도구는 적습니다. | [.md](https://openaitools.app/ko/tool/deep-infra.md) |
| 9 | [Pollinations](https://openaitools.app/ko/tool/pollinations) | 316,875 | +22.8% | 무료 | 생성 AI 개발자를 위한 개방형 인프라와 단일 API | [.md](https://openaitools.app/ko/tool/pollinations.md) |
| 10 | [Novita AI](https://openaitools.app/ko/tool/novita-ai) | 271,371 | -10.4% | 유료 | 이미지 생성 및 Stable Diffusion API에 크게 집중하지만, 서버레스 GPU 가격 책정이 약간 다릅니다. | [.md](https://openaitools.app/ko/tool/novita-ai.md) |

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출처: https://openaitools.app/ko/compare/replicate-ai-vs-runware

More Markdown: [카테고리](https://openaitools.app/ko/categories.md) · [트래픽 순위](https://openaitools.app/ko/rankings.md) · [llms.txt](https://openaitools.app/ko/llms.txt)