它们的增长率如何比较？

Replicate AI 的月度环比增长率为 -8.4%；Replit 为 -7.7%。

价格如何比较？

Replicate AI: 付费。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. Replit: 免费增值，付费。提供免费模式，聊天时长和项目数量有限。Replit Core 约每月 20 美元（按年付约每月 18 美元），Replit Pro 按年付约每月 90–100 美元，面向商业开发。企业方案提供 SSO/SAML、SOC 2 管控与管理员功能。

Replicate AI 和 Replit 的替代方案有哪些？

相关分类中的其他工具包括 Lovable, Cursor, OpenRouter, Google Antigravity, Base44。

Replicate AI vs Replit — 流量对比 (90.4万 vs 1001.64万)、价格与选型结论

Open AI Tools

Replicate AI vs Replit — 流量对比 (90.4万 vs 1001.64万)、价格与选型结论 | Open AI Tools

并排流量和规格矩阵

维度 / 指标	Replicate AI	Replit
官方域名	replicate.com	replit.com
定价模式	付费	免费增值，付费
月访问量	90.4万 (904,038)	1001.64万 (10,016,437)
月度环比流量变化	-8.26万 (-8.4%)	-83.39万 (-7.7%)
主要市场与份额	🇺🇸 美国 (16.3%)	🇺🇸 美国 (33.4%)
直接访问 / 月	57.75万	613.72万
用户评分	4.6	4.5
支持的平台	网站	网站

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.

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.

优势与优点

+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