# adata-plugin vs Replicate AI

adata-plugin 记录了 0 月访问量，月度环比增长为 —，定价为免费增值。Replicate AI 记录了 904,038 月访问量，增长率为 -8.4%，定价为付费。

| 指标 | [adata-plugin](https://openaitools.app/zh/tool/adata-plugin) | [Replicate AI](https://openaitools.app/zh/tool/replicate-ai) |
| :--- | :--- | :--- |
| 官网地址 | https://chromewebstore.google.com/detail/adata-plugin/aaoeokehpfjjdfdjpmfnhfpfbemffpaf | https://replicate.com |
| 分类 | AI Developer Tools, AI Productivity Tools, AI For Data Analytics | AI API, AI Models, AI Developer Tools, Open Source AI Models |
| 定价模式 | 免费增值 | 付费 |
| 月访问量 | 0 | 904,038 |
| 月度环比增长率 | — | -8.4% |
| 主要市场 | 全球 | 美国 |
| 直接访问量 | 0 | 577,501 |
| 评价得分 | 3.5 | 4.6 |
| 收藏数 | 0 | 5 |
| 支持平台 | 浏览器扩展 | 网站 |

## adata-plugin

adata-plugin 是一款面向每天进行数据标注工作的人群打造的浏览器扩展，适用于文本标注、分类任务及其他监督学习数据集。你无需逐条手动点击，扩展会在你现有页面上叠加 AI 辅助流程，给出建议标签，标注员只需确认、微调或覆盖，而不必每次从零判断。

该扩展直接通过 Chrome 应用商店分发，一键即可安装，无需特殊硬件或账号设置。它由个人开发者 so.zuwei 提供，当前版本为 1.0.0，首次发布于 2022 年 7 月，界面为中文。开发者声明不收集也不出售用户数据，这对处理敏感或专有数据集的团队尤为重要。

由于它是一款紧凑型实用工具而非完整标注平台，adata-plugin 最适合作为效率层叠加在你团队已有的标注工具或表格之上。它适合小型标注团队、自由职业数据标注员，以及希望在不迁移到企业级标注套件的前提下获得低摩擦提速的研究人员。

**定价与套餐:** 作为免费 Chrome 扩展分发，目前没有公开的付费版本；任何未来的高级功能将由开发者直接处理。

- **AI 标注建议:** 在你工作时给出可能的标签或标注，让你确认建议而非对每条数据从零分类。
- **一键 Chrome 安装:** 通过 Chrome 应用商店完全在浏览器中运行，无需服务器、插件依赖或创建账号。
- **贴合现有工作流:** 作为辅助层叠加在你已有的标注页面上，无需迁移数据集或工具。
- **注重隐私的设计:** 开发者声明不出售、不共享用户数据，也不会将其用于无关用途。
- **轻量占用:** 约 3.4 MB 的小型扩展，中文界面，旨在让标注过程保持快速、少干扰。

**优势:** 可直接从 Chrome 应用商店免费安装使用; 无需账号、后端或技术配置; 开发者明确声明不收集、不出售用户数据; 在不替换现有标注工具的前提下加速重复性标注工作

**局限:** 个人小型项目，没有评分或评价可供参考; 最后更新于 2022 年，未跟进较新的浏览器或 AI 模型变化; 界面仅提供中文，其他语言团队使用不便; 范围非常窄——仅辅助标注，并非完整标注平台

[工具详情（Markdown）](https://openaitools.app/zh/tool/adata-plugin.md)

## 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.

**定价与套餐:** 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.

**优势:** 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

**局限:** 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

[工具详情（Markdown）](https://openaitools.app/zh/tool/replicate-ai.md)

## 常见问题

### adata-plugin 和 Replicate AI 在月流量上如何比较？

adata-plugin 有 0 月访问量，而 Replicate AI 有 904,038。流量估算描述的是受众规模，而非产品质量。

### 它们的增长率如何比较？

adata-plugin 的月度环比增长率为 —；Replicate AI 为 -8.4%。

### 价格如何比较？

adata-plugin: 免费增值。作为免费 Chrome 扩展分发，目前没有公开的付费版本；任何未来的高级功能将由开发者直接处理。 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.

## 替代工具

| # | 工具名称 | 月访问量 | 月度环比增长率 | 定价模式 | 简介 | 工具详情（Markdown） |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [OpenRouter](https://openaitools.app/zh/tool/openrouter-ai) | 19,941,437 | -6.2% | 付费 | 擅长跨多个提供商路由文本和多模态提示词，但缺乏 Replicate 专属的自定义 ML 部署功能。 | [.md](https://openaitools.app/zh/tool/openrouter-ai.md) |
| 2 | [fal.ai](https://openaitools.app/zh/tool/fal-ai) | 3,093,684 | -3.7% | Paid, pay-as-you-go usage-based pricing | 图片和视频模型的无服务器执行速度更快，但相比 Replicate，通用自定义模型部署能力稍弱。 | [.md](https://openaitools.app/zh/tool/fal-ai.md) |
| 3 | [Postman](https://openaitools.app/zh/tool/postman) | 1,764,877 | -7.6% | 免费增值 | 设计、测试与治理 API —— 让 AI 智能体参与其中 | [.md](https://openaitools.app/zh/tool/postman.md) |
| 4 | [Together AI](https://openaitools.app/zh/tool/together-ai) | 1,645,930 | +64.9% | 免费增值，付费 | 更专注于 LLM 推理和微调，但缺乏 Replicate 那样由社区策划的多样化媒体模型目录。 | [.md](https://openaitools.app/zh/tool/together-ai.md) |
| 5 | [Unsloth AI](https://openaitools.app/zh/tool/unsloth-ai) | 887,457 | -17.6% | 免费 | Specializes specifically in ultra-efficient model fine-tuning, rather than providing a general-purpose model hosting API. | [.md](https://openaitools.app/zh/tool/unsloth-ai.md) |
| 6 | [Atlas Cloud](https://openaitools.app/zh/tool/atlas-cloud) | 777,031 | -16.4% | 付费 | Offers a broad mix of generative media models via API, but lacks Replicate's massive community deployment ecosystem. | [.md](https://openaitools.app/zh/tool/atlas-cloud.md) |
| 7 | [Labelbox](https://openaitools.app/zh/tool/labelbox) | 762,803 | -22.8% | 定价请咨询 | 前沿 AI 训练所用的环境，以及负责重复性工作的智能体 | [.md](https://openaitools.app/zh/tool/labelbox.md) |
| 8 | [Fireworks](https://openaitools.app/zh/tool/fireworks-ai) | 659,687 | +6% | 定价请咨询 | 为开源模型提供极快的推理速度，但模型选择比 Replicate 窄。 | [.md](https://openaitools.app/zh/tool/fireworks-ai.md) |
| 9 | [HARPA AI](https://openaitools.app/zh/tool/harpa-ai) | 537,461 | +99.3% | 免费增值 | 把各大 AI 模型与网页自动化合而为一的浏览器智能体 | [.md](https://openaitools.app/zh/tool/harpa-ai.md) |
| 10 | [DeepInfra](https://openaitools.app/zh/tool/deep-infra) | 457,810 | -8.9% | 付费 | 运行开源权重模型的定价显著更便宜，但提供的先进自定义训练和部署功能较少。 | [.md](https://openaitools.app/zh/tool/deep-infra.md) |

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来源：https://openaitools.app/zh/compare/adata-plugin-vs-replicate-ai

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