Dataify
Officially listedA full-pipeline AI data service platform built for LLMs and AI agents.
Dataify
Dataify is an infrastructure platform focused on full-pipeline AI data services, dedicated to connecting raw internet data with high-quality AI training needs. It is not only a powerful web scraping engine but also the "eyes" through which LLM development and AI agents acquire real-time data.
Core Capabilities
- Automated data collection APIs: Out-of-the-box interfaces for extracting search engine results (SERP), structured web pages, and multimodal video data, making data acquisition simple and efficient.
- Global proxy network infrastructure: Massive IP resources spanning dynamic residential and static datacenter pools help users easily break through anti-scraping limits and regional blocks.
- AI-specific training datasets: High-quality labeled text, image, and audio/video data for LLM pre-training (CPT) and fine-tuning (SFT), accelerating model iteration.
- Native MCP support: An official server based on the Model Context Protocol lets AI assistants such as Claude easily gain real-time web scraping capability.
- AI-ready structured output: Outputs JSON structured data highly optimized for AI training, saving developers substantial data cleaning time.
Best For Best for LLM training teams, AI agent developers, and data scientists. Whether you need to prepare massive fine-tuning data for an LLM or give your automated business research system stable data scraping capability, Dataify provides professional low-level support.
Unique Advantages Compared with traditional scraping tools or underlying proxy vendors, Dataify's biggest advantage is its "AI-native" DNA. It perfectly fuses proxy IPs, blocking countermeasures, and standardized data APIs — not only solving the scraping problem but, through the MCP protocol, letting AI tools directly and proactively fetch external data.
Editorial Review Dataify is a highly forward-looking choice in today's AI data infrastructure. It keenly captures LLMs' hunger for high-quality, real-time external data, and its deep integration of the MCP protocol is particularly impressive. Although the pure API-driven model has a certain bar for non-technical users, for teams with development capability it is an indispensable low-level data engine for building next-generation intelligent applications.
Pricing
### 💰 定价模式：免费增值 / 按量付费 **起步价**：免费 #### 主要方案 - **免费试用**：免费 - 包含基础测试额度，用于测试代理质量和基础抓取 API 功能。 - **自助按需购买**：按量付费 - 针对网络代理服务和标准采集 API，按流量或按调用次数计费。 - **企业定制服务**：联系销售 - 针对 AI 训练数据集、特殊行业抓取规则及大规模企业级并发需求。 — Visit website
FAQ
Is Dataify free?
Dataify offers free trial credits for new users to test basic scraping features and proxy quality; after that, usage is billed through on-demand purchases or customized enterprise plans, and users can pick the right package for their business scale.
What can Dataify be used for?
Dataify is mainly used for acquiring LLM fine-tuning data, real-time web data scraping for AI agents, automated competitor monitoring and analysis, and web data extraction and structuring across major platforms.
What types of proxy networks does Dataify support?
Dataify's network infrastructure is very comprehensive, offering massive global proxy IPs covering dynamic residential networks, high-concurrency static datacenter networks, static ISP networks, and more, effectively helping users break through strict anti-scraping restrictions.
What does Dataify's MCP Server do?
Through the official MCP Server (Model Context Protocol), developers can minimally equip AI assistants such as Claude Desktop or LobeChat with real-time web search and unlock-scraping capability.
How does Dataify differ from Bright Data?
Compared with Bright Data, which leans toward traditional underlying proxy services, Dataify's product form is more "AI-native," offering data cleaning optimized for LLM fine-tuning and native support for frontier AI protocol integrations such as MCP.
Is Dataify suitable for non-technical users?
Because Dataify relies heavily on API development and JSON configuration files for task scheduling, non-technical users or those without a code background face a certain barrier; it better suits engineering teams with development capability.