Bright Data vs. Apify: Comparison of web scraping platforms for AI

When you compare Bright Data and Apify, your decision often depends on how much control you want over your web data operations. You might prefer managed infrastructure that handles scale and reliability for you, or you might lean toward a developer-focused setup that gives you flexibility to build and run your own scrapers.

Both platforms make public web data accessible for artificial intelligence (AI) systems, but they approach it differently. Bright Data focuses on full-stack infrastructure with purpose-built APIs that simplify large-scale data extraction and delivery. Apify centers on a serverless model built around Actors, giving developers full autonomy to create and orchestrate custom scraping workflows.

In the sections that follow, you’ll see how both platforms differ across architecture, APIs, scalability and integration, helping you choose the best fit for your AI data pipeline.

TL;DR

The following table provides an overview of the Bright Data vs. Apify comparison. 

Compared features	Bright Data	Apify
Core model	Full-stack data infrastructure with purpose-built APIs and tools for large-scale web data collection	Serverless programs called Actors, for web scraping, data processing and automation
API setup	Multi-API setup, including Crawl API, Unlocker API, Archive API, Web Scraper API, AI Answer Engine Scrapers, SERP API and Deep Lookup API	API with RESTful HTTP endpoints and clients in Python and JavaScript
Browser automation	Via Browser API, supported by Web Unlocker algorithm under the hood for reliable web access	Headless browser support (Puppeteer and Playwright) and automatic proxy rotation
Marketplace	Dataset Marketplace for pre-collected datasets	Apify Store for ready-made scrapers
Data export formats	JSON, CSV, HTML, Markdown, NDJSON, JSONL, XLSX, Parquet, depending on the product	JSON (by default), JSONL, HTML, XML, CSV, RSS and XLSX formats, depending on the Actor
Custom development workflows	Cloud-hosted JavaScript-based IDE with AI code generator and 70+ pre-defined JavaScript functions	Open-source Crawlee (available in JavaScript and Python)
Monitoring and scheduling	Monitoring and observability via Control Panel; provides scheduling feature for custom scrapers built with the Web Scraper IDE. Other API products require webhook configuration and third-party integrations to schedule scraping jobs.	Built-in monitoring and scheduling via Apify Console or API
Scalability	Batch processing of up to 5,000 URLs for a single request; concurrent browsing	Actor-to-Actor chaining
Integration	LangChain, CrewAI, LlamaIndex, Dify, Zapier, cloud storage solutions and more	LlamaIndex, Haystack, LangChain, Pinecone, Qdrant, Github, n8n and more
When to use	Large-scale AI training data acquisition, feeding multi-step agentic workflows with live web data	Vertical-specific data extraction with ready-made scrapers, self-hosted custom web scraper development

Technical architecture

Bright Data operates as a full-stack web data infrastructure, while Apify adopts an Actor-based approach.

Bright Data full-stack infrastructure design 

Bright Data’s serverless full-stack infrastructure removes the need for AI teams to build and maintain in-house scraping solutions. Instead, Bright Data provides a suite of purpose-built APIs and tools for accessing and extracting public data. The platform handles the major aspects of scraping dynamic web content, including browser automation, CAPTCHA solving, proxy rotation, browser fingerprinting and content parsing, so teams can focus on retrieving real-time data for AI systems at scale. 

Apify Actor-based approach 

Apify centers its web data extraction capability around Actors, which are cloud-based scraping scripts that users can configure, run and schedule for data collection and web automation tasks. Developers can build and interact with Actors through Apify API or SDKs, while non-technical users can initiate ready-made Actors directly on the Apify Console. This flexibility makes Apify suitable for both teams that want complete code control and those seeking existing scraping solutions with minimal setup overhead. 

Key features comparison of Bright Data vs. Apify

Bright Data and Apify offer extensive features to support AI web scraping and automation workflows. We examine how each platform compares across different capabilities, including API setup, browser automation, custom scraper development and scalability. 

1. API setup

Bright Data provides APIs designed to support a range of AI data workflows, returning scraped results in Markdown, HTML, JSON, CSV, NDJSON, JSONL or plain text formats. They include: 

• Crawl API: Maps and crawls both static and dynamic site structures. Teams can fetch data for AI training pipelines or large language model (LLM) workflows using the Crawl API, with results delivery options for webhooks, cloud storage providers or direct API download. 
• Unlocker API: Uses AI and IP rotation to retrieve dynamic content at scale, while handling browser fingerprinting, JavaScript rendering, CAPTCHA solving and header customization. 
• Archive API: Provides access to Bright Data’s petabyte-scale cached repository of public web data snapshots for multimodal AI training. The repository contains over 100 billion web pages, 70 trillion text tokens, 365 billion video and image URLs and associated metadata, which teams can filter by date, domain, language and more. You can receive the data snapshots into an Amazon S3 bucket or via webhooks. 
• Web Scraper API: Supports large-scale web data collection for Machine Learning (ML) pipelines using 120+ domain-specific endpoints for popular sites. The API accepts both real-time and batch scrape requests to suit your specific use case and scale. Teams can adjust the output schema and export data to webhooks or cloud storage platforms. 
• AI Answer Engine Scrapers: Integrates with AI answer engines, including ChatGPT, Google AI Mode and Perplexity to generate relevant responses with hyperlinked citations from natural language queries. These AI scrapers build on the Web Scraper API and can be integrated into intelligent research assistants to improve their reasoning engine. 
• SERP API: Retrieves real-time search results from Google, Bing, Yandex, DuckDuckGo, Baidu, Yahoo and Naver. Teams can filter the results by device type, location or time range, depending on the search engine. 
• Deep Lookup API: AI-powered search engine that researches on specific entities (such as companies and products) and returns structured datasets with in-line citations and result reasoning explanations for each data field. Deep Lookup uses natural language queries to gather relevant records from the web and Bright Data’s professional datasets and web-scale repository. Teams can access Deep Lookup via its API to refine queries, include additional data columns and export results in JSON or CSV formats for business intelligence and predictive analytics workflows. 

Code examples for each API are available in the Bright Data documentation. 

In contrast, developers can run Actors programmatically through the Apify API with RESTful HTTP endpoints. To launch an existing Actor, you have to send a POST request to the Run Actor endpoint using either the Actor’s name or ID.  

Each run automatically creates a Dataset and key-value stores to store the scraped results. To retrieve results from the Dataset in JSON (default), you’ll need to send a GET request to the Get items endpoint along with the dataset ID you received in the Run response. Apify also provides API clients in JavaScript and Python to optimize interaction with its API. 

Bright Data’s multi-API setup allows teams to choose the most suitable tool for their project requirements, while Apify focuses on a single, language-agnostic API that works in any development environment.

2. Data export formats 

Bright Data offers several data output options depending on the product, including JSON, NDJSON, JSONL, CSV, Markdown, HTML, XLSX, Parquet, plain text and web page screenshot (.png). Teams can choose their preferred data format within the Bright Data Control Panel or pass it in the API request body. The image below illustrates output selection in the Web Scraper API. 

Meanwhile, Apify exports web data in JSON (by default), JSONL, HTML, XML, CSV, RSS and XLSX formats, depending on the Actor. Teams can specify their desired format and customize the output directly in Apify Console or via API request using several filter parameters, including: 

• offset: Skips a specified number of records from the start of the list. The default value is 0.
• limit: Defines the maximum number of records to return. Apify returns all items by default.
• omit: Excludes a specified list of fields from the data.
• unwind: Restructures the dataset by creating new records from specified array fields and merging them with the parent object, or flattening nested object fields into the parent record for easier processing into other formats. 

Both platforms support multiple export formats, with Bright Data offering more AI-ready outputs and Apify providing more control over dataset structure. 

3. Browser automation 

Bright Data automates web data collection for AI agents through its Browser API, which provides access to cloud-based browsers with built-in capabilities for CAPTCHA solving, headers customization, session persistence and parallel browsing. These browsers run in headfull mode and integrate with Bright Data’s Web Unlocker algorithm for reliable web access to dynamic content. 

The Browser API is also compatible with Puppeteer, Selenium and Playwright automation frameworks, enabling developers already working with these tools in their development environment to host their scripts on Bright Data’s infrastructure. To identify and resolve issues within your code, Bright Data provides the Browser API Debugger with Chrome DevTools integration. Teams can launch the Debugger directly from their script or via the Control Panel. 

Conversely, Apify automates browser tasks and executes dynamic content loading using headless browsers (Chrome or Firefox) and intelligent proxy rotation. Dedicated Puppeteer and Playwright Actors simulate human-like user interactions, including navigating web pages, filling out forms and clicking buttons. Still, you may need to manually define custom CSS selectors to fine-tune these Actors for more complex multi-step browser tasks and CAPTCHA solving. 

Bright Data handles the entire browser automation backend, abstracting infrastructure management for teams that prefer a managed environment, while Apify requires more manual configuration and hands-on management. 

4. Custom development workflows 

Bright Data Web Scraper IDE

Bright Data offers a Web Scraper IDE, allowing developers to build custom, JavaScript-based scrapers from scratch or adapt the scraping logic of existing code templates to suit their specific data needs. Teams can start with the AI code generator, which accepts natural language prompts and 70+ ready-made JavaScript functions to reduce development time. The IDE also includes an interactive preview feature and a built-in debugger, so developers can inspect and troubleshoot their scraper in real-time. 

Bright Data also provides a scheduling tool in the IDE dashboard for continuous web data ingestion into third-party cloud storage providers, including Amazon S3, Snowflake and Google Cloud. Teams can configure the output schema and receive data through API download, email or webhooks in JSON, CSV, NDJSON, XLSX or Parquet formats. 

Whereas Apify provides Crawlee, an open-source web crawling and browser automation framework, available in JavaScript and Python. Crawlee enables developers to build and self-host custom web crawlers, while it handles session management, request queuing, proxy rotation, JavaScript rendering, browser fingerprinting and automatic retries. Developers can customize existing templates, built with libraries such as Playwright, Puppeteer, BeautifulSoup and Cheerio, to their AI data acquisition needs.

Below is a sample Python code using the PlaywrightCrawler class, which uses Playwright to control headless browsers and handle JavaScript-rendered content.

import asyncio from crawlee.playwright_crawler import PlaywrightCrawler, PlaywrightCrawlingContext async def main() -> None:     crawler = PlaywrightCrawler(headless=True)         @crawler.router.default_handler     async def request_handler(context: PlaywrightCrawlingContext) -> None:         context.log.info(f’URL: {context.request.url}’)         context.log.info(f’Title: {await context.page.title()}’)         await crawler.run([         ‘https://www.example.com’,         ‘https://www.example.org’,     ]) if __name__ == ‘__main__’:     asyncio.run(main())

By default, Crawlee queues URLs added through the run function and processes them sequentially until it visits all enqueued URLs. For developers who prefer to run their web crawler as an Actor in Apify’s cloud, you can deploy it from shell scripts using the Apify CLI or Apify’s official GitHub integration (apify/push-actor-action). 

Bright Data manages infrastructure, enabling developers to focus on building and fine-tuning scrapers using AI and pre-built functions, while Apify emphasizes full control over crawler creation, deployment and management. 

5. Marketplace 

Bright Data offers a Dataset Marketplace where teams can access pre-collected or freshly gathered datasets to support AI model training and fine-tuning without building custom scrapers. These datasets are sourced from 120+ domains, including travel, finance, news, social media and e-commerce. Bright Data allows teams to create custom subsets that meet their specific data requirements by using filters, such as date and data fields to include or exclude. 

Teams can purchase datasets through a subscription plan or a one-off payment. Supported export formats include JSON, CSV, NDJSON, XLSX and Parquet, which can be delivered through API download, email, webhooks or your preferred cloud storage (AWS S3, Google Cloud, Snowflake, Microsoft Azure or Pubsub). 

In contrast, Apify provides a marketplace of ready-made web scraping tools (Actors), suitable for data teams seeking specialized scrapers without building from scratch. The Apify Store contains 7,000+ Actors, built by both Apify and external developers, to automate web data retrieval from several verticals, including social networks, e-commerce platforms and real estate websites. Teams can initiate the Actors’ run or configure them to specific data needs within the Apify Console. 

Bright Data prioritizes ready-to-use datasets optimized for AI training pipelines, while Apify focuses on pre-built scrapers that connect AI systems to web data. 

6. Monitoring and scheduling

Bright Data allows teams to monitor the progress of data extraction jobs from the Control Panel or through dedicated status-checking endpoints within its APIs. You can track whether a scraper is running, has failed or if its data is ready for download. Developers can also schedule run frequency when building custom scrapers within Bright Data’s Web Scraper IDE, as shown below. However, other API products might require webhook configuration or third-party integrations with automation platforms such as Zapier, Clay and Make to automate recurring scraping jobs. 

Meanwhile, teams can monitor Actors’ performance or run history and define alert triggers for job failures directly on the Apify Console or via its API. Apify will send notifications through email, Slack or in the Console, but developers using the Apify API can set up webhooks to receive real-time updates on specific Actor run events. 

Apify also manages job schedules through its schedule setup tool shown above, where you can define the run interval and time. Developers can automate data extraction jobs via the API using the create schedule endpoint, which accepts requests with a JSON object payload that includes the schedule’s name, your user ID and cron expression.

Bright Data offers built-in monitoring, but may require external schedule configurations, while Apify provides embedded monitoring and scheduling tools with minimal setup.

7. Integrations

AI development teams can integrate Bright Data’s web scraping capabilities into LangChain, LlamaIndex, Agno, CrewAI, Dify, xpander.ai and Pica, allowing LLMs and autonomous agents to retrieve and use up-to-date information. Bright Data connects to no-code platforms such as Clay, n8n, Zapier and Make to automate data extraction workflows. Through its Model Context Protocol (MCP) server, AI systems can access real-time web data. Enterprise users can also transfer web data into their preferred cloud storage solutions using Bright Data’s native support for Amazon S3, Snowflake, Microsoft Azure and Google Cloud Storage. 

Conversely, Apify Actors can push scraped web data into vector databases using pre-built connectors to Pinecone, Qdrant and Milvus. Developers can also call Apify Actors in Langflow, Haystack, Mastra, CrewAI, Langchain and LlamaIndex to access web data for agentic workflows and retrieval systems. Apify also has an MCP Server that enables AI agents to discover and call Apify Actors. For CI/CD workflows, Apify integrates with GitHub Actions. Non-technical users can trigger Actor runs using Apify’s Zapier, Make and n8n integrations. Apify is also compatible with data management platforms, including Airtable, Hevo, Keboola and Airbyte. 

Bright Data focuses on AI framework compatibility and direct cloud storage delivery, while Apify prioritizes complete workflow automation. 

8. Scalability 

Bright Data’s infrastructure is designed to scale with growing data needs. The Browser API has no artificial cap on concurrency, so teams can run multiple browser instances in parallel for high-volume data extraction tasks. Similarly, the Web Scraper API can batch process up to 5,000 URLs per request for teams extracting large-scale web content for model training. 

In contrast, Apify scales web data collection and processing through Actor-to-Actor chaining, which connects multiple Actors using pre-built integrations. For example, you can set up event triggers in the Apify Console or use webhooks to run Actor A, which scrapes product prices from Facebook Marketplace listings. Actor A then passes the scraped results to Actor B, which performs chunking and embedding and stores them in a vector database like Pinecone for semantic search. The image below shows what this chaining looks like in Apify Console.

Bright Data emphasizes bulk content retrieval to meet the data demands of AI training pipelines, while Apify focuses on orchestrating multi-Actor workflows. 

Both platforms support AI systems that require continuous and real-time web data access to ground information.

Strengths and limitations of Bright Data vs. Apify 

Below are some of Bright Data and Apify’s advantages and areas where they might fall short.

Bright Data strengths 

• Bright Data offers both ready-to-use datasets and a customizable scraping setup to suit different development preferences. 
• Bright Data’s Web Scraper API batch scrapes 5000 URLs for large-scale data retrieval and up to 20 URL inputs in real-time processing for AI projects that require live data.
• Bright Data’s Web Unlocker algorithm manages render logic, header formatting, session cookies, automatic retries and response validation, so teams can access and automate structured data retrieval to improve AI model performance.
• Bright Data’s MCP server includes a free tier with 5,000 monthly requests.

Bright Data limitations

• Its pricing structure is more tailored to enterprise users and AI organizations than developer-led projects or small teams.
• Non-technical users or beginners might find Bright Data’s feature variety overwhelming at first. 
• Most Bright Data products lack a built-in scheduling feature, so you may need to automate scraping jobs using webhooks and third-party tools. 

Apify strengths 

• The Apify Store contains 6,000+ ready-to-use Actors and coding templates for diverse use cases, reducing the need to write scraping scripts from scratch.  
• Apify provides native monitoring and scheduling capabilities, so teams can track Actor performance and automate scraping tasks within the Apify Console. 
• Apify’s Crawlee provides developers with fine-grained control to build and manage tailored scraping solutions.
• Apify’s extensive integration options enable teams to use scrapers and transfer web data across different workflows, including vector databases, GitHub and LLM frameworks. 

Apify limitations 

• The input configuration and setup process for some pre-built Actors might be complex for non-technical users. 
• High-volume or long-running data extraction projects can increase compute unit consumption and lead to higher pricing.  
• Community-maintained Actors may be outdated and unreliable for production-scale data pipelines. 

Despite their limitations, Bright Data and Apify provide the diversity, freshness and scale of data needed to support model training pipelines.

When to use Bright Data vs. Apify

The decision to use either Bright Data or Apify comes down to how much control you want over the data extraction process. Below, we’ve highlighted some of the AI development goals each platform is best aligned with.

Choose Bright Data if:

• You want to scale up web data collection in a managed environment. Bright Data provides the cloud-hosted infrastructure, concurrency and browser automation capability that organizations building AI applications need to retrieve public data at scale. 
• You need real-time data feeds for multi-agent workflows. Bright Data MCP Server allows AI agents to call its web search, crawling and browser automation tools. 
• You’re collecting customer reviews from social networks and e-commerce sites for sentiment analysis. Enterprises can use Bright Data’s dedicated social media and e-commerce scrapers, or relevant pre-collected data from the Dataset Marketplace, to train Natural Language Processing (NLP) models that track customer sentiment and monitor business reputation. 

Choose Apify if:

• You need a fully customizable developer-focused tool. With Crawlee, developers can define the logic and manage the execution, resource usage and scale of their scrapers. 
• You’re pulling web data into vector databases for RAG pipelines. Apify provides purpose-built Actors and connectors for Pinecone, Qdrant and Milvus, so teams can populate vector stores, enriching LLM responses with up-to-date information. 
• You’re seeking precise web data for AI support agents or chat assistants. The Apify Store offers vertical-specific Actors that can augment the reasoning capabilities of your support agents by fetching real-time and context-aware web content. 

Final takeaway 

Bright Data and Apify can feed structured and relevant data into training pipelines and RAG systems, but choosing the right platform for your AI project involves understanding your data goals, customization needs and how much infrastructure management you’re willing to handle long-term. 

To inform your decision, experiment with their free trials and assess which platform offers the tools and services that align best with your use case before fully committing.