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Scrapy VS ReefAPI

Compare Scrapy VS ReefAPI and see what are their differences

Scrapy

A Fast and Powerful Scraping and Web Crawling Framework

Rating
0 reviews
Pricing
Open source
Alternatives Is it good?
ReefAPI

One HTTP API over 185+ live web data sources - search, social, e-commerce, real estate, jobs and finance - returning clean JSON.

Rating
0 reviews
Pricing
Freemium $15 / Monthly (Pro - 10,000 credits/mo)
Alternatives Is it good?
you.bot
One API for 80+ AI models — LLM, image, video & music — priced up to 80% below the official APIs. Pay only for successful calls; failed runs refunded; credits never expire. sponsored

Which is more popular?

Based on our record, Scrapy seems to be more popular. It has been mentioned 102 times since March 2021.

social mentions
102 vs 0
Web Scraping popularity
98% vs 2%
alternatives listed
236 vs 13

Base details

Website, pricing, platforms and company facts side by side.

Scrapy
ReefAPI
Website scrapy.org reefapi.com
Pricing
Open source
Freemium $15 / Monthly (Pro - 10,000 credits/mo) Official pricing
Platforms —
Web REST API SaaS Cloud Cross Platform +2
Company — Startup from Turkey · 1 - 9 employees · 2026
Listed in
Web Scraping Data Extraction Web Crawling Data +1
Web Scraping API Data Integration Web Scraping Data Extraction APIs Web Service Automation API Tools Developer Tools +5

About Scrapy and ReefAPI

In their own words, as submitted to SaaSHub.

Scrapy
ReefAPI

No description of Scrapy yet.

ReefAPI is one HTTP API over 185+ live web data sources. Instead of maintaining a scraper per site, you send one request shape to one endpoint and get normalised JSON back: search engines, social platforms, e-commerce, real estate, job boards, travel, news, finance and company data. One API key...

Read more about ReefAPI

Features and specs

What each product offers, as listed by its team.

Scrapy 6 features
ReefAPI 6 features
  • Efficiency
    Scrapy is designed to be efficient and robust, capable of handling multiple tasks simultaneously and scraping large websites in a fast and reliable manner.
  • Built-in Tooling
    Scrapy comes with built-in tools for handling common tasks such as following links, extracting data using XPath and CSS, and exporting data in a variety of formats.
  • Customization
    Scrapy offers extensive customization options, allowing users to build complex spiders and modify their behavior through middleware and pipelines.
  • Python Integration
    Being a Python framework, Scrapy integrates seamlessly with the Python ecosystem, enabling the use of libraries like Pandas, NumPy, and others to process and analyze scraped data.
  • Community Support
    Scrapy has a large and active community, providing extensive documentation, tutorials, and third-party extensions to enhance functionality.
  • Asynchronous Processing
    Scrapy’s asynchronous processing model enhances performance by allowing multiple concurrent requests, reducing the time required for crawling sites.

Possible disadvantages

  • Steep Learning Curve
    For beginners, Scrapy's comprehensive feature set and the need for understanding concepts like XPath and CSS selectors can be challenging.
  • Resource Intensive
    Scrapy can be resource-intensive, potentially consuming significant memory and CPU, which can be problematic for scraping very large websites or running multiple spiders simultaneously.
  • Debugging Complexity
    Debugging Scrapy projects can be complex due to its asynchronous nature and the multiple layers of middleware and pipelines that need to be understood.
  • Overhead for Small Projects
    For simple or small-scale scraping tasks, the overhead of setting up and configuring a Scrapy project might be excessive, with simpler alternatives being more suitable.
  • Limited JavaScript Support
    Scrapy's out-of-the-box support for JavaScript-heavy websites is limited, requiring additional tools like Splash or Selenium, which can complicate the setup.
  • Dependency Management
    Managing Scrapy's dependencies and compatibility with other Python packages can sometimes be challenging, leading to potential conflicts and maintenance overhead.
  • One key for 185+ sources
    A single API key and one shared credit pool cover every source: search engines, social platforms, e-commerce, real estate, job boards, travel, news, finance and company data. No per-source contracts.
  • One request shape
    Every source uses the same POST /<engine>/v1/<action> shape and returns the same JSON envelope, so adding a new source does not mean writing a new client.
  • Normalised JSON output
    Responses are parsed into consistent fields across sources rather than returned as raw HTML, so a price, a listing or a review has the same shape whichever site it came from.
  • Failed calls are not charged
    Credits are only consumed by calls that return data. Failed or blocked requests cost nothing.
  • MCP server for AI assistants
    An MCP endpoint lets AI assistants and agents discover and call the same 185+ sources directly, without custom glue code.
  • Playground and free tier
    1,000 free credits with no card, and an in-browser playground for trying any endpoint before writing code.

Analysis

An editorial look at what each product does well and who it suits.

Scrapy
ReefAPI

Overall verdict

  • Yes, Scrapy is a good option for those looking to implement web scraping projects due to its robust set of features, active community, and comprehensive documentation. It is particularly well-suited for projects that require scraping from multiple websites and processing large volumes of data efficiently.

Why this product is good

  • Scrapy is a popular open-source web crawling framework for Python that's designed for extensive, flexible, and efficient web scraping. Its built-in tools and features make it easy to extract data from websites quickly and automatically. Key advantages include its ability to handle requests asynchronously, its support for multiple protocols, its item pipeline feature that allows for data cleaning and storage, and its ease of integration with other Python libraries and databases.

Recommended for

    Scrapy is recommended for developers, data scientists, and businesses that need to gather data from websites efficiently. It's particularly useful for projects involving data aggregation, market research, competitive analysis, and monitoring pricing changes across various platforms.

Overall verdict

  • I don't have verified, up-to-date information about a product or service called 'ReefAPI' at reefapi.com, so I can't confirm its legitimacy, quality, or features. Before using it, please research independently.

Why this product is good

  • No verifiable information is available in my training data about this specific service
  • I cannot confirm claims about functionality, pricing, or reliability without a verified source
  • Domain names can change ownership or purpose over time, making prior knowledge unreliable

Recommended for

  • Users should check the site directly for documentation, pricing, and terms of service
  • Look for independent reviews, GitHub repos, or developer community discussions before integrating it
  • Verify company legitimacy through domain registration lookup, SSL certificates, and contact information
  • Test any API with a free tier or sandbox environment before committing to production use

Videos

Walkthroughs and reviews on video.

Scrapy 3 videos + Add
ReefAPI 0 videos + Add

Python Scrapy Tutorial - 22 - Web Scraping Amazon

More videos

  • - Scrapy - Overview and Demo (web crawling and scraping)
  • - GFuel LemoNADE Taste Test &amp; Review! | Scrapy

No ReefAPI videos yet. You could help us improve this page by suggesting one.

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
Scrapy
ReefAPI
98% 98%
Web Scraping
2% 2%
0% 0%
API Tools
100% 100%
98% 98%
Data Extraction
2% 2%
100% 100%
Data
0% 0%

Questions & Answers

As answered by people managing Scrapy and ReefAPI.

Which are the primary technologies used for building your product?

ReefAPI's answer:

The gateway is Python and FastAPI, running in Docker with PostgreSQL and Redis behind it. The public API is plain HTTP and JSON, described by an OpenAPI 3.0 spec, so any HTTP client works and no SDK is required.

On top of that there is an MCP server so AI assistants can call the catalogue directly. The website and playground are built with Next.js.

What makes your product unique?

ReefAPI's answer:

ReefAPI is one HTTP API over 185+ live web data sources rather than a toolkit for building your own scrapers. Every source is reached with the same request shape and returns the same JSON envelope, so adding a new source is a parameter change, not a new integration.

One API key and one shared credit pool cover all of them, so there is no separate contract, key or invoice per source. Credits do not expire, and calls that fail or return no data are not charged.

There is also an MCP endpoint, so AI assistants and agents can call the same 185+ sources directly without any glue code.

Why should a person choose your product over its competitors?

ReefAPI's answer:

Most alternatives sell you either a generic fetch endpoint that hands back raw HTML, or a single-site API you have to buy one source at a time. ReefAPI sits between the two: the sources are already parsed into consistent fields, and all 185+ of them come with one key and one balance.

Practical differences:

  • Coverage breadth. Search engines, social platforms, e-commerce, real estate, job boards, travel, news, finance and company data are all in the same catalogue.
  • Normalised output. A price, a listing or a review has the same shape whichever site it came from, so downstream code does not fork per source.
  • Billing that matches usage. Credits never expire, lighter calls cost fewer credits than heavier ones, and failed calls cost nothing.
  • MCP support, so AI agents can use the same catalogue without a custom wrapper.

If you only ever need one website, a single-site API may be cheaper. ReefAPI pays off when the list of sources is going to grow.

How would you describe the primary audience of your product?

ReefAPI's answer:

Developers and small product teams who need live web data as an input to something else: price and catalogue monitoring, market and competitor research, lead and company enrichment, real-estate and job-market analysis, and AI agents or RAG pipelines that need current data rather than training-set memory.

The common thread is that data collection is not their product. They want the data as JSON and would rather not run and maintain per-site collection code themselves.

What's the story behind your product?

ReefAPI's answer:

ReefAPI started in early 2026 out of a repeated problem: every new project needed data from another website, and each one meant writing, hosting and then repairing a separate collector. The work was never the interesting part, and it broke every time a site changed.

The idea was to do that work once per source and expose it as a normal HTTP endpoint. One key, one credit balance, one response shape, and a catalogue that grows over time. It launched in February 2026 and now covers 185+ live sources across 14 or so industries.

User comments

Share your experience with using Scrapy and ReefAPI. For example, how are they different and which one is better?

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Reviews and articles

External articles and on-site reviews we used to compare the two products.

Scrapy no reviews yet
ReefAPI no reviews yet
  • Top 15 Best TinyTask Alternatives in 2022
    www.dashtech.org · Jul 2022

    The software is simply deployable via the cloud, or you can host the spiders on your server using Scrapy. Only the rules need to be written; Scrapy will take care of the rest to separate the facts. With Scrapy’s...

We have no reviews of ReefAPI yet. Be the first one to post

Social recommendations and mentions

Recommendations tracked on public social media and blogs since March 2021.

Scrapy 102 mentions
ReefAPI 0 mentions
  • uv Python cheatsheet: what changed in 0.12 and what still trips you up
    Scrapy runs its continuous integration on uv today. The workflow pins astral-sh/setup-uv, drives the test matrix through uvx --with tox-uv tox, and sets UV_PYTHON_PREFERENCE: only-system with a comment explaining why, which is to make uv... - Source: dev.to / 28 days ago
  • Why everyone is talking about loop-engineering and how is it changing agentic ai workflows? Claude Code and Web Scraping examples
    Think about what a mature scraping project already contains. There is a schema that every item must validate against. There are field coverage thresholds, because a run where only 60% of products have prices is a failed run no matter... - Source: dev.to / 4 months ago
  • How to write and publish a Python package to PyPI
    This guide walks through the full process using uv, a fast, modern Python toolchain that replaces pip, virtualenv, pip-tools, twine, and build with a single tool. We will write a reusable Scrapy download handler, structure it as a proper... - Source: dev.to / 5 months ago

View more

Tracking ReefAPI since Jun 2026.

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