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Top 7 Differences of Web Scraping vs API in 2024

Updated on Jan 2
7 min read
Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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The two common methods to get data from the web are application programming interfaces (APIs) and web scraping. But business users might not know which one suits their data extraction needs the best. 

In this article, we will explain how web scrapers and APIs work and compare them in terms of benefits and technical dependencies. We will also give examples of which one is a better choice for specific business use cases and websites like Amazon, Twitter or Instagram.

What is web scraping?

Web scraping is the process of extracting data from websites or specific web pages. It utilizes web scraping software to access and gather data from websites. Web scraping can be done either manually or with a web scraping tool.

What is API? 

API stands for application programming interface. API is a set of definitions and communication protocols that connects a computer to a person.

What is a web  scraping API?

A web scraping API is a tool that extracts data from URLs using an API call. It builds a connection between a user and web server to allow access and data exchange.

Web Scraping vs. API: How do they work?

Web scraping bots extract all the content, such as text, images or videos from a publicly available web page and store it as a data file (see Figure 1). It is similar to taking a picture of a website and analyzing different elements of the picture. The main actor in this case is the web scraper.

API builds an automated data pipeline between a website and the requester targeting a specific part of the website content. Data can be pulled on an automated schedule or manually on demand. It is similar to a subscription where you automatically get an updated content on regular basis. Both the website and the receiver take active role in APIs.

Figure 1: 7 steps to scraping a website

Which one requires less technical effort?

This depends on whether the website allows APIs or web scrapes as well as whether you build your solution in-house. However, a big difference between APIs and web scraping is the availability of readily available tools. APIs will often require the data requester to build a custom application for the specific data query. On the other hand, there are many external web scraping tools that require no coding. Some of them are free browser extensions that scrape the web page you are at or paid service providers that apply readily available templates to scrape data from your target websites. Let’s go into more detail.

1. Solution availability

  • API technology should be provided by the website you want the data from. If they don’t support an API, then this is not an option in the first place. You can check out the specific website you are interested in or API repositories to learn about the availability of a specific website, whether it is free or paid after a certain limit.
  • Web scraping does not need to be technically supported by the website. A common rule of thumb is, if you find a website through a search engine, it is possible to scrape that website. However, the website should allow their content to be scraped. This is done by the website specifying what can or can not be scraped on their robot.txt file where the data owner either gives or denies permission to data scraping.

2. Stability

  • One strength of APIs is that, since it is an authorized access to data, the requester does not need to worry about being detected as a malicious actor and can expect support from the websites in case the API fails unexpectedly.
  • Web scrapers can be blocked by the websites because they bring additional traffic to the website. Web scrapers change their origin of request with the technology called dynamic proxies to overcome this challenge.

3. Access to data

  • Even if an API is available, not all the data may be available to the API. The scope and the granularity of the data you can pull will be specified in the API documentation by the website. For example, LinkedIn provides a limited API for pulling only the basic information of people’s profiles and you need to justify your use case if you want to access the full profile information.
  • Technically, all the content on a publicly available web page can be scraped. However, the scraper should respect the data limitations that the website specified in their terms and conditions. For example, a web scraper can pull any information that you see on a person’s public LinkedIn profile.

4. Technical difficulty

  • APIs will need you to build a custom code including your access keys and specifying the data you need. Websites will often provide an API guide but even this will require a basic understanding of a data query code, such as using a code notebook to run the query, understanding API response codes or specifying parameters to access the needed data. This effort can be outsourced to a developer, but it is not common to use an external tool for APIs to extract data from various platforms.
  • Building a web scraper from scratch also requires coding skills but compared to API, there are more readily available tools that you can scrape the data without any coding. This is often because websites often have similar foundational structures that web scrapers can recognize, because websites need to be scraped by search engines to get ranked in searches. This makes web scraping a reapplied practice for similar websites or the same website across multiple requesters.

5. Cost

  • APIs can be free or paid depending on how the data the website offers can be used commercially. If it is the API for a service you already pay for, such as an analytics service, it is likely that the API will be free of charge. However, even free APIs may charge beyond a certain data limitation in order to control the volume of requests. For example, Google Maps API starts for free but if you plan to host thousands of customer queries on your platform based on the map data, you will need to pay a variable amount based on your volume.
  • Web scraping can be free if you built a solution in-house or leverage an open source solution, such as a browser extension. However, if you leverage an external provider, you will have a variable cost or sign up for a subscription plan. Many web scraping solutions offer a free trial or dataset sample for businesses to assess the ROI of such an investment.

6. Data Cleaning

  • API query outputs can be very complicated and you will often need to parse the data that you need. However, if the API supports more granularity, you may be able to target the specific data point you need and minimize further data processing.
  • Web scraping provides the entire content on a web page. If you need only a specific part of the web page, such as the price of a product page, then you will need to apply a rigorous data parsing to filter the data you need. It is an exhaustive task to do in-house, but external web scrapers often provide the data processed and readily available for analysis.
  • APIs are provided by the website you need data from. Therefore, as long as you follow their API guideline and do not share your API access with any other party, pulling data via API is fully legal.
  • Web scraping is legal as long as the scraper follows the website’s terms and conditions specified in their robot.txt file. If businesses leverage an in-house soltuion, they should make sure checking this step or leverage an external service provider to benefit from their experience. Check out our detailed post about the legal and ethical aspects of web scraping.

Recommendation on when to use which solution

Use APIs

  • If you need data from a service that you work together with and they support API for the data you need, then you may be able to get technical support to build an API data pipeline.
  • If you need to get data from a page that is not publicly available, such as your analytics data for a paid analytics solution that is available only to you, then API will often be the only solution.

Use web scrapers

  • If you need data from a popular website such as Amazon or Twitter, which you may save time by using already available web scraper solutions instead of getting API access.
  • If you are not sure about the business value of the data, you may get a sample via free web scraping tools or free trial with web scraping services and evaluate whether you should invest in API or web scraper in the long term.


Amazon offers many services with multiple API connections for each, but we will focus on Amazon Product Advertising API for collecting product information for use cases like dynamic pricing. Amazon provides this API for free, however the requester should register as an Amazon Associate, which often requires you to have business on Amazon. Moreover, this API has a quota of pulling 10 products per query, so if you have thousands of products to search for and update them frequently, this can be a serious limitation.

Web scraping could be a better alternative than API for Amazon, given that all the information you need for product listing is already available publicly. This option doesn’t require any registration on Amazon, so any developer or web scraping provider can collect the data for you. The speed of pulling data with web scraping will depend on the solution you use but external web scraper providers are often capable of pulling thousands of pages in an hour.

See our article 3 Ways to Gain a Competitive Edge with Amazon Data for more information on scraping Amazon product pages.


Since Twitter data is commonly used for marketing and research purposes, their API documentation is very intuitive. You can get Twitter developer access for API by justifying your case even if you are not a Twitter user. Number of tweets you can pull and how old in data you can go depends on Twitter’s most up to date regulations, but currently it is 900 tweets in 15 minutes and it is possible to pull all available tweets since 2006. However, according to a research from 2020, web scraping is still more time efficient than leveraging an API. Therefore, we recommend you to consider whether you need to pull big amounts of data and very frequently, and then consider web scraper as a solution that may worth the cost.


Instagram contains more images than text, but the information such as the name of the account holders, hashtags and captions are still valuable for various marketing use cases such as finding new influencers for your brand or conducting sentiment analysis for your brand. Instagram provides a free API but the data provided is limited. For example, you can not pull comments via the API, while it is possible to do so with web scraping.

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This article was drafted by former AIMultiple industry analyst Bengüsu Özcan.

Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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