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What is the tech stack behind Pinterest?9 min read

Pinterest started as a small startup website in 2010 with a few thousand users. Since then, the visual bookmark app has grown into a technology powerhouse with over 400 million monthly users, serving over 200 billion pins across 4 billion cards and beyond.

No small business for a company that has waited nine years for launch to go public and submit an IPO, and has been labeled “the best thing” for some time now. Their 2020 Quarterly Report shows 48% year-over-year revenue growth to $ 1,693 million, showing they are ready to ditch that slogan. Continued interest in Pinterest (no pun intended) increased dramatically during the pandemic, with people turning to the app for inspiration during the lockdown.

A recent e-commerce report highlights the massive increase in online shopping, with e-commerce companies in the second quarter of growth posting an average increase of 140.2%. With the constant growth in online shopping and labeling, coupled with the profitability of a Pinterest-like user base and ss advertising business, now is not the time to shy away from your idea of ​​creating an application like Pinterest. The internet is a big place and there is a lot of room for competition.

How does the Pinterest app work?

The Pinterest app works as a visual bookmarking platform that allows users (pinners) to “pin” (report) their likes and interests while traveling online. It has been described as a social media service designed to discover and save online information in the form of images, videos, and GIFs using pins and maps.

With machine learning pulling the strings, the app can identify and recommend content based on a user’s previous pins – their recorded or recorded content. Algorithms running in the background work to inspire users with new content that they haven’t yet searched for or pinned to their message boards.

While it may seem simple at first glance, behind the scenes it equates to an app like Uber or Facebook in terms of complexity. With Pins in the hundreds of billions and over 4 billion cards and beyond, big data plays a central role in everything the Pinterest app does. To handle this high volume of data, Pinterest engineers created a huge inline dataset for the app. Pinterest has a strong data strategy that hasn’t gone bad yet, with AR driving that strategy.

How much does it cost to create an app like Pinterest?

The cost of a Pinterest-like app will be primarily determined by the scope and complexity of the app and by who / how you choose to build it. You have two options here, depending on the type of app you want to create. If you just want to make a Pinterest clone app with some superficial “basics” it can be done in a matter of days with an engineer using Rails tools or even no code.

It should be noted that this basic app will only allow users to block content and will not scale well if too much data is thrown. If you wanted to create a truly equivalent product, that would require extensive ML and AR capabilities, with potentially tens to hundreds of engineers doing research and development, data management, and more. be required for an application targeting a global user base.

The costs for this type of construction will be determined by the following: What technological resources do you have at your disposal Who you choose to build it and their hourly rate How long will it take to design the user interface and the UX to develop the application platform (s) How long will it take to develop the back-end servers.

The going rate for a software development agency in major U.tech hubs like SF and New York is typically between $ 100 and $ 199 per hour. However, there are many great nearshore options that many U.tech companies and startups are using to keep costs down.

If you have certain technological capabilities but need additional support, increasing IT staff can be a cost-effective solution that provides the same high level of service, if not better. The MasterClass app is a great example of a business effectively using its augmented IT staff to reduce costs. Here’s a look at the Rootstrap app development process currently used by Tony Robbins and Google, in addition to the aforementioned MasterClass.

Pinterest Tech Stack

Since its inception, Pinterest’s tech stack has grown at an extremely rapid pace. The original Pinterest stack consisted of Django and Python, with their web servers taking the form of Node.js and Tornado. Originally, their engineers used Redis and Memcached / Membase to handle caching, while RabbitMQ was set up to handle the application queue. To manage persistent data storage they used MySQL and to manage load balancing and static delivery they used Varnish and HAproxy.

Today, the evolution of Pinterest technologies is evident from the current technology stack put in place by the engineering team. With all of Pinterest’s technological advancements over the past decade, here is the existing technological infrastructure that currently maintains the underlying framework for the app:

Pinterest existing technological infrastructure
Pinterest existing technological infrastructure

Machine Learning (ML)

Pinterest uses machine learning to identify content similar to previous Pins of its users in order to recommend new related content to them. The algorithms behind this help the app deliver inspirational content that users need to search for to add Pins to their boards. Pinterest has developed its Labs feature to address the complex challenges of machine learning and artificial intelligence.

The Labs feature brings together the best engineers, scientists, and researchers from around the world to work on recommendation systems, image recognition, and big data analysis. Taxonomy is a methodological approach used to classify entities and define any hierarchical relationship between them. This methodology can take the form of a knowledge management system used to improve the accuracy of machine learning models in place for user behavior modeling, research, and classification capabilities.

Pinterest has created its own taxonomy-based knowledge management system to provide its analyst with a very reliable and efficient tool for understanding content. This allows them to see how the content is performing and provides insight into emerging trends on the platform.

Artificial Intelligence (AI)

In addition to the Labs feature, Pinterest Lens is a great example of effective use of AI. Running in beta, Lens works as a visual search tool that can be used online or offline. Users can inspire ideas by simply pointing their built-in Pinterest camera at an item or content of their choice.

It uses artificial intelligence to identify objects captured by a user’s pins or smartphone, with the ultimate goal of showcasing suggested products and themes based on their tastes. The Lens function plays an important role in the Pinterest machine and has contributed to hundreds of millions of searches by users, not only on the Pinterest app but also on browser extensions.

Augmented Reality (AR)

With 80 to 85% of Pinterest users being women, tech giants are using it to develop innovative features like its TryOn platform. Pinterest has integrated internal facial AR technology to provide a search filter for the skin tones of its users. However, when using this type of AR, the functionality presents challenges.

One of them teaches this technology to avoid introducing bias, like what we saw with Zoom and Twitter’s algorithmic bias issues last year. Although AR / AI bias cannot be completely eliminated, steps can be taken to reduce it, such as having a multidisciplinary data science team with different points of view. With this approach, all the data selected to train the machine learning model can take into account different aspects of the information to represent a more precise reality.

Computer Vision Technology (CVT)

Machine vision technology is an artificial intelligence application used by Pinterest in an attempt to change the way people shop online. CVT has greatly contributed to the artificial intelligence revolution, which the pandemic has helped accelerate due to the aforementioned surge in e-commerce.

When it comes to advertising, CVT allows the app to effectively target ads that are relevant to its users, based on their organic experiences and the billions of pins thrown on the boards. Pinterest offers hundreds of thousands of ads and CVT guarantees a high level of relevance of ads based on a user’s thinking. This feature has proven to be popular because it has received positive feedback from users of the app.

A lot of this is due to the functionality. manual override like “hide this ad” and “hide this pin”, or just go straight to the point “never show me again”. This intentional feedback loop is put in place on purpose by developers because it allows machine learning to use hundreds of thousands of metrics from organic research and user experiences.

Pinterest Website

The Pinterest website was initially launched in closed beta with its core technology stack in the form of MySQL and Python, with Rackspace Cloud Servers as the host, before moving to Amazon web services. Pinterest co-founder Paul Sciarra shared the following in an interview in 2011 about their original tech stack: “We use heavily modified python + Django at the application level. Tornado and (very selectively ) node.js as web server.

Memcached and membase / redis for object and logicalcaching respectively RabbitMQ as message queue .Nginx, HAproxy and Varnish for static delivery and load balancing Persistent data storage using from MySQL.MrJob on EMR for mapreduce.Speaking at a GOTO 2014 conference, Pinterest’s founding engineer Marty Weiner spoke about the evolution of the site’s technology.

How the company has grown from an engineer and 2 founders , running with 1 small web engine and 1 small MySQL database on Rackspace, to a tech giant helping hundreds of millions of people discover new and existing interests.

Over a decade later, the following technologies are pulling the strings on the Pinterest site:

Pinterest Technologies
Pinterest Technologies

Apps Similar To Pinterest

There have been plenty of attempts to create an app like Pinterest. The hunt package tends to focus on lifestyle areas that Pinterest hasn’t covered, while others are more inclined to directly compete with what Pinterest has to offer. For example, Pinterest wasn’t the only company to attract pandemic customers by leveraging AI.

Amazon is now using AI to meet buyer demands with its product recommendation engine accounting for 35% of the site’s sales, according to McKinsey. Tech startups are also kicking in, with AR-based companies like ModiFace, Syte, and Edited providing their customers with features to virtually try on products like lipstick and spot trending fashion. Below is a breakdown of the technology used by some of the most popular Pinterestlike apps on the market:

Apps like Pinterest
Apps like Pinterest