Pinterest Works with AWS to Power Next Chapter of AI-Driven Visual Search Discovery

Key Terms

cloud-native architecture technical

Cloud-native architecture is a way of building and running software so applications are made from small, independent pieces that run in the cloud rather than on a single physical server—think of it like a set of LEGO blocks that can be rearranged, repaired, or scaled on demand. For investors, this matters because it usually enables faster product updates, lower infrastructure costs, and better ability to handle spikes in demand, which can improve a company’s growth prospects, reliability, and profit margins.

data lake technical

A data lake is a large, centralized storage system that holds raw digital information in its original form — documents, spreadsheets, sensor logs, images, and more — rather than forcing everything into a neat structure first. For investors, a well-run data lake is like a company’s research library: it can speed product development, improve forecasting and risk detection, and support better decisions, but it also requires disciplined management to avoid becoming disorganized and costly.

multimodal models technical

Multimodal models are AI systems that can understand and generate more than one type of information—such as text, images, audio and video—so a single tool can read documents, interpret pictures and listen to speech. They matter to investors because they can power new products, boost automation and efficiency, shift cost structures and competitive positions, and create technical or regulatory risks; think of them as one employee who can quickly handle tasks that used to require many specialists.

vision-language models technical

Vision-language models are artificial intelligence systems that connect images and text, allowing a computer to describe pictures, answer questions about images, or generate captions much like a bilingual translator converts between two languages. They matter to investors because they enable new products and efficiencies—such as automated image analysis, smarter search, or customer-facing tools—affecting revenue opportunities, development costs, and competitive advantage, while also introducing data, privacy, and regulatory risks.

kubernetes technical

Kubernetes is an open-source system that automates running and managing many pieces of software across groups of computers, like a conductor coordinating musicians so each piece plays at the right time and place. For investors, it matters because companies that use it can deploy updates faster, scale services up or down automatically, and cut infrastructure costs — factors that influence growth, reliability and operating margins.

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$4 billion deal, the largest in Pinterest's history, deepens an over decade-long collaboration through 2031

Pinterest expands use of compute, cloud-native architecture, and Amazon custom silicon, including Graviton and Trainium, to power AI at scale

SAN FRANCISCO--(BUSINESS WIRE)-- Pinterest, Inc. (NYSE: PINS) announced a major expansion of its collaboration with Amazon Web Services (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), its Preferred Cloud Services Provider, including a planned $4 billion commitment for cloud services through 2031. The agreement is the largest infrastructure commitment in Pinterest’s history and is expected to accelerate the company’s AI roadmap, provide a more responsive search and shopping experience, and further modernize the infrastructure powering Pinterest's global visual search discovery platform.

Pinterest Works with AWS to Power Next Chapter of AI-Driven Visual Search Discovery

"Pinterest is heavily investing in AI to make discovery more personal, visual and actionable for the hundreds of millions of people who use our platform every month," said Matt Madrigal, Chief Technology Officer, Pinterest. "This expanded commitment with AWS gives us the compute flexibility, hardware optionality, and infrastructure efficiency to accelerate our AI vision for the next generation of visual discovery on Pinterest. This strategic partnership will help accelerate AI innovation at Pinterest, improving both our consumer experience and advertiser performance by advancing our proprietary models and our use of open-source models.”

A Partnership Built for Scale

Pinterest and AWS have worked together since 2010 to improve the reliability, efficiency and performance of Pinterest's core services, including jointly optimizing one of the largest-scale data lakes on AWS. This renewed agreement significantly deepens that long-standing relationship and is structured to support Pinterest's next phase of growth across AI model training, inference, and platform infrastructure.

"Pinterest is building some of the most advanced visual AI systems on AWS, powering discovery for more than 600 million users. As one of our longest-standing customers, we know what it takes to support that scale securely and efficiently," said Dave Brown, SVP, Compute & ML Services, AWS. "AWS compute and purpose-built silicon like Trainium and Graviton give Pinterest the price-performance to train and run AI models at massive scale across both training and inference. This commitment provides Pinterest the AI infrastructure to move faster and deliver new experiences to users sooner."

Scaling AI for Visual Discovery

Pinterest has long applied AI to visual discovery and personalization, and in recent years has accelerated this work with major advances in its recommendation systems and multimodal models. Powered by its proprietary Taste Graph, Pinterest helps users move from open inspiration to personalized, actionable results. The company has evolved from traditional embedding-based retrieval to transformer-based generative models, while continually adapting open-source AI and enhancing its proprietary vision models. Most recently, Pinterest launched Pinterest Assistant, bringing multi-turn conversational discovery to its visual search and discovery experience, powered by open-source vision-language models optimized for scale.

Hardware Optionality and Accelerated Compute

As part of the expanded AWS agreement, Pinterest plans to diversify its use of accelerated compute to support its growing AI needs while improving price performance – and turning to Amazon custom silicon to do it. This includes leveraging AWS Trainium to host and run large language models and vision-language models that power experiences like personalized visual search and AI-assisted discovery. In addition, Pinterest plans to expand its use of Graviton, which already powers roughly a third of the company’s compute infrastructure, to run more of the systems that support discovery for more than 600 million people every month. Together, these investments are expected to give Pinterest greater flexibility to match infrastructure to evolving AI needs.

Modernizing Pinterest's Compute Platform

Pinterest will also continue a major infrastructure modernization effort under the agreement, transitioning from traditional EC2-based environments to a Kubernetes-based architecture on Amazon Elastic Kubernetes Service (EKS). The migration is expected to improve developer velocity, operational reliability and infrastructure efficiency across Pinterest's global platform.

These investments are intended to strengthen and refine Pinterest’s AI infrastructure foundation and support its continued innovation in AI-powered visual search and discovery.

About Pinterest

Pinterest is a visual search and discovery platform where people find inspiration, curate ideas and shop products — all in a positive place online. Headquartered in San Francisco, Pinterest has over 600 million monthly active users worldwide.

View source version on businesswire.com: https://www.businesswire.com/news/home/20260604200750/en/

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Source: Pinterest, Inc.