Innovating with MongoDB | Customer Successes, December 2025

Hello, hello, (almost) happy holidays! As we close out the year and look ahead to 2026, I've noticed a common theme in the conversations I’ve had with customers lately. Both enterprises and fast-growing startups are actively transitioning away from legacy relational databases, including PostgreSQL, to meet the rapidly changing requirements of today's fast-paced, AI-driven world.

The growing focus on AI demands continuous software adaptation, but organizations relying on rigid systems are finding themselves stifled. Traditional relational databases were built around "structure," which directly opposes the flexibility that modern applications—especially those leveraging AI—require. MongoDB Atlas is built for change. Our document model and integrated data capabilities provide the cornerstone for companies that need to break free from these outdated constraints.

In this issue of Innovating with MongoDB (check out October here), we're highlighting four industry leaders who have boldly transformed their entire technology stack to capitalize on scale, agility, and AI innovation. 

You’ll learn how Factory consolidated its stack to power agent-native AI processing billions of tokens, and how McKesson scaled transaction volume 300x to ensure pharmaceutical supply chain safety. We also see how Sentra leveraged Atlas’s flexible document model to slash security query times from minutes to seconds, and how 99Minutos cut costs by 50% while managing hyper-growth logistics across Latin America.

Dive into the stories below to see why migration isn't just about changing databases—it's about changing your trajectory!

Factory

Enterprise AI development platform Factory needed a database that could support the unique requirements of agent-native development. Factory's initial setup—a combination of Firebase, PostgreSQL, and S3—could not provide the vector search capabilities, scalability, and consolidated functionality needed to handle processing billions of tokens daily. 

To optimize performance and developer efficiency, Factory migrated to MongoDB Atlas. This switch enabled the fast-growing AI startup to consolidate its data into a single, robust database, capable of supporting exponential growth and scale. The platform is bolstered by Voyage AI's embedding models, which deliver superior performance in code-retrieval benchmarks.

This strategic move eliminated juggling multiple disconnected systems, freeing teams to focus on innovation. This unified foundation ensures Factory can scale seamlessly to support hundreds of thousands of developers while delivering a more competitive, agile, and high-performance product.

McKesson

To comply with the U.S. Drug Supply Chain Security Act (DSCSA), McKesson—the largest drug distributor in the United States—needed a database that could trace 1.2+ billion serial numbers annually across the supply chain by 2025.

To meet the challenges of scaling, McKesson determined it needed to move beyond its initial SAP and PostgreSQL database approach, which struggled with flexibility and performance.

The company chose MongoDB for its Central Data Repository (CDR) and Distributed Serial Repository (DSR) solution. The flexible document model, a natural fit for hierarchical supply chain data, delivered faster queries, simplified development, and eased McKesson’s migration to the cloud. 

The entire migration and launch was completed in under six months. Critically, the new MongoDB-based solution enabled McKesson to scale transaction volume by 300x without disruption, ensuring mission-critical reliability, full DSCSA compliance, and supporting the ultimate goal of patient safety.

Sentra

Data security platform Sentra needed to escape the rigid architecture of its PostgreSQL database to handle rapidly increasing data volumes. The previous solution lacked the elasticity to scale efficiently and accelerate development in a fast-changing landscape. To overcome these constraints, Sentra migrated to a resilient, cloud-native architecture on MongoDB Atlas.

The shift involved a strategic rebuild to embrace the flexible document model. Partnering with MongoDB Professional Services for optimization, they shrank search indexes by 70% and drastically boosted query speed. Queries that took three minutes in PostgreSQL now return in one second, vastly improving the customer experience. Sentra also utilizes MongoDB Atlas Search to power complex filters and aggregations, empowering enterprises to surface and protect sensitive data.

As a result, Sentra scaled from 20 million to over 1 billion assets on MongoDB Atlas. This new foundation maintains top speed and reliability while scaling massively, allowing customers to receive precise, real-time insights and alerts for threat detection and compliance.

99Minutos

99minutos, a fast-growing Latin American logistics business that specializes in last-mile delivery, initially launched with PostgreSQL on AWS. When faced with rapid expansion— 7,500% in less than 2 years—99minutos quickly realized they were in need of a new, modern database solution. Their outdated relational database lacked the adaptability required to unify developments across varied geographical regions and struggled to handle massive traffic spikes.

To shift their teams' focus squarely onto growth rather than daily operational tasks, 99minutos migrated their database infrastructure to MongoDB Atlas on Google Cloud. This successful transition to a modern, agile microservices architecture immediately delivered significant business impact by cutting database-related costs by over 50% and achieving over 99.9% uptime. Furthermore, the new solution is designed for massive scaling, supporting a 300% increase in demand. 

By simplifying their infrastructure and dramatically improving efficiency, 99minutos gained a reliable platform that allows them to keep their focus on market expansion.

Video spotlight: 

Before you go, watch McKesson share their success story on the keynote stage at our 2025 .local NYC event