MongoDB Embraces AI & Reduces Developer Friction With New Features

Even if you haven’t heard of MongoDB, odds are good that you touch it in your daily online life. MongoDB has enabled more than 43,000 organizations to build solutions leveraging MongoDB technology, including some of the biggest names in technology, healthcare, telecom, and financial services. The company’s horizontal document-oriented (often called NoSQL) database technology underpins a broad swath of workloads that all need modern data services – needs that often don’t directly map to the constraints of traditional relational databases.

Servicing the quickly evolving needs of modern application development requires rapid innovation and fast product cycles. MongoDB demonstrated both last week at its MongoDB.local 2023 event in New York City, introducing a compelling set of new features and services.

The announcements cover a wide breadth of territory, with new capabilities to leverage the latest AI technology, features that enable greater developer productivity, ease the burden of enterprise application development, and even a new program to simplify deploying MongoDB technology into a targeted set of verticals. There’s a lot to delve into.

Enabling AI

It’s impossible to talk about application development today without touching on artificial intelligence. Generative AI, typified by large language models (LLMs) such as ChapGPT, capture headlines daily. The question technology companies and IT practitioners alike most often ask me is how AI will affect them. MongoDB this past week illustrated how generative AI impacts the data plane.

MongDB Atlas Vector Search

Technologies such as generative AI changes how we think about managing the data that feeds AI-driven systems. Language processing, for example, utilize attributes on data called “vectors.”

You can think of vector embeddings as tags placed on data as an AI model that define the relationship between words. These vectors are then used as efficient shortcuts when running generative AI models (this is a simplistic explanation of vectors; interested readers should read this more in-depth explanation).

MongoDB’s new MongoDB Atlas Vector Search is designed to simplify the development of AI language and generative AI applications. The new capability allows vector embedding directly on data stored by MongoDB, allowing new generative AI applications to be quickly and efficiently developed on MongoDB Atlas.

MongoDB Atlas Vector Search is also integrated with the open-source LangChain and LlamaIndex frameworks with tools for accessing and managing LLMs for various applications.

MongoDB AI Innovators Program

Building and deploying applications leveraging the latest in AI technology can be daunting. The concepts, tools, and even infrastructure significantly differ from more traditional software development approaches. AI applications can also require multiple iterations of model training as the application evolves, adding significant development costs.

Last week, recognizing the unique challenges of developing AI applications, MongoDB announced its new MongoDB AI Innovators Program, designed to ease the unique burdens of developing AI applications. The new program offers several benefits, including providing eligible organizations with up to $25,000 in credits for MongoDB Atlas.

The AI Innovators Program also includes engagement opportunities with MongoDB to fast-track strategic partnerships and joint go-to-market activities with what the company calls its AI Amplify track. Companies participating in AI Amplify track have their submissions evaluated by MongoDB to gauge the appropriateness of a potential partnership. MongoDB technical experts are also available for solutions architecture and to help identify compelling use cases to use in co-marketing opportunities.

Finally, MongoDB is making its partner ecosystem available to program participants. Organizations participating in the MongoDB AI Innovators Program will have prioritized access to opportunities with MongoDB Partners, and eligible organizations can be fast-tracked to join the MongoDB Partner Ecosystem to build seamless, interoperable integrations and joint solutions. MongoDB has over 1,000 partners, making this an attractive benefit of the program.

New MongoDB Atlas Capabilities

In addition to the new vector search capabilities already mentioned, there were four additional capabilities introduced into MongoDB Atlas:

• MongoDB Atlas Search Nodes now provide dedicated infrastructure for search use cases so customers can scale independently of their database to manage unpredictable spikes and high-throughput workloads with greater flexibility and operational efficiency.
• MongoDB Atlas Stream Processing transforms building event-driven applications that react and respond in real-time by unifying how developer teams work with data-in-motion and data-at-rest.
• MongoDB Atlas Time Series collections now make time-series workloads more efficient at scale for use cases from predictive maintenance for factory equipment to automotive vehicle-fleet monitoring to financial trading platforms.
• New multi-cloud options for MongoDB Atlas Online Archive and Atlas Data Federation now enable customers to seamlessly tier and query data in Microsoft Azure and in addition to Amazon Web Services.

Keeping with its theme of simplifying the developer experience, these new features should ease the burden of developing applications using MongoDB Atlas as an intelligent data platform.

Reducing Developer Friction

MongoDB is a foundational component for data modernization, but it is only part of the solution. Mongo recognizes this, calling its technology a “Developer Data Platform.” The phrase emphasizes the importance of empowering developers to build next-generation AI-enabled applications, often while also using AI. MongoDB empowers developers by delivering a data plane offering the capabilities most needed for modern applications.

Mongo announced new programming language support to facilitate adoption across multiple environments. The company added support for server-side Kotlin applications (Kotlin is a programming language designed for cross-platform application development). There is also new support for data processing and analytics with Python as MongoDB makes its open-source PyMongoArrow library generally available, allowing developers to efficiently convert data stored in MongoDB using some of the most popular Python-based analytics frameworks.

MongoDB is also adding additional support for deploying and managing MongoDB using Amazon AWS infrastructure-as-code (IaC) capabilities. MongoDB released a new integration with the AWS Cloud Development Kit (CDK), allowing developers to manage MongoDB Atlas resources with C#, Go, Java, and Python. This is a significant enabler for developers deploying on AWS.

MongoDB also simplified its Kubernetes integration with improvements to its MongoDB Atlas Kubernetes Operator. The new functionality allows developers to install MongoDB’s horizontal document-oriented (often called NoSQL) database technology underpins a broad swath of workloads that all need modern data services – needs that often don’t directly map to the constraints of traditional relational databases.

Finally, MongoDB announced its new MongoDB Relational Migrator tool. The new tool makes migrating from traditional legacy databases into a MongoDB environment easier and significantly faster. MongoDB Relational Migrator analyzes legacy databases, automatically generates new data schema and code, and then executes a seamless migration to MongoDB Atlas without downtime. This capability will reduce the pain often experienced when moving data into a new environment from a legacy data store.

Analyst’s Take

MongoDB held an investor conference parallel to its developer-focused MongoDB.local event. At the investor event, MongoDB’s chief product officer, Sahir Azam, described how the company builds its product strategy and GTM activities around its understanding of the customer’s journey.

The features, and new business opportunities, announced by MongoDB make sense to anyone familiar with the development of a modern data-driven application. The new offerings help developers leverage MongoDB technology to create new applications while also implementing the features required to develop next-generation AI-enabled solutions.

There’s no question that developers appreciate what the company is delivering. As an enabling technology for other applications, MongoDB’s approach not only makes sense, it’s also necessary. It’s also paying off.

MongoDB has beaten consensus estimates in its earnings for seventeen straight quarters, with its most recent earnings besting EPS estimates by nearly 195%. The most recent quarter also saw Mongo growing its top-line revenue by 29% year-over-year. The company has increased revenue by 8x since 2018. That’s a tremendous vote of confidence from its customers, especially in a market that’s still hampering growth for nearly every foundational technology company.

MongoDB competes in a crowded segment, and we see innovation coming from its closest competitors, evidenced by recent announcements from competitors such as Elastic. At the same time, MongoDB stands out in this intensely competitive environment with its relentless focus on improving the experience for developers, quickly adapting to new trends in data analysis and AI, and implementing programs that allow its customers to launch new applications quickly. Seventeen straight earnings beats, over a thousand partners, and more than 43,000 customers all show that MongoDB is earning its success.

Disclosure: Steve McDowell is an industry analyst, and NAND Research an industry analyst firm, that engages in, or has engaged in, research, analysis, and advisory services with many technology companies, which may include those mentioned in this article. Mr. McDowell does not hold any equity positions with any company mentioned in this article.