Top AI Announcements at MongoDB.local NYC

The AI landscape is evolving so quickly that it’s no surprise customers are overwhelmed by their choices. Between foundation models for everything from text to code, AI frameworks, and the steady stream of AI-related companies being founded daily, developers and organizations face a dizzying array of AI choices.

MongoDB empowers customers through a developer data platform that helps them avoid vendor lock-in from cloud providers or AI vendors in this fast-moving space. This freedom allows customers to choose the large language model (LLM) that best suits their needs - now or in the future, whether it's open source or proprietary. Today at MongoDB.local NYC, we announced many new product capabilities, partner integrations, services, and solution offering that enable development teams to get started and build customer-facing solutions with AI.

This post is also available in: Deutsch, Français, Español, Português, Italiano, 한국어, 简体中文.

Run everywhere, with whatever technology you are using in your AI stack

MongoDB’s flexible document model is built on the ethos of “data that is accessed and used together is stored together.” Vectors are a natural extension of this capability, meaning customers can store their source data, metadata, and related vector embeddings in the same document. All of this is accessed and queried with a common Query API, making vector data easy to combine and work with other types of data stored within MongoDB.

MongoDB Atlas—our fully managed, multi-cloud developer data platform—makes it easy to build AI-powered applications and experiences, with the breadth and depth of MongoDB’s AI partnerships and integrations—no matter which language, application framework, foundation model, or technology partner is used or preferred by developers.

This year, we’re continuing to focus on our AI partnerships and integrations to make it easier for developers to build innovative applications with generative AI, including:

MongoDB integrates well with a rich ecosystem of AI developer frameworks, LLMs, and embedding providers. We continue investing in making the entire AI stack work seamlessly, enabling developers to take advantage of generative AI capabilities in their applications easily. MongoDB’s integrations and our industry-leading multi-cloud capabilities allow organizations to move quickly and avoid lock-in to any particular cloud provider or AI technology in a rapidly evolving space.

Build high-performance AI applications securely and at scale

Workload isolation, without data isolation, is critical for building performant, scalable AI applications. Search Nodes in MongoDB Atlas provide dedicated computing and enable users to isolate memory-intensive AI workloads for superior performance and higher availability. Users can optimize resource consumption for their use case, upsizing or downsizing the hardware for that specific node irrespective of the rest of the database cluster. Search Nodes make optimizing performance for vector search queries easy without over or under-provisioning an entire cluster. The IaC integrations with Hashicorp Terraform Atlas Provider and Cloudformation enable developers to configure and programmatically deploy Search Nodes at scale.

Search Nodes are an integral part of Atlas - our fully managed, battle-tested, multi-cloud platform. Previously, we announced the availability of Search Nodes for our AWS and Google Cloud customers. We are excited to announce the preview of Search Nodes for our Azure customers at MongoDB.local NYC. Search Nodes on Atlas helps developers move faster by removing the friction of integrating, securing, and maintaining the essential data components required to build and deploy modern AI applications.

Improve developer productivity with AI-powered experiences

Today, we also announced new and improved releases of our intelligent developer experiences in MongoDB Compass, MongoDB Relational Migrator, and MongoDB Atlas Charts, aiming to enhance developer productivity and velocity. With the updated releases, developers can use natural language to query their data using MongoDB Compass, troubleshoot common problems during development, perform SQL-to-Query API conversion right from within MongoDB Relational Migrator, and quickly build charts and dashboards using natural language prompts in MongoDB Atlas Charts.

Collectively, these intelligent experiences will help developers build differentiated features with greater control and flexibility, making it easier than ever to build applications with MongoDB.

Enable development teams to get started and build customer-facing solutions faster and easier with AI

MongoDB makes it easy for companies of all sizes to build AI-powered applications. To provide customers with a straightforward way to get started with generative AI, MongoDB is announcing the MongoDB AI Application Program (MAAP). Based on usage patterns for common AI use cases, customers receive a functioning application built on a reference architecture backed by MongoDB Atlas, vetted AI models and hosting solutions, technical support, and a full-service engagement led by our Professional Services team.

We’re launching with an incredible group of industry-leading partners, including Anthropic, Anyscale, AWS, Cohere, Credal.ai, Fireworks.ai, Google Cloud, gravity9, LangChain, LlamaIndex, Microsoft Azure, Nomic, PeerIslands, Pureinsights, and Together AI. MongoDB is in a unique position in the market to be able to pull together such an impressive AI partner ecosystem in a single customer-focused program, and we’re excited to see how MAAP will help customers more easily go from ideation to fully functioning generative AI applications.

Last year, to further enable startups to build AI solutions with MongoDB Atlas, we launched the AI Innovators Program, an extension of MongoDB for Startups, which offers an additional $5000 in Atlas credits to our AI startups. This year, we are expanding the program by introducing an AI Startup Hub, which features a curated guide for getting started with MongoDB and AI, quickstarts for MongoDB and select AI partners, and startup credit offerings from our AI partners.

We provide two new AI Accelerator consulting packages for larger enterprise companies: AI Essentials and AI Implementation. While MAAP is aimed exclusively at building highly vetted reference architectures, these consulting packages allow customers to design, build, and deploy open-ended AI prototypes and solutions into their applications.

Data has always been a competitive advantage for organizations, and MongoDB makes it easy, fast, and flexible to innovate with data. We continue to invest in making all the other parts of the AI stack easy for organizations: vetting top partners to ensure compatibility with different parts of the application stack, building a managed service that spans multiple clouds in operation, and ensuring the openness that's always been a part of MongoDB which avoids vendor lock-in.

How does MongoDB Atlas unify operational, analytical, and generative AI data services to streamline building AI-enriched applications? Check out our MongoDB for AI page to learn more.

← Previous

MongoDB Introduces Workload Identity Federation for Database Access

MongoDB Atlas customers run workloads (applications) inside AWS, Azure, and Google Cloud. Today, to enable these workloads to authenticate with MongoDB Atlas cluster—customers create and manage MongoDB Atlas database users using the natively supported SCRAM (password) and X.509 authentication mechanisms and configure them in their workloads. Customers have to manage the full identity lifecycle of these users in their applications, including frequently rotating secrets. To meet their evolving security and compliance requirements, our enterprise customers require database users to be managed within their existing identity providers or cloud providers of their choice. Workload Identity Federation will be in general availability later this month and allows management of MongoDB Atlas database users with Azure Managed Identities, Azure Service Principals, Google Service Accounts, or an OAuth2.0 compliant authorization service. This approach makes it easier for customers to manage, secure, and audit their MongoDB Atlas database users in their existing identity provider or a cloud provider of their choice and enables them to have "passwordless" access to their MongoDB Atlas databases. Along with Workload Identity Federation, Workforce Identity Federation , which was launched in public preview last year, will be generally available later this month. Workforce Identity Federation allows organizations to configure access to MongoDB clusters for their employees with single sign-on (SSO) using OpenID Connect. Both features complement each other and enable organizations to have complete control of database access for both application users and employees. Workload Identity Federation support will be available in Atlas Dedicated Clusters on MongoDB 7.0 and above, and is supported by Java, C#, Node, and Python drivers. Go driver support will be added soon. Quick steps to get started with Workload Identity Federation: Configure Atlas with your OAuth2.0 compatible workload identity provider such as Azure or Google Cloud. Configure Azure Service Principal or Google Cloud Service Accounts for the Azure or Google Cloud resource where your application runs. Add the configured Azure Service Principal or Google Cloud Service Account as Atlas database users with Federated authentication. Using Python or any supported driver inside your application, authenticate and authorize with your workload identity provider and Atlas clusters. To learn more about Workload Identity Federation, please refer to the documentation . And to learn more about how MongoDB’s robust operational and security controls protect your data, read more about our security features .

May 2, 2024
Next →

Payments Modernization and the Role of the Operational Data Layer

To stay relevant and competitive, payment solution providers must enhance their payment processes to adapt to changing customer expectations, regulatory demands, and advancing technologies. The imperative for modernization is clear: payment systems must become faster, more secure, and seamlessly integrated across platforms. Driven by multiple factors—real-time payments, regulatory shifts like Payment Services Directive 2 (PSD2), heightened customer expectations, the power of open banking, and the disruptive force of fintech startups—the need for payment modernization has never been more pressing. But transformation is not without its challenges. Complex systems, industry reliance on outdated technology, high upgrade costs, and technical debt all pose formidable obstacles. This article will explore modernization approaches and how MongoDB helps smooth transformations. Approaches to modernization As businesses work to modernize their payment systems, they need to overcome the complexities inherent in updating legacy systems. Forward-thinking organizations embrace innovative strategies to streamline their operations, enhance scalability, and facilitate agile responses to evolving market demands. Two such approaches gaining prominence in the realm of payment system modernization are domain-driven design and microservices architecture : Domain-driven design: This approach focuses on a business's core operations to develop scalable and easier-to-manage systems. Domain-driven design ensures that technology serves strategic business goals by aligning system development with business needs. At its core, this approach seeks to break down complex business domains into manageable components, or "domains," each representing a distinct area of business functionality. Microservices architecture: Unlike traditional monolithic architectures, characterized by tightly coupled and interdependent components, a microservices architecture decomposes applications into a collection of loosely coupled services, each of which is responsible for a specific business function or capability. It introduces more flexibility and allows for quicker updates, facilitating agile responses to changing business requirements. Discover how Wells Fargo launched their next-generation card payments by building an operational data store with MongoDB . Modernizing with an operational data layer In the payments modernization process, the significance of an operational data layer (ODL) cannot be overstated. An ODL is an architectural pattern that centrally integrates and organizes siloed enterprise data, making it available to consuming applications. The simplest representation of this pattern looks something like the sample reference architecture below. Figure 1: Operational Data Layer structure An ODL is deployed in front of legacy systems to enable new business initiatives and to meet new requirements that the existing architecture can’t handle—without the difficulty and risk of fully replacing legacy systems. It can reduce the workload on source systems, improve availability, reduce end-user response times, combine data from multiple systems into a single repository, serve as a foundation for re-architecting a monolithic application into a suite of microservices, and more. The ODL becomes a system of innovation, allowing the business to take an iterative approach to digital transformation. Here's why an ODL is considered ideal for payment operations: Unified data management: Payment systems involve handling a vast amount of diverse data, including transaction details, customer information, and regulatory compliance data. An ODL provides a centralized repository for storing and managing this data, eliminating silos and ensuring data integrity. Real-time processing: An ODL enables real-time processing of transactions, allowing businesses to handle high numbers of transactions swiftly and efficiently. This capability is essential for meeting customer expectations for instant payments and facilitating seamless transactions across various channels. Scalability and flexibility: Payment systems must accommodate fluctuating transaction volumes and evolving business needs. An ODL offers scalability and flexibility, allowing businesses to scale their infrastructure as demand grows. Enhanced security: An ODL incorporates robust security features —such as encryption, access controls, and auditing capabilities—to safeguard data integrity and confidentiality. By centralizing security measures within the ODL, businesses can ensure compliance with regulatory requirements and mitigate security risks effectively. Support for payments data monetization: Payment systems generate a wealth of data that can provide valuable insights into customer behavior, transaction trends, and business performance. An ODL facilitates real-time analytics and reporting by providing a unified platform for collecting, storing, and analyzing this data. Transform with MongoDB MongoDB’s fundamental technology principles ensure companies can reap the advantages of microservices and domain-driven design—specifically, our flexible data model and built-in redundancy, automation, and scalability. Indeed, the document model is tailor-made for the intricacies of payment data, ensuring adaptability and scalability as market demands evolve. Here’s how MongoDB helps with domain-driven design and microservice implementation to adopt industry best practices: Ease of use: MongoDB’s document model makes it simple to model or remodel data to fit the needs of payment applications. Documents are a natural way of describing data. They present a single data structure, with related data embedded as sub-documents and arrays, making it simpler and faster for developers to model how data in the application will be mapped to data stored in the database. In addition, MongoDB guarantees the multi-record ACID transactional semantics that developers are familiar with, making it easier to reason about data. Flexibility: MongoDB’s dynamic schema is ideal for handling the requirements of microservices and a domain-driven design. Domain-driven design emphasizes modeling the domain to reflect the business requirements, which may evolve over time. MongoDB's flexible schema allows you to store domain objects as documents without rigid schema constraints, facilitating agile development and evolution of the domain model. Speed: Using MongoDB for an ODL means you can get better performance when accessing data, and write less code to do so. A document is a single place for the database to read and write data for an entity. This locality of data ensures the complete document can be accessed in a single database operation that avoids the need internally to pull data from many different tables and rows. Data access and microservice-based APIs: MongoDB integrates seamlessly with modern technologies and frameworks commonly used in microservices architectures. MongoDB's flexible data model and ability to handle various data types, including structured and unstructured data, is a great fit for orchestrating your open API ecosystem to make data flow between banks, third parties, and consumers possible. Scalability: Even if an ODL starts at a small scale, you need to be prepared for growth as new source systems are integrated, adding data volume, and new consuming systems are developed, increasing workload. MongoDB provides horizontal scale-out on low-cost, commodity hardware or cloud infrastructure using sharding to meet the needs of an ODL with large data sets and high throughput requirements. High availability: Microservices architectures require high availability to ensure that individual services remain accessible even in the event of failures. MongoDB provides built-in replication and failover capabilities, ensuring data availability and minimal downtime in case of server failures. Payment modernization is not merely a trend but a strategic imperative. By embracing modern payment solutions and leveraging the power of an ODL with MongoDB, organizations can unlock new growth opportunities, enhance operational efficiency, and deliver superior customer experiences. Learn how to build an operational data layer with MongoDB using this Payments Modernization Solution Accelerator . Learn more about how MongoDB is powering industries on our solution library .

May 15, 2024