Product Updates
The most recent MongoDB product releases and updates
Advancing Encryption in MongoDB Atlas
Maintaining a strong security posture and ensuring compliance with regulations and industry standards are core responsibilities of enterprise security teams. However, satisfying these responsibilities is becoming increasingly complex, time-consuming, and high-stakes. The rapid evolution of the threat landscape is a key driver of this challenge. In 2024, the percentage of organizations that experienced a data breach costing $1 million or more jumped from 27% to 36%. 1 This was partly fueled by a 180% surge from 2023 to 2024 in vulnerability exploitation by attackers. 2 Concurrently, regulations are tightening. Laws like the Health Insurance Portability and Accountability Act (HIPAA) 3 and the U.S. Securities and Exchange Commission’s cybersecurity regulations 4 have introduced stricter security requirements. This has raised the bar for compliance. Thousands of enterprises rely on MongoDB Atlas to protect their sensitive data and support compliance efforts. Encryption plays a crucial role on three levels; securing data at rest, in transit, and in use. However, security teams need more than solely strong encryption. Flexibility and control are essential to align with an organization’s specific requirements. MongoDB is introducing significant upgrades to MongoDB Atlas encryption to meet these needs. This includes enhanced customer-managed key (CMK) functionality and support for TLS 1.3. This post explores these improvements, along with the planned deprecation of outdated TLS versions, to strengthen organizations’ security postures. Why customer-managed keys (CMKs) matter Customer-managed keys (CMKs) are a security and data governance feature that delivers enterprises full control over the encryption keys protecting their data. With CMKs, customers can define and manage their encryption strategy. This ensures they have ultimate authority over access to their sensitive information. MongoDB Atlas customer key management provides file-level encryption, similar to transparent data encryption (TDE) in other databases. This customer-managed encryption-at-rest feature works alongside always-on volume-level encryption 5 in MongoDB Atlas. CMKs ensure all database files and backups are encrypted. MongoDB Atlas also integrates with AWS Key Management Service (AWS KMS), Azure Key Vault , and Google Cloud KMS . This ensures customers have the flexibility to manage keys as part of their broader enterprise security strategy. Customers using CMKs retain complete control of their encryption keys. If an organization needs to revoke access to data due to a security concern or any other reason, it can do so immediately by freezing or destroying the encryption keys. This capability acts as a “kill switch,” ensuring sensitive information becomes inaccessible when protection is critical. Similarly, an organization can destroy the keys to render the data and backups permanently unreadable and irretrievable. This may be applicable should they choose to retire a cluster permanently. Announcing CMK over private networking As part of a commitment to deliver secure and flexible solutions for enterprise customers, MongoDB is introducing CMKs over private networking. This enhancement enables organizations to manage their encryption keys without exposing their key management service (KMS) to the public internet. Using CMKs in MongoDB Atlas previously required Azure Key Vault and AWS KMS to be accessible via public IP addresses prior to today. While functional, this posed challenges for customers who need to keep KMS traffic private. It forced those customers to either expose their KMS endpoints or manage IP allow lists. By using private networking, customers can now: Eliminate the need for public IP exposure. Simplify network management by removing the need to manage allowed IP addresses. This reduces administrative effort and misconfiguration risk. Align with organizational requirements that mandate the use of private networking. Customer key management over private networking is now available for Azure Key Vault and AWS KMS . Customers can enable and manage this feature for all their MongoDB Atlas projects through the MongoDB Atlas UI or the MongoDB Atlas Administration API . More enhancements are coming for MongoDB customer key management in 2025. These include secretless authentication mechanisms and CMKs for search nodes. MongoDB Atlas TLS enhancements advance encryption in transit Securing data in transit is equally vital as a foundation of encryption at rest with CMKs. To address this, MongoDB Atlas enforces TLS by default. This ensures encrypted communication across all aspects of the platform, including client connections. Now MongoDB is reinforcing its TLS implementation with key enhancements for enterprise-grade security. MongoDB is in the process of rolling out fleetwide support for TLS 1.3 in MongoDB Atlas. The latest version of the cryptographic protocol offers several advantages over its predecessors. This includes stronger security defaults, faster handshakes, and reduced latency. Concurrently, TLS versions 1.0 and 1.1 are being deprecated. The rationale for this is known weaknesses and their inability to meet modern security standards. MongoDB is aligning with industry best practices by standardizing on TLS 1.2 and 1.3. This ensures a secure communication environment for all MongoDB Atlas users. Additionally, MongoDB now offers custom cipher suite selection, giving enterprises more control over their cryptographic configurations. This feature lets organizations choose the cipher suites for their TLS connections, ensuring compliance with their security requirements. Achieving encryption everywhere This post covers how MongoDB secures data at rest with CMKs and in transit with TLS. However, what about data in use while it’s being processed in a MongoDB Atlas instance? That’s where Queryable Encryption comes in. This groundbreaking feature enables customers to run expressive queries on encrypted data without ever exposing the plaintext or keys outside the client application. Sensitive data and queries never leave the client unencrypted. This ensures sensitive information is protected and inaccessible to anyone without the keys, including database administrators and MongoDB itself. MongoDB is committed to providing enterprise-grade security that evolves with the changing threat and regulatory landscapes. Organizations now have greater control, flexibility, and protection across every stage of the data lifecycle with enhanced CMK functionality, TLS 1.3 adoption, and custom cipher suite selection. As security challenges grow more complex, MongoDB continues to innovate to enable enterprises to safeguard their most sensitive data. To learn more about these encryption enhancements and how they can strengthen your security posture, visit MongoDB Data Encryption . 1 PwC , October 2024 2 Verizon Data Breach Investigations Report , 2024 3 U.S. Department of Health and Human Services , December 2024 4 U.S. Securities and Exchange Commission , 2023 5 MongoDB Atlas Security White Paper , Encryption at Rest section page 12
Secure by Default: Mandatory MFA in MongoDB Atlas
On March 26, 2025, MongoDB will start rolling out mandatory multi-factor authentication (MFA) for MongoDB Atlas users. While MFA has long been supported in Atlas, it was previously optional. MongoDB is committed to delivering customers the highest level of security, and the introduction of mandatory MFA adds an extra layer of protection against unauthorized access to MongoDB Atlas. Note: MFA will require users to provide a second form of authentication, such as a one-time passcode or biometrics. To ensure a smooth transition, users are encouraged to set up their preferred MFA method in advance. This process should take around three minutes to set up. If MFA is not configured by March 26, 2025, users will need to enter a one-time password (OTP) sent to their registered email each time they log in. Why are we making MFA mandatory? Stealing users’ credentials is a key tactic in the modern cyberattack playbook. According to a Verizon report, stolen credentials have been involved in 31% of data breaches in the past decade, and credential stuffing is the most common attack type for web applications. 1 Credential stuffing is when attackers use stolen credentials obtained from a data breach on one service to attempt to log in to another service. These breaches are particularly harmful, taking an average of 292 days to detect and contain. 2 This rise in cyber threats has rendered password-only security inadequate. Organizations of all sizes trust MongoDB Atlas to safeguard their mission-critical applications and sensitive data. These range from global enterprises to individual developers. Therefore, to strengthen account security and to reduce the risk of unauthorized access, MongoDB is introducing mandatory MFA. The impact of MFA A large-scale study by Microsoft measured the effectiveness of MFA to prevent cyberattacks on enterprise accounts. The findings indicated enabling MFA reduces the risk of account compromise by 99.22%. For accounts with previously leaked credentials, MFA still lowered the risk by 98.56%. This makes MFA one of the most effective defenses against unauthorized access. By default, requiring MFA strengthens the security of all MongoDB Atlas accounts. By reducing the risk of compromised accounts being used in broader attacks, this proactive step protects individual users and enhances MongoDB Atlas’s overall security. Ensuring strong authentication practices across the Atlas ecosystem maintains the integrity of mission-critical applications and sensitive data— and a safer experience for everyone is the result. Preparing for mandatory MFA MFA will be a prerequisite for all users when logging into MongoDB services using Atlas credentials. These services include: MongoDB Atlas user interface MongoDB Support portal MongoDB University MongoDB Forums Atlas supports the following MFA methods: Security key or biometrics: FIDO2 (WebAuthn) compliant security keys (e.g., YubiKey ) or biometric authentication (e.g., Apple Touch ID or Windows Hello) One-time password (OTP) and push notifications: Provided through the Okta Verify app Authenticator apps: Such as Twilio Authy , Google Authenticator , or Microsoft Authenticator for generating time-based OTPs Email: For generating OTPs MongoDB encourages users to choose phishing-resistant MFA methods, such as security keys or biometrics. Strengthening security with mandatory MFA Requiring MFA is a significant step that enhances MongoDB Atlas’s default security. Multi-factor authentication protects users from credential-based attacks and unauthorized access. Making MFA’s additional layer of authentication mandatory ensures greater account security. This safeguards mission-critical applications and data. To ensure a smooth transition, users are encouraged to set up their preferred MFA method before March 26, 2025. For detailed setup instructions, refer to the MongoDB documentation . And, please visit the MongoDB security webpage and Trust Center to learn more about MongoDB’s commitment to security.
Reintroducing the Versioned MongoDB Atlas Administration API
Our MongoDB Atlas Administration API has gotten some work done in the last couple of years to become the best “Versioned” of itself. In this blog post, we’ll go over what’s changed and why migrating to the newest version can help you have a seamless experience managing MongoDB Atlas . What does the MongoDB Atlas Administration API do? MongoDB Atlas, MongoDB’s managed developer data platform, contains a range of tools and capabilities that enable developers to build their applications’ data infrastructure with confidence. As application requirements and developer teams grow, MongoDB Atlas users might want to further automate database operation management to scale their application development cycles and enhance the developer experience. The entry point to managing MongoDB Atlas in a more programmatic fashion is the legacy MongoDB Atlas Administration API. This API enables developers to manage their use of MongoDB Atlas at a control plane level. The API and its various endpoints enable developers to interact with different MongoDB Atlas resources—such as clusters, database users, or backups—and lets them perform operational tasks like creating, modifying, and deleting those resources. Additionally, the Atlas Administration API supports the MongoDB Atlas Go SDK , which empowers developers to seamlessly interact with the full range of MongoDB Atlas features and capabilities using the Go programming language. Why should I migrate to the Versioned Atlas Administration API? While it serves the same purpose as the legacy version, the new Versioned Atlas Administration API gives a significantly better overall experience in accessing MongoDB Atlas programmatically. Here’s what you can expect when you move over to the versioned API. A better developer experience The Versioned Atlas Administration API provides a predictable and consistent experience with API changes and gives better visibility into new features and changes via the Atlas Administration API changelog . This means that breaking changes that can impact your code will only be introduced in a new resource version and will not affect the production code running the current, stable version. Also, every time a new version two resource is added, you will be notified of the older version being deprecated, giving you at least one year to upgrade before the removal of the previous resource version. As an added benefit, the Versioned Atlas Administration API supports Service Accounts as a new way to authenticate to MongoDB Atlas using the industry standard OAuth2.0 protocol with the Client Credentials flow. Minimal workflow disruptions With resource-level versioning, the Versioned Atlas Administration API provides specific resource versions, which are represented by dates. When migrating from the legacy, unversioned MongoDB Atlas Administration API (/v1) to the new Versioned Atlas Administration API (/v2), the API will default to resource version 2023-02-01. To simplify the initial migration, this resource version applies uniformly to all API resources (e.g., /backup or /clusters). This helps ensure that migrations do not adversely affect current MongoDB Atlas Administration API–based workloads. In the future, each resource can adopt a new version independently (e.g., /cluster might update to 2026-01-01 while /backup remains on 2023-02-01). This flexibility ensures you only need to act when a resource you use is deprecated. Improved context and visibility Our updated documentation provides detailed guidance on the versioning process. All changes—including the release of new endpoints, the deprecation of resource versions, or nonbreaking updates to #stable resources—are now tracked in a dedicated, automatically updated changelog. Additionally, the API specification offers enhanced visibility and context for all stable and deprecated resource versions, ensuring you can easily access documentation relevant to your specific use case. Why should I migrate to the new Go SDK? In addition to an updated API experience, we’ve introduced version 2 of the MongoDB Atlas Go SDK for the MongoDB Atlas Administration API. This version supports a range of capabilities that streamline your experience when using the Versioned Atlas Administration API: Full endpoint coverage: MongoDB Atlas Go SDK version 2 enables you to access all the features and capabilities that the versioned API offers today with full endpoint coverage so that you can programmatically use MongoDB Atlas in full. Flexibility: When interacting with the new versioned API through the new Go SDK you can choose which version of the MongoDB Administration API you want to work with, giving you control over when breaking changes impact you. Ease of use: The new Go SDK enables you to simplify getting started with the MongoDB Atlas Administration API. You’ll be able to work with fewer lines of code and prebuilt functions, structs, and methods that encapsulate the complexity of HTTP requests, authentication, error handling, versioning, and other low-level details. Immediate access to updates: When using the new Go SDK, you can immediately access any newly released API capabilities. Every time a new version of MongoDB Atlas is released, the SDK will be quickly updated and continuously maintained, ensuring compatibility with any changes in the API and speeding up your development process. How can I experience the enhanced version? To get started using the Versioned Atlas Administration API, you can visit the migration guide , which outlines how you can transition over from the legacy version. To learn more about the MongoDB Atlas Administration API, you can visit our documentation page .
Simplify Security At Scale with Resource Policies in MongoDB Atlas
Innovation is the gift that keeps on giving: industries that are more innovative have higher returns, and more innovative industries see higher rates of long-term growth 1 . No wonder organizations everywhere strive to innovate. But in the pursuit of innovation, organizations can struggle to balance the need for speed and agility with critical security and compliance requirements. Specifically, software developers need the freedom to rapidly provision resources and build applications. But manual approval processes, inconsistent configurations, and security errors can slow progress and create unnecessary risks. Friction that slows down employees and leads to insecure behavior is a significant driver of insider risk. Paul Furtado Vice President, Analyst, Gartner Enter resource policies , which are now available in public preview in MongoDB Atlas. This new feature balances rapid innovation with robust security and compliance. Resource policies allow organizations to enable developers with self-service access to Atlas resources while maintaining security through automated, organization-wide ‘guardrails’. What are resource policies? Resource policies help organizations enforce security and compliance standards across their entire Atlas environment. These policies act as guardrails by creating organization-wide rules that control how Atlas can be configured. Instead of targeting specific user groups, resource policies apply to all users in an organization, and focus on governing a particular resource. Consider this example: An organization subject to General Data Protection Regulation (GDPR) 2 requirements needs to ensure that all of their Atlas clusters run only on approved cloud providers in regions that comply with data residency and privacy regulations. Without resource policies, developers may inadvertently deploy clusters on any cloud provider. This risks non-compliance and potential fines of up to 20 million euros or 4% of global annual turnover according to article 83 of the GDPR. But, by using resource policies, the organization can mandate which cloud providers are permitted, ensuring that data resides only in approved environments. The policy is automatically applied to every project in the organization, preventing the creation of clusters on unauthorized cloud platforms. Thus compliance with GDPR is maintained. The following resource policies are now in public preview: Restrict cloud provider: Limit Atlas clusters to approved cloud providers (AWS, Azure, Google Cloud). Restrict cloud region: Restrict cluster deployments in approved cloud providers to specific regions. Block wildcard IP: Reduce security risk by disabling the use of 0.0.0.0/0 (or “wildcard”) IP address for cluster access. How resource policies enable secure self-service Atlas access Resource policies address the challenges organizations face when trying to balance developer agility with robust security and compliance. Without standardized controls, there is a risk that developers will configure Atlas clusters to deviate from corporate or external requirements. This invites security vulnerabilities and compliance gaps. Manual approval and provisioning processes for every new project creates delays. Concurrently, platform teams struggle to enforce consistent standards across an organization, increasing operational complexity and costs. With resource policies, security and compliance standards are automatically enforced across all Atlas projects. This eliminates manual approvals and reduces the risk of misconfigurations. Organizations can deliver self-service access to Atlas resources for their developers. This allows them to focus on building applications instead of navigating complex internal review and compliance processes. Meanwhile, platform teams can manage policies centrally. This ensures consistent configurations across the organization and frees time for strategic initiatives. The result is a robust security posture, accelerated innovation, and greater efficiency. Automated guardrails prevent unauthorized configurations. Concurrently, centralized policy management streamlines operations and ensures alignment with corporate and external standards. Resource policies enable organizations to scale securely and innovate without compromise. This empowers developers to move quickly while simplifying governance. iA Financial Group, one of Canada’s largest insurance and wealth management firms, uses resource policies to ensure consistency and compliance in MongoDB Atlas. “Resource Policies have allowed us to proactively supervise Atlas’s usage by our IT delivery teams,” said Geoffrey Céré, Solution Architecture Advisor at iA Financial Group. “This has been helpful in preventing non-compliant configurations with the company’s regulatory framework. Additionally, it saves our IT delivery teams time by avoiding unauthorized deployments and helps us demonstrate to internal audits that our configurations on the MongoDB Atlas platform adhere to the regulatory framework.” Creating resource policies Atlas resource policies are defined using the open-source Cedar policy language , which combines expressiveness with simplicity. Cedar’s concise syntax makes writing and understanding policies easy, streamlining policy creation and management. Resource policies can be created and managed programmatically through infrastructure-as-code tools like Terraform or CloudFormation, or by integrating directly using the Atlas Admin API. To explore what constructing a resource policy looks like in practice, let’s return to our earlier example. This is an organization subject to GDPR requirements that wants to ensure all of their Atlas clusters run on approved cloud providers only. To prevent users from creating clusters on Google Cloud (GCP), the organization could write the following policy named “ Policy Preventing GCP Clusters .” This policy forbids creating or editing a cluster when the cloud provider is Google Cloud. The body defines the behavior of the policy in the human and machine-readable Cedar language. If required, ‘ gcp ’ could be replaced with ‘ aws ’. Figure 1. Example resource policy preventing the creation of Atlas clusters on GCP. Alternatively, the policy could allow users to create clusters only on Google Cloud with the following policy named “Policy Allowing Only GCP Clusters”. This policy uses the Cedar clause “unless” to restrict creating or editing a cluster unless it is on GCP. Figure 2. Example resource policy that restricts cluster creation to GCP only. Policies can also have compound elements. For example, an organization can create a project-specific policy that only enforces the creation of clusters in GCP for the Project with ID 6217f7fff7957854e2d09179 . Figure 3. Example resource policy that restricts cluster creation to GCP only for a specific project. And, as shown in Figure 4, another policy might restrict cluster deployments on GCP as well as on two unapproved AWS regions: US-EAST-1 and US-WEST-1. Figure 4. Example resource policy restricting cluster deployments on GCP as well as AWS regions US-EAST-1 and US-WEST-1. Getting started with resource policies Resource policies are available now in MongoDB Atlas in public preview. Get started creating and managing resource policies programmatically using infrastructure-as-code tools like Terraform or CloudFormation. Alternatively, integrate directly with the Atlas Admin API. Support for managing resource policies in the Atlas user interface is expected by mid-2025. Use the resources below to learn more about resource policies. Feature documentation Postman Collection Atlas Administration API documentation Terraform Provider documentation AWS CDK AWS Cloud Formation documentation 1 McKinsey & Company , August 2024 2 gdpr.eu
Dynamic Workloads, Predictable Costs: The MongoDB Atlas Flex Tier
MongoDB is excited to announce the launch of the Atlas Flex tier . This new offering is designed to help developers and teams navigate the complexities of variable workloads while growing their apps. Modern development environments demand database solutions that can dynamically scale without surprise costs, and the Atlas Flex tier is an ideal option offering elasticity and predictable pricing. Previously, developers could either pick the predictable pricing of a shared tier cluster or the elasticity of a serverless instance. Atlas Flex tier combines the best features of the Shared and Serverless tiers and replaces them, providing an easier choice for developers. This enables teams to focus on innovation rather than database management. This new tier underscores MongoDB’s commitment to empowering developers through an intuitive and customer-friendly platform. It simplifies cluster provisioning on MongoDB Atlas , providing a unified, simple path from idea to production. With the ever-increasing complexity of application development, it’s imperative that a database evolve alongside the project it supports. Whether prototyping a new app or managing dynamic production environments, MongoDB Atlas provides comprehensive support. And, by seamlessly combining scalability and affordability, the Atlas Flex tier reduces friction as requirements expand. Bridging the gap between flexibility and predictability: What the Atlas Flex tier offers developers Database solutions that can adapt to fluctuating workloads without incurring unexpected costs are becoming a must-have for every organization. While traditional serverless models offer flexibility, they can result in unpredictable expenses due to unoptimized queries or unanticipated traffic surges . The Atlas Flex tier bridges this gap and empowers developers with: Flexibility: 100 ops/sec and 5 GB of storage are included by default, as is dynamic scaling of up to 500 ops/sec. Predictable pricing: Customers will be billed an $8 base fee and additional fees based on usage. And pricing is capped at $30 per month. This prevents runaway costs—a persistent challenge with serverless architectures. Data services: Customers can access various features such as MongoDB Atlas Search , MongoDB Atlas Vector Search , Change Streams , MongoDB Atlas Triggers , and more. This delivers a comprehensive solution for development and test environments. Seamless migration: Atlas Flex tier customers can transition to dedicated clusters when needed via the MongoDB Atlas UI or using the Admin API. The Atlas Flex tier marks a significant step forward in streamlining database management and enhancing its adaptability to the needs of modern software development. The Atlas Flex tier provides unmatched flexibility and reliability for managing high-variance traffic and testing new features. Building a unified on-ramp: From exploration to production MongoDB Atlas enables a seamless progression for developers at every stage of application development. With three distinct tiers—Free, Flex, and Dedicated—MongoDB Atlas encourages developers to explore, build, and scale their applications: Atlas Free tier: Perfect for experimenting with MongoDB and building small applications at no initial cost, this tier remains free forever. Atlas Flex tier: Bridging the gap between exploration and production, this tier offers scalable, cost-predictable solutions for growing workloads. Atlas Dedicated tier: Designed for high-performance, production-ready applications with built-in automated performance optimization, this tier lets you scale applications confidently with MongoDB Atlas’s robust observability, security, and management capabilities. Figure 1. An overview of the Free, Flex, and Dedicated tiers This tiered approach gives developers a unified platform for their entire journey. It ensures smooth transitions as projects evolve from prototypes to enterprise-grade applications. At MongoDB, our focus has always been on removing obstacles for innovators, and this simple scaling path empowers developers to focus on innovation rather than navigating infrastructure challenges. Supporting startups with unpredictable traffic When startups launch applications with uncertain user adoption rates, they often face scalability and cost challenges. But the Atlas Flex tier addresses these issues! For example, startups can begin building apps with minimal upfront costs. The Atlas Flex tier enables them to scale effortlessly to accommodate traffic spikes, with support for up to 500 operations per second whenever required. And as user activity stabilizes and grows, migrating to dedicated clusters is a breeze. MongoDB Atlas removes the stress of managing infrastructure. It enables startups to focus on building exceptional user experiences and achieving product-market fit. Accelerating MVPs for gen AI applications The Atlas Flex tier is particularly suitable for minimum viable products in generative AI applications. Indeed, those incorporating vector search capabilities are perfect use cases. For example, imagine a small research team specializing in AI. It has developed a prototype that employs MongoDB Atlas Vector Search for the management of embeddings in the domain of natural language processing. The initial workloads remain under 100 ops/sec. As such, the overhead costs $8 per month. As the model is subjected to comprehensive testing and as demand for queries increases, the application can be seamlessly scaled while performance is uninterrupted. Given the top-end cap of $30 per month, developers can refine the application without concerns for infrastructure scalability or unforeseen expenses. The table below shows how monthly Atlas Flex tier pricing breaks down by capacity. Understanding the costs: The Atlas Flex tier’s pricing breakdown. The monthly fee for each level of usage is prorated and billed on an hourly basis. All clusters on MongoDB Atlas, including Atlas Flex tier clusters, are pay-as-you-go. Clusters are only charged for as long as they remain active. For example, a workload that requires 100 ops/sec for 20 days, 250 ops/sec for 5 days, and 500 ops/sec for 5 days would cost approximately $13.67. If the cluster was deleted after the first 20 days of usage, the cost would be approximately $5.28. This straightforward and transparent pricing model ensures developers can plan budgets with confidence while accessing world-class database capabilities. Get started today The Atlas Flex tier revolutionizes database management. It caters to projects at all stages—from prototypes to production. Additionally, it delivers cost stability, enhanced scalability, and access to MongoDB’s robust developer tools in a single seamless solution. With Atlas Flex tier, developers gain the freedom to innovate without constraints, confident that their database can handle any demand their applications generate. Whether testing groundbreaking ideas or scaling for a product launch, this tier provides comprehensive support. Learn more or get started with Atlas Flex tier today to elevate application development to the next level.
Official Django MongoDB Backend Now Available in Public Preview
We are pleased to announce that the Official Django MongoDB Backend Public Preview is now available. This Python package makes it easier than ever to combine the sensible defaults and fast development speed Django provides with the convenience and ease of MongoDB. Building for the Python community For years, Django has been consistently rated one of the most popular web frameworks in the Python ecosystem. It’s a powerful tool for building web applications quickly and securely, and implements best practices by default while abstracting away complexity. Over the last few years, Django developers have increasingly used MongoDB, presenting an opportunity for an official MongoDB-built Python package to make integrating both technologies as painless as possible. We recognize that success in this endeavor requires more than just technical expertise in database systems—it demands a deep understanding of Django's ecosystem, conventions, and the needs of its developer community. So we’re committed to ensuring that the Official Django MongoDB Backend not only meets the technical requirements of developers, but also feels painless and intuitive, and is a natural complement to the base Django framework. What’s in the Official Django MongoDB Backend In this public preview release, the Official Django MongoDB Backend offers developers the following capabilities: The ability to use Django models with confidence . Developers can use Django models to represent MongoDB documents, with support for Django forms, validations, and authentication. Django admin support . The package allows users to fire up the Django admin page as they normally would, with full support for migrations and database schema history. Native connecting from settings.py . Just as with any other database provider, developers can customize the database engine in settings.py to get MongoDB up and running. MongoDB-specific querying optimizations . Field lookups have been replaced with aggregation calls (aggregation stages and aggregate operators), JOIN operations are represented through $lookup, and it’s possible to build indexes right from Python. Limited advanced functionality . While still in development, the package already has support for time series, projections, and XOR operations. Aggregation pipeline support . Raw querying allows aggregation pipeline operators. Since aggregation is a superset of what traditional MongoDB Query API methods provide, it gives developers more functionality. And this is just the start—more functionality (including BSON data type support and embedded document support in arrays) is on its way. Stay tuned for the General Availability release later in 2025! Benefits of using the Official Django MongoDB Backend While during the public preview MongoDB requires more work to set up in the initial stages of development than Django’s defaults, the payoff that comes from the flexibility of the document model and the full feature set of Atlas makes that tradeoff worth it over the whole lifecycle of a project. With the Official Django MongoDB Backend, developers can architect applications in a distinct and novel way, denormalizing their data and creating Django models so that data that is accessed together is stored together. These models are both easier to maintain and their retrieval is more performant for a number of use cases—which when paired with the robust, native Django experience MongoDB is creating is a compelling offering, improving the developer experience and accelerating software development. At its core, the MongoDB document model aligns well with Django's mission to “encourage rapid development and clean, pragmatic design.” The MongoDB document model naturally mirrors how developers think about and structure their data in code, allowing for a seamless context switch between a Django model and a MongoDB document. For many modern applications— especially those dealing with hierarchical, semi-structured, or rapidly evolving data structures— the document model provides a more natural and flexible solution than traditional relational databases. Dovetailing with this advantage is the fact it’s simpler than ever to develop locally with MongoDB, thanks to how painless it is to create a local Atlas deployment with Docker. With sensible preconfigured defaults, it’s possible to create a single-node replica set simply by pulling the Docker image and running it, using only an Atlas connection string, and no extra steps needed. The best part? It’s even possible to convert an existing Atlas implementation running in Docker Compose to a local image. Developing with Django and MongoDB just works with the Atlas CLI and Docker. How to get started with the Official Django MongoDB Backend To get started, it’s as easy as running pip install django-mongodb-backend . MongoDB has even created an easy-to-use starter template that works with the django-admin command startproject , making it a snap to see what typical MongoDB migrations look like in Django. For more information, check out our quickstart guide . Interested in giving the package a try for yourself? Please try our quickstart guide and consult our comprehensive documentation . To see the raw code behind the package and follow along with development, check out the repository . For an in-depth look into some of the thinking behind major package architecture decisions, please read this blog post by Jib Adegunloye. Questions? Feedback? Please post on our community forums or through UserVoice . We value your input as we continue to work to build a compelling offering for the Django community.
Test Out Search Like Never Before: Introducing Search Demo Builder
MongoDB is excited to announce the availability of Search Demo Builder , the newest addition to the Atlas Search Playground. The Search Demo Builder allows anyone to jump right in and discover the value of MongoDB Atlas Search without first creating an Atlas account. The Search Demo Builder offers an intuitive environment for testing and configuring common search features—without having to build an index or to write queries from scratch. What is the Search Demo Builder? Search Demo Builder is an interactive tool within the Atlas Search Playground that makes exploring MongoDB Atlas Search simple and accessible. It allows you to explore, configure, and experiment with key features like searchable fields, autocomplete, and facets—all without needing technical expertise, writing queries, or building indexes from scratch. Best of all, with Search Demo Builder you can see exactly how changes affect the search results through the Search Experience Preview. This feature gives you a real-time look at what your experience would look like as you tweak and configure your feature set. Some of the key features of Search Demo Builder include: Searchable fields utilizing dynamic fields as the default, but with the option to specify fields to search against. Autocomplete that can be configured on string fields to enable a search-as-you-type experience, and includes index definition and autocomplete query. Filters and facets that are interactive and can be configured on arrays of strings and numbers. Experience preview screen where features are reflected in an interactive preview experience. Index and query definitions that are auto-generated based on the configured search features Figure 1. A view of the new Search Demo Builder experience. User benefits associated with Search Demo Builder include: Instant setup: Start immediately with preloaded datasets or upload your own small collection—no sign-up or complex configuration required. Guided exploration: Step-by-step product tours and tooltips make Search Demo Builder accessible for users of all skill levels. Interactive workspace: Experiment with features like autocomplete and facets in a dedicated, visual environment. Shareable indexes and queries: View and copy generated indexes and query definitions for use outside of Search Demo Builder. Search Demo Builder versus Code Sandbox The Search Demo Builder is designed to make Atlas Search accessible for users who prefer a visual interface and makes exploring and testing search features quick. The Code Sandbox , meanwhile, offers deeper customization and hands-on experimentation with JSON queries. Together, these tools provide a comprehensive environment for working with Atlas Search, regardless of your experience level. For more information on the Atlas Search Playground, including the Code Sandbox, check out our initial announcement blog . Get started with Search Demo Builder today Ready to try out Atlas Search for yourself? Head over to Search Demo Builder today and see what you can do with Atlas Search (you can also navigate to it in the lefthand navigation once you visit the Atlas Search Playground UI). Whether you’re testing out ideas for a new project or just getting your feet wet, the new Search Demo Builder provides an easy to navigate experience that makes getting started a breeze. Figure 2. Lefthand nav panel with Search Demo Builder. To learn more about the Atlas Search Playground, visit our documentation . And be sure to share what you think in our user feedback portal .
MongoDB Atlas Integration with Ably Unlocks Real-time Capabilities
Enterprises across sectors increasingly realize that data, like time, doesn’t wait. Indeed, harnessing and synchronizing information in real time is the new currency of business agility. Enter the alliance between MongoDB and Ably—a partnership that has led to Ably's new database connector for MongoDB Atlas . The new database connector provides a robust framework for businesses to create real-time, data-intensive applications that can provide top-notch user experiences thanks to an opinionated client SDK to be used on top of LiveSync, ensuring both data integrity and real-time consistency—without compromising your existing tech stack. The synergy of MongoDB Atlas and Ably LiveSync This new MongoDB Atlas-Ably integration tackles a fundamental challenge in modern application architecture: maintaining data consistency across distributed systems in real-time. MongoDB Atlas serves as the foundation—a flexible, scalable database service that adapts to the ebb and flow of data demands. Meanwhile, Ably LiveSync acts as the nervous system, ensuring that every change, every update, resonates instantly across the entire application ecosystem. The Ably LiveSync database connector for MongoDB Atlas offers a transformative approach to real-time data management, combining unparalleled scalability with seamless synchronization. This solution effortlessly adapts to growing data volumes and expanding user bases, catering to businesses of all sizes—from agile startups to established enterprises. By rapidly conveying database changes to end-users, it ensures that all stakeholders operate from a single, up-to-date source of truth, fostering data consistency across the entire organization. At its core, LiveSync is built with robust resilience in mind, featuring built-in failover mechanisms and connection recovery capabilities. This architecture provides businesses with the high availability they need to maintain continuous operations in today's always-on digital landscape. Moreover, by abstracting away the complexities of real-time infrastructure, LiveSync empowers developers to focus on creating features that drive business value. This focus on developer productivity, combined with its scalability and reliability, positions Ably LiveSync for MongoDB Atlas as a cornerstone technology for companies aiming to harness the power of real-time data synchronization. Figure 1: Ably real-time integration with MongoDB Atlas. Industry transformation: A real-time revolution This new integration has a number of implications across various sectors. For example, in the banking and financial services sector , the MongoDB Atlas-Ably integration enables instantaneous fraud detection systems that can promptly react to potential threats. Live trading platforms benefit as well, seamlessly updating to reflect every market change as it happens. Banking applications are equally enhanced, with real-time updating of account balances and transactions, ensuring that users always have access to the most recent financial information. In the retail industry , meanwhile, the integration facilitates real-time inventory management across both physical and online stores, ensuring that supply matches demand at all times. This capability supports dynamic pricing strategies that can adapt instantly to fluctuations in consumer interest, and it powers personalized shopping experiences with live product recommendations tailored to individual customer preferences. Manufacturing and mobility sectors also see transformative benefits. With the capability for real-time monitoring of production lines, businesses can implement just-in-time manufacturing processes, streamlining operations and reducing waste. Real-time tracking of vehicles and assets enhances logistics efficiency, while predictive maintenance systems provide foresight into potential equipment failures, allowing for timely interventions. The healthcare sector stands to gain significantly from this technology. Real-time patient monitoring systems offer healthcare providers immediate alerts, ensuring swift medical responses when necessary. Electronic health records receive seamless updates across multiple care settings, promoting coherent patient care. Efficient resource allocation is achieved through live tracking of hospital beds and equipment, optimizing hospital operations. Insurance companies are not left out of this technological leap. The integration allows for dynamic risk assessment and pricing models that adapt in real-time, refining accuracy and responsiveness. Instant claim processing and status updates enhance customer satisfaction, while live tracking of insured assets facilitates more accurate underwriting and expedites the resolution of claims. Finally, in telecommunications and media this integration promises buffer-free content delivery and streaming services, vastly improving the end-user experience. real-time network performance monitoring enables proactive issue resolution, maintaining service quality. Users can enjoy synchronized experiences across multiple devices and platforms, fostering seamless interaction with digital content. Today's business imperative As industries continue to evolve at a rapid pace, the integration of MongoDB Atlas and Ably LiveSync provides a compelling way for businesses to not only keep up but lead the real-time revolution. For IT decision-makers looking to put their organizations at the forefront of innovation, this integration turns static data into a dynamic driver of business growth and market leadership. Access MongoDB Atlas and Ably LiveSync Resources and start your journey towards real-time innovation today. Learn more about how MongoDB Atlas can power industry-specific solutions .
Leveraging BigQuery JSON for Optimized MongoDB Dataflow Pipelines
We're delighted to introduce a major enhancement to our Google Cloud Dataflow templates for MongoDB Atlas. By enabling direct support for JSON data types, users can now seamlessly integrate their MongoDB Atlas data into BigQuery, eliminating the need for complex data transformations. This streamlined approach not only saves users time and resources, but it also empowers customers to unlock the full potential of their data through advanced data analytics and machine learning. Figure 1: JSON feature for user options on Dataflow Templates Limitations without JSON support Traditionally, Dataflow pipelines designed to handle MongoDB Atlas data often necessitate the transformation of data into JSON strings or flattening complex structures to a single level of nesting before loading into BigQuery. Although this approach is viable, it can result in several drawbacks: Increased latency: The multiple data conversions required can lead to increased latency and can significantly slow down the overall pipeline execution time. Higher operational costs: The extra data transformations and storage requirements associated with this approach can lead to increased operational costs. Reduced query performance: Flattening complex document structures in JSON String format can impact query performance and make it difficult to analyze nested data. So, what’s new? BigQuery's Native JSON format addresses these challenges by enabling users to directly load nested JSON data from MongoDB Atlas into BigQuery without any intermediate conversions. This approach offers numerous benefits: Reduced operating costs: By eliminating the need for additional data transformations, users can significantly reduce operational expenses, including those associated with infrastructure, storage, and compute resources. Enhanced query performance: BigQuery's optimized storage and query engine is designed to efficiently process data in Native JSON format, resulting in significantly faster query execution times and improved overall query performance. Improved data flexibility: users can easily query and analyze complex data structures, including nested and hierarchical data, without the need for time-consuming and error-prone flattening or normalization processes. A significant advantage of this pipeline lies in its ability to directly leverage BigQuery's powerful JSON functions on the MongoDB data loaded into BigQuery. This eliminates the need for a complex and time-consuming data transformation process. The JSON data within BigQuery can be queried and analyzed using standard BQML queries. Whether you prefer a streamlined cloud-based approach or a hands-on, customizable solution, the Dataflow pipeline can be deployed either through the Google Cloud console or by running the code from the github repository . Enabling data-driven decision-making To summarize, Google’s Dataflow template provides a flexible solution for transferring data from MongoDB to BigQuery. It can process entire collections or capture incremental changes using MongoDB's Change Stream functionality. The pipeline's output format can be customized to suit your specific needs. Whether you prefer a raw JSON representation or a flattened schema with individual fields, you can easily configure it through the userOption parameter. Additionally, data transformation can be performed during template execution using User-Defined Functions (UDFs). By adopting BigQuery Native JSON format in your Dataflow pipelines, you can significantly enhance the efficiency, performance, and cost-effectiveness of your data processing workflows. This powerful combination empowers you to extract valuable insights from your data and make data-driven decisions. Follow the Google Documentation to learn how to set up the Dataflow templates for MongoDB Atlas and BigQuery. Get started with MongoDB Atlas on Google Marketplace . Learn more about MongoDB Atlas on Google Cloud on our product page .
Checkpointers and Native Parent Child Retrievers with LangChain and MongoDB
MongoDB and LangChain, the company known for its eponymous large language model (LLM) application framework, are excited to announce new developments in an already strong partnership. Two additional enhancements have just been added to the LangChain codebase, making it easier than ever to build cutting-edge AI solutions with MongoDB. Checkpointer support In LangGraph, LangChain’s library for building stateful, multi-actor applications with LLMs, memory is provided through checkpointers . Checkpointers are snapshots of the graph state at a given point in time. They provide a persistence layer, allowing developers to interact and manage the graph’s state. This has a number of advantages for developers—human-in-the-loop, "memory" between interactions, and more. Figure adapted from “Launching Long-Term Memory Support in LangGraph”. LangChain Blog. Oct. 8, 2024. https://blog.langchain.dev/launching-long-term-memory-support-in-langgraph/ MongoDB has developed a custom checkpointer implementation, the " MongoDBSaver " class, that, with just a MongoDB URI (local or Atlas ), can easily store LangGraph state in MongoDB. By making checkpointers a first-class feature, developers can have confidence that their stateful AI applications built on MongoDB will be performant. That’s not all, since there are actually two new checkpointers as part of this implementation— one synchronous and one asynchronous . This versatility allows the new functionality to be even more versatile, and serving developers with a myriad of use cases. Both implementations include helpful utility functions to make using them painless, letting developers easily store instances of StateGraph inside of MongoDB. A performant persistence layer that stores data in an intuitive way will mean a better end-user experience and a more robust system, no matter what a developer is building with LangGraph. Native parent child retrievers Second, MongoDB has implemented a native parent child retriever inside LangChain. This approach enhances the performance of retrieval methods utilizing the retrieval-augmented Generation (RAG) technique by providing the LLM with a broader context to consider. In essence, we divide the original documents into relatively small chunks, embed each one, and store them in MongoDB. Using such small chunks (a sentence or a couple of sentences) helps the embedding models to better reflect their meaning. Now developers can use " MongoDBAtlasParentDocumentRetriever " to persist one collection for both vector and document storage. In this implementation, we can store both parent and child documents in a single collection while only having to compute and index embedding vectors for the chunks. This has a number of performance advantages because storing vectors with their associated documents means no need to join tables or worry about painful schema migrations. Additionally, as part of this work, MongoDB has also added a " MongoDBDocStore " class which provides many helpful utility functions. It is now easier than ever to use documents as a key-value store and insert, update, and delete them with ease. Taken together, these two new classes allow developers to take full advantage of MongoDB’s abilities. MongoDB and LangChain continue to be a strong pair for building agentic AI—combining performance and ease of development to provide a developer-friendly experience. Stay tuned as we build out additional functionality! To learn more about these LangChain integrations, here are some resources to get you started: Check out our tutorial . Experiment with checkpointers and native parent child retrievers to see their utility for yourself. Read the previous announcement with LangChain about AI Agents, Hybrid Search, and Indexing.
Binary Quantization & Rescoring: 96% Less Memory, Faster Search
We are excited to share that several new vector quantization capabilities are now available in public preview in MongoDB Atlas Vector Search : support for binary quantized vector ingestion, automatic scalar quantization, and automatic binary quantization and rescoring. Together with our recently released support for scalar quantized vector ingestion , these capabilities will empower developers to scale semantic search and generative AI applications more cost-effectively. For a primer on vector quantization, check out our previous blog post . Enhanced developer experience with native quantization in Atlas Vector Search Effective quantization methods—specifically scalar and binary quantization—can now be done automatically in Atlas Vector Search. This makes it easier and more cost-effective for developers to use Atlas Vector Search to unlock a wide range of applications, particularly those requiring over a million vectors. With the new “quantization” index definition parameters, developers can choose to use full-fidelity vectors by specifying “none,” or they can quantize vector embeddings by specifying the desired quantization type—”scalar” or “binary” (Figure 1). This native quantization capability supports vector embeddings from any model provider as well as MongoDB’s BinData float32 vector subtype . Figure 1: New index definition parameters for specifying automatic quantization type in Atlas Vector Search Scalar quantization—converting a float point into an integer—is generally used when it's crucial to maintain search accuracy on par with full-precision vectors. Meanwhile, binary quantization—converting a float point into a single bit of 0 or 1—is more suitable for scenarios where storage and memory efficiency are paramount, and a slight reduction in search accuracy is acceptable. If you’re interested in learning more about this process, check out our documentation . Binary quantization with rescoring: Balance cost and accuracy Compared to scalar quantization, binary quantization further reduces memory usage, leading to lower costs and improved scalability—but also a decline in search accuracy. To mitigate this, when “binary” is chosen in the “quantization” index parameter, Atlas Vector Search incorporates an automatic rescoring step, which involves re-ranking a subset of the top binary vector search results using their full-precision counterparts, ensuring that the final search results are highly accurate despite the initial vector compression. Empirical evidence demonstrates that incorporating a rescoring step when working with binary quantized vectors can dramatically enhance search accuracy, as shown in Figure 2 below. Figure 2: Combining binary quantization and rescoring helps retain search accuracy by up to 95% And as Figure 3 shows, in our tests, binary quantization reduced processing memory requirement by 96% while retaining up to 95% search accuracy and improving query performance. Figure 3: Improvements in Atlas Vector Search with the use of vector quantization It’s worth noting that even though the quantized vectors are used for indexing and search, their full-fidelity vectors are still stored on disk to support rescoring. Furthermore, retaining the full-fidelity vectors enables developers to perform exact vector search for experimental, high-precision use cases, such as evaluating the search accuracy of quantized vectors produced by different embedding model providers, as needed. For more on evaluating the accuracy of quantized vectors, please see our documentation . So how can developers make the most of vector quantization? Here are some example use cases that can be made more efficient and scaled effectively with quantized vectors: Massive knowledge bases can be used efficiently and cost-effectively for analysis and insight-oriented use cases, such as content summarization and sentiment analysis. Unstructured data like customer reviews, articles, audio, and videos can be processed and analyzed at a much larger scale, at a lower cost and faster speed. Using quantized vectors can enhance the performance of retrieval-augmented generation (RAG) applications. The efficient processing can support query performance from large knowledge bases, and the cost-effectiveness advantage can enable a more scalable, robust RAG system, which can result in better customer and employee experience. Developers can easily A/B test different embedding models using multiple vectors produced from the same source field during prototyping. MongoDB’s flexible document model lets developers quickly deploy and compare embedding models’ results without the need to rebuild the index or provision an entirely new data model or set of infrastructure. The relevance of search results or context for large language models (LLMs) can be improved by incorporating larger volumes of vectors from multiple sources of relevance, such as different source fields (product descriptions, product images, etc.) embedded within the same or different models. To get started with vector quantization in Atlas Vector Search, see the following developer resources: Documentation: Vector Quantization in Atlas Vector Search Documentation: How to Measure the Accuracy of Your Query Results Tutorial: How to Use Cohere's Quantized Vectors to Build Cost-effective AI Apps With MongoDB
Atlas Stream Processing Now Supports Azure and Azure Private Link
Today, we’re excited to announce that Atlas Stream Processing now supports Microsoft Azure! This update opens new possibilities for developers leveraging Azure’s cloud ecosystem, offering a way to: Seamlessly integrate MongoDB Atlas and Apache Kafka Effortlessly handle complex and rapidly changing data structures Use the familiarity of the MongoDB Query API for processing streaming data Benefit from a fully managed service that eliminates operational overhead Azure support in four regions At launch, we’re supporting four Azure regions spanning both the U.S. and Europe: Azure Region Location US East Virginia, US US East 2 Virginia, US US West California, US West Europe Netherlands We’ll continue adding more regions across cloud providers in the future. Let us know which regions you need next in UserVoice . Atlas Stream Processing simplifies integrating MongoDB with Apache Kafka to build event-driven applications. New to Atlas Stream Processing? Watch our 3-minute explainer . How it works Working with Atlas Stream Processing on Azure will feel just like it does already today when using AWS. During the Stream Processing Instance (SPI) tier selection in the Atlas UI or CLI, simply select Azure as your provider and then choose your desired region. Figure 1: Stream Processing instance setup via Atlas UI $ atlas streams instances create AzureSPI --provider AZURE --region westus --tier SP10 Figure 2: Stream Processing instance setup via the Atlas CLI Secure networking for Azure Event Hubs via Azure Private Link In addition to adding support for Azure in multiple regions, we’re introducing Azure Private Link support for developers using Azure Event Hubs . Event Hubs is Azure’s native, Kafka-compatible data streaming service. As a reminder, Atlas Stream Processing supports any service that uses the Kafka Wire Protocol . That includes Azure Event Hubs, AWS Managed Service for Kafka (MSK), Redpanda, and Confluent Cloud. As we have written before , security is critical for data services, and it’s especially important with stream processing systems where connecting to technologies like Apache Kafka external to a database like MongoDB Atlas, is required. For this reason, we’re engineering Atlas Stream Processing to leverage the advanced networking capabilities available through the major cloud providers (AWS, Azure, and GCP). Networking To better understand the value of support for private link, let’s summarize the three key ways that developers typically connect between data services: Public networking Private networking through VPC peering Private networking through private link Public networking connects services using public IP addresses. It’s the least secure of all approaches. This makes it the easiest to set up, but it's a less secure approach than either VPC peering or private link. Private networking through VPC peering connects services across two virtual private clouds (VPCs). This improves security compared with public networking by keeping traffic off the public internet and is commonly used for testing and development purposes. Private networking through private link is even more secure by enforcing connections to specific endpoints. While VPC peering lets resources from one VPC connect to all of the resources in the other VPC, private link ensures that each specific resource can only connect to defined services with specific associated endpoints. This connection method is important for use cases relying on sensitive data. Figure 3: Private Link allows for connecting to specific endpoints Ready to get started? With support for Azure Private Link, Atlas Stream Processing now makes it simple to implement the most secure method for networking across MongoDB and Kafka on Azure Event Hubs. Login today to get started, or check out our documentation to create your first private link connection.