Analyst(s): Nick Patience, Steven Dickens
Publication Date: October 8, 2024
MongoDB has released version 8.0 of its document database, focused on enhancing developer productivity and innovation through the use of AI, particularly in the area of application modernization. It says its product improvements are attracting companies as customers that are beyond its original cloud- and digital-native cohort as they scramble to modernize their legacy applications. Ultimately, MongoDB’s ambition is to become a foundational platform for building AI-powered applications, not just a database for web apps.
What is Covered in this Article:
- MongoDB shipped version 8.0 of its document database with improved performance, scalability, observability and security.
- The company is focused on three main opportunities: developer acceleration, AI applications, and application modernization.
- Developer acceleration involves empowering developers to build and ship applications faster with improved tooling, streamlined workflows, and the power of AI.
- MongoDB positions itself as the database foundation for AI applications, with new features such as vector search, integrated vector database, and quantization.
- Application modernization includes enabling enterprises to modernize legacy applications by leveraging AI for code conversion, refactoring, and unlocking data silos.
The News: MongoDB has released version 8.0 of its document database, focused on enhancing developer productivity and innovation through the use of AI, particularly in the area of application modernization. It says its product improvements are attracting companies as customers that are beyond its original cloud- and digital-native cohort as they scramble to modernize their legacy applications.
MongoDB Creating FOMO as App Modernization Takes Hold
Analyst Take: MongoDB is shifting its focus from being solely a NoSQL database for web apps to becoming a foundation for application modernization, targeting enterprises with legacy systems. This, it claims, is creating an element of FOMO (fear of missing out) among companies in mature sectors such as insurance, banking, and pharmaceuticals. As reference customer Novo Nordisk commented at the MongoDB.local London event, they had a 30-year history of using SQL databases, followed by some experiments with graphic databases before realizing that a document model is what they needed.
Performance Improvements
As developers are MongoDB’s core user base, it is always working to improve their experience. New with version 8.0 is an enhanced developer experience with improved VS Code and GitHub Copilot integration, and a new IntelliJ plugin in private preview. An enhanced MongoDB Atlas Vector Search provides a unified solution for storing, searching, and querying vector embeddings of unstructured data, combining the benefits of standalone and integrated vector databases; improved observability with filterable and timeout-enabled resource consumption for queries; and quantization reduces the memory and storage footprint of vector embeddings, making them more cost-effective for large-scale deployments. Finally, regarding security, after introducing queryable encryption in version 7.0, version 8.0 adds support for range queries to find data within a date or numeric range while remaining encrypted, which is crucial in industries such as finance and healthcare.
AI Partnerships
MongoDB recognizes that it requires a robust partner ecosystem to achieve its goals. By partnering with leading AI providers such as OpenAI, Anthropic, and Microsoft AI, MongoDB integrates advanced models and capabilities into its platform, enhancing its AI-powered offerings. These partnerships are key to MongoDB’s success, enabling the company to push beyond its core competencies and deliver cutting-edge functionality such as integrated vector search, crucial for handling unstructured data in AI workloads. The collaboration also includes reference architectures that accelerate the development and deployment of AI applications, allowing enterprises to modernize legacy systems with greater efficiency. Through this partner ecosystem, MongoDB positions itself not just as a database provider but as a foundational platform for scalable, AI-centric applications. By tapping into the innovations of leading AI companies, MongoDB ensures that its platform remains at the forefront of enterprise application modernization in the AI era.
Real-Time Applications
The demand for real-time data processing is growing, albeit gradually, with techniques such as real-time AI model inference still limited to a few narrow use cases due to current performance constraints. While AI models are not yet fast enough for widespread real-time inference, MongoDB recognizes a trend of streaming data processing moving closer to the application layer. With MongoDB 8.0’s enhancements, such as faster data distribution and improved vector search, the platform is better positioned to support real-time applications. These improvements enable enterprises to offer more dynamic, responsive user experiences, particularly in areas such as finance, IoT, and e-commerce.
Low-Code/No-Code Disruption
Not only is complex code now being written using Gen AI tools, but the abstraction layer of low-code/no-code platforms is evolving as well. MongoDB is witnessing this trend, as vertical-specific ISVs leverage AI to build powerful applications atop its document database. By enabling developers and non-technical users alike to harness advanced AI capabilities, MongoDB 8.0 supports the growing adoption of low-code/no-code platforms. Features such as integrated vector search and enhanced developer tools make it easier for users to create scalable, data-driven applications without deep coding expertise, allowing businesses to accelerate innovation and application development.
What to Watch:
Things to watch out for in MongoDB 8.0 release include the following:
- Performance Enhancements: MongoDB’s 32% throughput and 200% time series boost reflect the race to handle complex workloads. This challenges AWS DynamoDB and Google Spanner, positioning MongoDB for real-time and IoT applications.
- Scalability and Costs: Faster data distribution and 50% lower shared costs highlight MongoDB’s focus on cloud-native efficiency, competing with Aurora and Cosmos DB for large-scale, distributed applications without vendor lock-in.
- Security Enhancements: Queryable Encryption’s full-lifecycle protection targets growing regulatory demands, challenging competitors such as Google BigQuery that focus on encryption but lack MongoDB’s seamless data processing.
- Vector Search Optimization: MongoDB’s quantized vectors improve efficiency for AI workloads, competing with Elasticsearch and Pinecone, while offering a unified platform for enterprises consolidating AI and search functions.
See the complete press release on the general availability of MongoDB 8.0 on the MongoDB website.
Disclosure: The Futurum Group is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of The Futurum Group as a whole.
Other insights from The Futurum Group:
Analyzing MongoDB’s Q1 FY 2025 Success
Earnings Roundup and More on Sovereign Cloud – Infrastructure Matters, Episode 43
Author Information
Nick Patience is VP and Practice Lead for AI Platforms at The Futurum Group. Nick is a thought leader on AI development, deployment, and adoption - an area he has researched for 25 years. Before Futurum, Nick was a Managing Analyst with S&P Global Market Intelligence, responsible for 451 Research’s coverage of Data, AI, Analytics, Information Security, and Risk. Nick became part of S&P Global through its 2019 acquisition of 451 Research, a pioneering analyst firm that Nick co-founded in 1999. He is a sought-after speaker and advisor, known for his expertise in the drivers of AI adoption, industry use cases, and the infrastructure behind its development and deployment. Nick also spent three years as a product marketing lead at Recommind (now part of OpenText), a machine learning-driven eDiscovery software company. Nick is based in London.
Steven engages with the world’s largest technology brands to explore new operating models and how they drive innovation and competitive edge.
