MongoDB is a key player in data storage and analytics, with a document-oriented setup. This differs from legacy relational-style databases and next-gen versions like Snowflake. How so? Relational databases store data in rigid rows and columns linked by pre-set relationships. These databases look like giant Excel spreadsheets and use structured query language (SQL) to work. The datasets are fixed, with formatting and filtering more limited.
Per MDB leadership, relational databases cannot seamlessly handle unstructured data like MongoDB’s “Not Only SQL” (NoSQL) data lake can. MDB's document-oriented setup organizes and uses unstructured data more effectively, which is vital in the age of agentic AI. Relational and SQL are fine for traditional machine learning, but less effective for agentic, multi-step, goal-oriented tasks. It’s worth noting that Snowflake and Databricks would readily argue their platforms are better suited for fielding unstructured data. It's subjective.
- MongoDB’s NoSQL document-oriented database is called Atlas.
- The newest version of its database software that powers Atlas is called MongoDB 8.1. This offers 20% to 60% performance boosts vs. the old version and better time series (timeline-based) data services.
Atlas’s cloud-native database architecture uses a group of servers (or a cluster) to actually store data for eventual product creation within its platform. The nature of MongoDB’s Atlas product allows clusters to be easily added to or subtracted from for seamless tweaking as needs fluctuate. That greatly helps control costs and improve performance. It also offers MongoDB Search for data querying with a highly capable Vector Search offering for AI use cases as well. Vector Search allows clients to effortlessly scrape insights from data via theme-based querying rather than just word-based. MongoDB’s retrieval-augmented generation (RAG) perfectly complements these search products, as it enriches large language model (LLM) outputs with external context as needed to uplift querying precision. This way, search products deliver optimally relevant and accurate responses.
More Core Atlas Products:
- MongoDB Data Lake houses unstructured data for querying and analytics.
- Atlas Stream Processing enables real-time data ingestion. That matters a lot for app developers who constantly toy with, split-test and render every single little detail within their apps. Real-time access to data querying helps make that process better.
- Atlas Search Nodes (nodes = servers) automate productive usage of compute capacity and separate database & search functions to enable easier, more affordable scaling. This product is key for cloud cost control.
Product Expansion & AI:
Beyond its aforementioned Vector Search offering, MongoDB excels in automating the cumbersome data modernization work for migrations. Its Relational Migrator (RM) handles and takes client headaches out of this process. Now, it’s focused on leveraging RM to let companies actively re-code their legacy applications and create more of an end-to-end, full-service migration. They call this the “modernization factory.” With this holistic strategy, MongoDB becomes a more prevalent and relied-upon vendor, as it directly handles data and software modernization through its singular platform.
Voyage AI (recently acquired) is a key player in GenAI and agentic AI model trustworthiness. While models routinely create delightful, jaw-dropping experiences, they’re also wrong a lot. Hallucination rates (wrong answer rates) can often reach 25%, making models really only useful when you know what a right answer should look like and are double-checking. That limitation puts a tight ceiling on utility and is what Voyage aims to fix.
It has two products. First is its set of Vector Embedding Models. As leadership puts it, these are the “bridge between models and a client's private data.” They allow for meticulous information transfer into models and significantly overlap with its RAG offering with the aim of ensuring maximum answer accuracy. Embedding Models from Voyage are what actually make data usage for RAG and their database offerings by numerically organizing insights in a more standardized manner. All of this helps with machine learning accuracy rates and uncovering patterns to reduce model mistakes. Secondly, it offers reranking models. As an LLM query yields a batch of sources to begin forming a final response, these reranking models (as the name indicates) rank the available sources to curate the final answer from a better batch of information. It’s a second filter to make sure models are using only the best, most accurate information. With Voyage, MDB gains a broader suite of GenAI services to round out a more cohesive platform.
More things to Know:
Atlas App Services is a serverless toolkit for developers to build modern apps. It provides a slew of templates, integrations and guardrails to diminish building friction. MongoDB AI Applications Program (MAAP) is an AI adoption acceleration initiative. It helps customers effectively use GenAI tools (with Voyage AI giving them more confidence in doing so). It gives customers the help they need to embrace new AI-enabled technologies like Vector Search.
Enterprise Advanced (EA) refers to its on-premise (Atlas is cloud-based), database and app bundle. It allows companies to purchase licensing for subscription-based usage (rather than paying for consumption under Atlas).
Most of MongoDB’s growth is based on acquiring new customers and migrating their workloads onto the platform, as well as consumption-based revenue from customers using its data products and growing workloads.