Firebase & MongoDB Atlas: A Powerful Combo for Rapid App Development

Venkatesh Shanbhag, Zi Wang, and Iman Rahmati
April 7, 2025 | Updated: April 8, 2025

Firebase and MongoDB Atlas are powerful tools developers can use together to build robust and scalable applications. Firebase offers build and runtime solutions for AI-powered experiences, while MongoDB Atlas provides a fully managed cloud database service optimized for generative AI applications.

We’re pleased to announce the release of the Firebase extension MongoDB Atlas, a direct MongoDB connector for Firebase that further streamlines the development process by enabling seamless integration between the two platforms. This extension enables developers to directly interact with MongoDB collections and documents from within their Firebase projects, simplifying data operations and reducing development time.

A direct MongoDB connector, built as a Firebase extension, facilitates real-time data synchronization between Firebase and MongoDB Atlas. This enables data consistency across both platforms, empowering developers to build efficient, data-driven applications using the strengths of Firebase and MongoDB.

MongoDB as a backend database for Firebase applications

Firebase offers a streamlined backend for rapid application development, providing offerings like authentication, hosting, and real-time databases. However, applications requiring complex data modeling, high data volumes, or sophisticated querying often work well with MongoDB’s document store. Integrating MongoDB as the primary data store alongside Firebase addresses these challenges. MongoDB provides a robust document database with a rich query language (MongoDB Query Language), powerful indexing (including compound, geospatial, and text indexes), and horizontal scalability for handling massive datasets. This architecture enables developers to use Firebase’s convenient backend services while benefiting from MongoDB’s powerful data management capabilities. Developers commonly use Firebase Authentication for user management, then store core application data, including complex relationships and large volumes of information, in MongoDB. This hybrid approach combines Firebase’s ease of use with MongoDB’s data-handling prowess.

Furthermore, the integration of MongoDB Atlas Vector Search significantly expands the capabilities of this hybrid architecture. Modern applications increasingly rely on semantic search and AI-driven features, which require efficient handling of vector embeddings. MongoDB Atlas Vector Search enables developers to perform similarity searches on vector data, unlocking powerful use cases

Quick-start guide for Firebase’s MongoDB Atlas extension

With the initial release of the MongoDB Atlas extension in Firebase, we are targeting the extension to perform operations such as findOne, insertOne, and vectorSearch on MongoDB. This blog will not cover how to create a Firebase application but will walk you through creating a MongoDB backend for connecting to MongoDB using our Firebase extension. To learn more about how to integrate the deployed backend into a Firebase application, see the official Firebase documentation.

Install the MongoDB Atlas extension in Firebase.

Open the Firebase Extensions Hub.
Find and select the MongoDB Atlas extension.

Or use the search bar to find “MongoDB Atlas.”

Click on the extension card.
Click the “Install” button. You will be redirected to the Firebase console.

On the Firebase console, choose the Firebase project where you want to install the extension.

Image 1. Image of the MongoDB Atlas extension’s installation page.

Screenshot showing the MongoDB Atlas extension's installation page. The page is titled Install and authorize extension.

On the installation page:

Review “Billing and Usage.”
Review “API Endpoints.”
Review the permissions granted to the function that will be created.
Configure the extension: Provide the following configuration details:
- MongoDB URI: The connection string for your MongoDB Atlas cluster
  - Database Name: The name of the database you want to use
  - Collection Name: The name of the collection you want to use
  - Vertex AI Embedding to use: The type of embedding model from Vertex AI
  - Vertex AI LLM model name: The name of the large language model (LLM) model from Vertex AI
  - MongoDB Index Name: The name of the index in MongoDB
  - MongoDB Index Field: The field that the index is created upon
  - MongoDB Embedding Field: The field that contains the embedding vectors
  - LLM Prompt: The prompt that will be sent to the LLM

Click on “Install Extension.”

Image 2. Image of the MongoDB Atlas extension created from Firebase extension hub.

Screenshot of the MongoDB Atlas extension being created from Firebase extension hub.

Once the extension is created, you can interact with it through the associated Cloud Function.

Image 3. Firebase extension created cloud run function

Screenshot of the Firebase extension having created a cloud run function.

In conclusion, the synergy between Firebase extensions and MongoDB Atlas opens up exciting possibilities for developers seeking to build efficient, scalable, AI-powered applications. By using Firebase’s streamlined backend services alongside MongoDB’s robust data management and vector search capabilities, developers can create applications that handle complex data and sophisticated AI functionalities with ease.

The newly introduced Firebase extension for MongoDB Atlas, specifically targeting operations like findOne, insertOne, and vectorSearch, marks a significant step toward simplifying this integration. While this initial release provides a solid foundation, the potential for further enhancements, such as direct connectors and real-time synchronization, promises to further empower developers. As demonstrated through the quick-start guide, setting up this powerful combination is straightforward, enabling developers to quickly harness the combined strength of these platforms.

Ultimately, this integration fosters a more flexible and powerful development environment, enabling the creation of innovative, data-driven applications that meet the demands of modern users.

Build your application with a pre-packaged solution using Firebase.

Visit our product page to learn more about MongoDB Atlas.

← Previous

Next-Generation Mobility Solutions with Agentic AI and MongoDB Atlas

Driven by advancements in vehicle connectivity, autonomous systems, and electrification, the automotive and mobility industry is currently undergoing a significant transformation. Vehicles today are sophisticated machines, computers on wheels, that generate massive amounts of data, driving demand for connected and electric vehicles. Automotive players are embracing artificial intelligence (AI), battery electrical vehicles (BEVs), and software-defined vehicles (SDVs) to maintain their competitive advantage. However, managing fleets of connected vehicles can be a challenge. As cars get more sophisticated and are increasingly integrated with internal and external systems, the volume of data they produce and receive greatly increases. This data needs to be stored, transferred, and consumed by various downstream applications to unlock new business opportunities. This will only grow: the global fleet management market is projected to reach $65.7 billion by 2030, growing at a rate of almost 10.8% annually. A 2024 study conducted by Webfleet showed that 32% of fleet managers believe AI and machine learning will significantly impact fleet operations in the coming years; optimizing route planning and improving driver safety are the two most commonly cited use cases. As fleet management software providers continue to invest in AI, the integration of agentic AI can significantly help with things like route optimization and driver safety enhancement. For example, AI agents can process real-time traffic updates and weather conditions to dynamically adjust routes, ensuring timely deliveries while advising drivers on their car condition. This proactive approach contrasts with traditional reactive methods, improving vehicle utilization and reducing operational and maintenance costs. But what are agents? In short, they are operational applications that attempt to achieve goals by observing the world and acting upon it using the data and tools the application has at its disposal. The term "agentic" denotes having agency, as AI agents can proactively take steps to achieve objectives without constant human oversight. For example, rather than just reporting an anomaly based on telemetry data analysis, an agent for a connected fleet could autonomously cross-check that anomaly against known issues, decide whether it's critical or not, and schedule a maintenance appointment all on its own. Why MongoDB for agentic AI Agentic AI applications are dynamic by nature as they require the ability to create a chain of thought, use external tools, and maintain context across their entire workflow. These applications generate and consume diverse data types, including structured and unstructured data. MongoDB’s flexible document model is uniquely suited to handle both structured and unstructured data as vectors. It allows all of an agent’s context, chain-of-thought, tools metadata, and short-term and long-term memory to be stored in a single database. This means that developers can spend more time on innovation and rapidly iterate on agent designs without being constrained by rigid schemas of a legacy relational database. Figure 1. Major components of an AI agent. Figure 1 shows the major components of an AI agent. The agent will first receive a task from a human or via an automated trigger, and will then use a large language model (LLM) to generate a chain of thought or follow a predetermined workflow. The agent will use various tools and models during its run and store/retrieve data from a memory provider like MongoDB Atlas . Tools: The agent utilizes tools to interact with the environment. This can contain API methods, database queries, vector search, RAG application, anything to support the model Models: can be a large language model (LLM), vision language model (VLM), or a simple supervised machine learning model. Models can be general purpose or specialized, and agents may use more than one. Data: An agent requires different types of data to function. MongoDB’s document model allows you to easily model all of this data in one single database. An agentic AI spans a wide range of functional tools and context. The underlying data structures evolve throughout the agentic workflow and as an agent uses different tools to complete a task. It also builds up memory over time. Let us list down the typical data types you will find in an agentic AI application. Data types: Agent profile: This contains the identity of the agent. It includes instructions, goals and constraints. Short-term memory: This holds temporary, contextual information—recent data inputs or ongoing interactions—that the agent uses in real-time. For example, short-term memory could store sensor data from the last few hours of vehicle activity. In certain agentic AI frameworks like Langgraph, short term memory is implemented through a checkpointer. The checkpointer stores intermediate states of the agent’s actions and/or reasoning. This memory allows the agent to seamlessly pause and resume operations. Long-term memory: This is where the agent stores accumulated knowledge over time. This may include patterns, trends, logs and historical recommendations and decisions. By storing each of these data types into rich, nested documents in MongoDB, AI developers can create a single-view representation of an agent’s state and behavior. This enables fast retrieval and simplifies development. In addition to the document model advantage, building agentic AI solutions for mobility requires a robust data infrastructure. MongoDB Atlas offers several key advantages that make it an ideal foundation for these AI-driven architectures. These include: Scalability and flexibility: Connected Car platforms like fleet management systems need to handle extreme data volumes and variety. MongoDB Atlas is proven to scale horizontally across cloud clusters, letting you ingest millions of telemetry events per minute and store terabytes of telemetry data with ease. For example, the German company ZF uses MongoDB to process 90,000 vehicle messages per minute (over 50 GB of data per day) from hundreds of thousands of connected cars. The flexibility of the document model accelerates development and ensures your data model stays aligned with the real-world entities it represents. Built-in vector search: AI agents require a robust set of tools to work with. One of the most widely used tools is vector search, which allows agents to perform semantic searches on unstructured data like driver logs, error codes descriptions, and repair manuals. MongoDB Atlas Vector Search allows you to store and index high-dimensional vectors alongside your documents and to perform semantic search over unstructured data. In practice, this means your AI embeddings live right next to the relevant vehicle telemetry and operational data in the database, simplifying architectures for use cases like the connected car incident advisor, in which a new issue can be matched against past issues before passing contextual information to the LLM. For more, check out this example of how an automotive OEM leverages vector search for audio based diagnostics with MongoDB Atlas Vector Search. Time series collections and real-time data processing: MongoDB Atlas is designed for real-time applications. It provides time series collections for connected car telemetry data storage, change streams, and triggers that can react to new data instantly. This is crucial for agentic AI feedback loops, where ongoing data ingestion and learning are happening continuously. Best-in-class embedding models with Voyage AI: In early 2025, MongoDB acquired Voyage AI , a leader in embedding and reranking models. Voyage AI embedding models are currently being integrated into MongoDB Atlas, which means developers will no longer need to manage external embedding APIs, standalone vector stores, or complex search pipelines. AI retrieval will be built into the database itself, making semantic search, vector retrieval, and ranking as seamless as traditional queries. This will reduce the time required for developing agentic AI applications. Agentic AI in action: Connected fleet incident advisor Figure 2 shows a list of use cases in the Mobility sector, sorted by various capabilities that an agent might demonstrate. AI agents excel at managing multi-step tasks via context management across tasks, they automate repetitive tasks better than Robotic process automation (RPA), and they demonstrate human-like reasoning by revisiting and revising past decisions. These capabilities enable a wide range of applications both during the manufacturing of a vehicle and while it's on the road, connected and sending telemetry. We will review a use case in detail below, and will see how it can be implemented using MongoDB Atlas, LangGraph, Open AI, and Voyage AI. Figure 2. Major use cases of agentic AI in the mobility and manufacturing sectors. First, the AI agent connects to traditional fleet management software and supports the fleet manager in diagnosing and advising the drivers. This is an example of a multi-step diagnostic workflow that gets triggered when a driver submits a complaint about the vehicle's performance (for example, increased fuel consumption). Figure 3 shows the sequence diagram of the agent. Upon receiving the driver complaint, it creates a chain of thought that follows a multi-step diagnostic workflow where the system ingests vehicle data such as engine codes and sensor readings, generates embeddings using the Voyage AI voyage-3-large embedding model, and performs a vector search using MongoDB Atlas to find similar past incidents. Once relevant cases are identified, those–along with selected telemetry data–are passed to OpenAI gpt-4o LLM to generate a final recommendation for the driver (for example, to pull off immediately or to keep driving and schedule regular maintenance). All data, including telemetry, past issues, session logs, agent profiles, and recommendations are stored in MongoDB Atlas, ensuring traceability and the ability to refine diagnostics over time. Additionally, MongoDB Atlas is used as a checkpointer by LangGraph, which defines the agent's workflow. Figure 3. Sequence diagram for a connected fleet advisor agentic workflow. Figure 4 shows the agent in action, from receiving an issue to generating a recommendation. So by leveraging MongoDB’s flexible data model and powerful Vector Search capabilities, we can agentic AI can transform fleet management through predictive maintenance and proactive decision-making. Figure 4. The connected fleet advisor AI agent in action. To set up the use case shown in this article, please visit our GitHub repository . And to learn more about MongoDB’s role in the automotive industry, please visit our manufacturing and automotive webpage . Want to learn more about why MongoDB is the best choice for supporting modern AI applications? Check out our on-demand webinar, “ Comparing PostgreSQL vs. MongoDB: Which is Better for AI Workloads? ” presented by MongoDB Field CTO, Rick Houlihan.

April 4, 2025

Next →

Rethinking Information Retrieval in MongoDB with Voyage AI

The future of AI-powered search The role of the modern database is evolving. AI-powered applications require more than just fast, scalable, and durable data management: they need highly accurate data retrieval and intelligent ranking, which are enabled by the ability to extract meaning from large volumes of unstructured inputs like text, images, and video. Retrieval-augmented generation (RAG) is now the default for LLM-powered applications, making accuracy in AI-driven search and retrieval a critical priority for developers. Meanwhile, customers in industries like healthcare, legal, and finance need highly reliable answers to power the applications their users rely on. MongoDB Atlas Search already combines keyword and vector search through its hybrid capabilities. However, to truly meet developers’ needs and expectations, we are expanding our focus to integrating best-in-class embedding and reranking models into Atlas to ensure optimal performance and superior outcomes. These models enable search systems to understand meaning beyond exact words in text, and to recognize semantic similarities across images, video, and audio. Embedding models and rerankers empower customer support teams to quickly match queries with pertinent documents, assist legal professionals in surfacing key clauses within long contracts, and optimize RAG pipelines by retrieving contextually significant information that addresses users’ queries. MongoDB is actively building this future. In February, we announced the acquisition of Voyage AI , a pioneer in state-of-the-art embedding and reranking models. With Voyage’s leading models and Atlas Search, developers will get a unified, production-ready stack for semantic retrieval. Why embedding and reranking matter Embedding and reranking models are core components of modern information retrieval, providing the link between natural language and accurate results: Embedding models transform data into vector representations that capture meaning and context, enabling searches based on semantic similarity rather than just keyword matches. Reranking models improve search accuracy by scoring and ranking a smaller set (e.g., 1000) of documents based on their relevance to a query, ensuring the most meaningful results appear first. A typical system uses an embedding model to project documents into a vector space that encodes semantics. A nearest neighbor search provides a list of documents close to a given query. These results are processed with a reranking model that enables deeper, clause-by-clause comparison between the queries and the nearest neighbors. This combination can greatly improve retrieval accuracy. For example, the system processing a user query for “holiday cookie recipes without tree nuts” may first retrieve a set of holiday recipes with the nearest neighbor search. In reranking, the query would be fully compared to each retrieved document to ensure each recipe does not contain any nuts. Voyage AI’s embedding and reranking models Voyage offers a suite of embedding models that support both general-purpose use cases and domain-specific needs . General models like voyage-3 , voyage-3-large , and voyage-3-lite handle diverse text inputs. For specialized applications, Voyage provides models tailored to domains like code ( voyage-code-3 ), legal ( voyage-law-2 ), and finance ( voyage-finance-2 ), offering higher accuracy by capturing the context and semantics unique to each field. They also offer a multimodal model ( voyage-multimodal-3 ) capable of processing interleaved text and images. In addition, Voyage provides reranking models in standard and lite versions , each focused on optimizing relevance while keeping latency and computational load under control. Voyage’s embedding models are designed to optimize the two distinct workloads required for each application, and our inference platform is purpose-built to support both scenarios efficiently: Document embeddings are created for all documents in a database whenever they are added or updated, capturing the semantic meaning of the documents an application has access to. Typically generated in batch, they are optimized for scale and throughput. Query embeddings enable the system to effectively interpret the user's intent for relevant results. Produced for a user's search query at the moment it's made, they are optimized for low latency and high precision. Figure 1. Voyage AI's embedding workflow: Document and query processing in MongoDB. Voyage AI’s embedding and reranking models consistently outperform leading production-grade models across industry benchmarks. For example, the general-purpose voyage-3-large model shows up to 20% improved retrieval accuracy over widely adopted production models across 100 datasets spanning domains like law, finance, and code. Despite its performance, it requires 200x less storage when using binary quantized embeddings. Domain-specific models like voyage-code-2 also outperform general-purpose models by up to 15% on code tasks On the reranking side, rerank-lite-1 and rerank-1 deliver gains of up to 14% in precision and recall across over 80 multilingual and vertical-specific datasets. These improvements translate directly into better relevance, faster inference, and more efficient RAG pipelines at scale. MongoDB Atlas Search + Voyage AI models today MongoDB Atlas Vector Search enables powerful semantic retrieval with a wide range of embedding and reranking models. Developers can benefit from using Voyage models with Atlas Vector Search today, even before the deeper integration arrives. Figure 2. Example code for embedding and vector search with Voyage AI and MongoDB. “AI-powered search”, not “AI Search” Not all AI search experiences are created equal. As we begin integrating Voyage AI models directly into MongoDB Atlas, it’s worth sharing how we’re approaching this work. The best solutions today blend traditional information retrieval with modern AI techniques, improving relevance while keeping systems explainable and tunable. AI-powered search in MongoDB Atlas enhances traditional search techniques with modern AI models. Embeddings improve semantic understanding, and reranking models refine relevance. But unlike opaque AI stacks, this approach remains transparent, customizable, and efficient: More control: Developers can tune search logic and ranking strategies based on their domain. More flexibility: Models can be updated or swapped to improve on an industry-specific corpus of data. More efficiency: MongoDB handles both storage and retrieval, optimizing cost and performance at scale. With Voyage’s models integrated directly into Atlas workflows, developers gain powerful semantic capabilities without sacrificing clarity or maintainability. Building the MongoDB + Voyage AI “better together” story While MongoDB’s flexible query language unlocks powerful capabilities, Atlas Vector Search can require thoughtful setup, especially for advanced use cases. Users must select and fine-tune embedding models to fit specific use cases. Additionally, they must either rely on serverless model APIs or build and maintain infrastructure to host models themselves. Each insert of new data and search query requires independent API calls, adding operational overhead. As applications scale or when models need updating, managing these new data types in clusters introduces additional friction. Finally, integrating rerankers further complicates the workflow by requiring separate API calls and custom handling for reordering results. By natively bringing Voyage AI's industry-leading models to MongoDB Atlas, we will eliminate these burdens and introduce new capabilities that empower customers to deliver highly relevant query results with simplicity. MongoDB is actively integrating Voyage's embedding and reranking models into Atlas to deliver a truly native experience. These deep integrations will not only simplify the developer workflow but will also enhance accuracy, performance, and cost efficiency - all without the usual complexity of tuning disparate systems. And our ongoing commitment to partnering with innovative companies across AI and tech ensures that models from various providers remain supported within a collaborative ecosystem. However, adopting the native Voyage models allows developers to focus on building their applications while achieving the highest quality of information retrieval. Figure 3. Enhanced AI-powered retrieval with MongoDB and Voyage AI. As we work on these native integrations, we're actively exploring advanced capabilities to further enhance the Atlas platform. Our investigations focus on: Defining the optimal approach to multi-modal information retrieval, integrating diverse inputs like text and images for richer results. Developing instruction-tuned retrieval, which allows concise prompts to precisely guide model interpretations, ensuring searches align closely with user intent. For example, enabling a search for “shoes” to prioritize sneakers or dress shoes, depending on user behavior and preferences. Determining the best ways to integrate domain-specific models tailored to the unique needs and use cases of industries such as legal, finance, and healthcare to achieve superior retrieval accuracy. Making it easy to update and change models without impacting availability. Bringing additional AI capabilities into our expressive aggregation pipeline language Improving the ability to automatically assess model performance, with the potential to offer this capability to customers. Building the future of AI-powered search From RAG pipelines to AI-powered customer experiences, information retrieval is the backbone of real-world AI applications. Voyage’s models strengthen this foundation by surfacing better documents and improving final LLM outputs. We are building this future around four core principles, with accuracy at the forefront: Accurate: ensuring the precision of information retrieval is always our top priority, empowering applications to achieve production-grade quality and mass adoption. Seamless: built into existing developer workflows. Scalable: optimized for performance and cost. Composable: open, flexible, and deeply integrated. By embedding Voyage into Atlas, MongoDB offers the best of both worlds: industry-leading retrieval models inside a fully managed, developer-friendly platform. This unified platform allows models and data to work together seamlessly, empowering developers to build scalable, high-performance AI applications with precision at their core. Join our MongoDB Community to learn about upcoming events, hear stories from MongoDB users, and connect with community members from around the world.

April 24, 2025