MongoDB

Miranda Barrigas

Product Marketing Manager

As part of MongoDB’s ongoing commitment to innovation and providing the best possible developer experience, we have some important updates about our [Atlas GraphQL](https://www.mongodb.com/docs/atlas/app-services/graphql/) and [Atlas Hosting services](https://www.mongodb.com/docs/atlas/app-services/hosting/). Our goal is always to offer developers the best services and tools on Atlas, whether built by MongoDB or delivered by our trusted partners so that builders can focus on providing the best application possible. In line with this vision, we strategically decided to deprecate the Atlas GraphQL API and Atlas Hosting services. 

This blog post outlines what this means for users, the timeline for this transition, and how we plan to support you through this change.

**What’s Changing?**

New users cannot create apps with GraphQL / hosting enabled. Existing customers will have time to move off of the service and find an alternative solution by **September 30, 2025**.

**Why Are We Making This Change?**

The decision to streamline our services reflects our commitment to natively offering best-in-class services while collaborating with leading partners to provide the most comprehensive data platform.

**How We’re Supporting You**

We recognize that challenges can come with change, so our team will continue to provide comprehensive assistance and guidance to ensure a smooth migration process. As part of our commitment to providing developers the best services and tools, we have identified several MongoDB partners who offer best-in-class solutions with similar functionality to our GraphQL and hosting services.

We’ve collaborated with some of these partners to create official step by step migration guides in order to provide a seamless transition to our customers. We encourage you to explore these options [here](https://www.mongodb.com/docs/atlas/app-services/migrate-hosting-graphql).

- **Migration Assistance**: Learn more about the MongoDB partner integrations that make it easy to connect to your Atlas database:
    - **GraphQL Partners**: [Apollo](https://cloud.mongodb.com/ecosystem/apollographql), [Hasura](https://cloud.mongodb.com/ecosystem/hasura), [WunderGraph](https://docs.wundergraph.com/docs/supported-data-sources/mongodb-atlas), [Grafbase](https://cloud.mongodb.com/ecosystem/grafbase), [AWS AppSync](https://cloud.mongodb.com/ecosystem/aws-appsync)
    - **Hosting Partners**: [Vercel](https://cloud.mongodb.com/ecosystem/vercel), [Netlify](https://www.mongodb.com/developer/languages/javascript/developing-web-application-netlify-serverless-functions-mongodb/#create-a-new-netlify-project-with-the-netlify-cli), [Koyeb](https://cloud.mongodb.com/ecosystem/koyeb), [Northflank](https://cloud.mongodb.com/ecosystem/northflank), [DigitalOcean](https://cloud.mongodb.com/ecosystem/digitalocean-managed-mongodb)
- **Support and Guidance**: Our support team is available to assist you with any questions or concerns. We encourage you to reach out via the [MongoDB Support Portal](https://www.mongodb.com/docs/atlas/support/#get-help-with-service) or contact your Account Executive for personalized assistance.

**Looking Forward**

We’re here to support you every step of the way as you explore and migrate to alternative solutions. Our team is working diligently to ensure this transition is as seamless as possible for all affected users. We’re also excited about what the future holds for the MongoDB Atlas, the industry’s leading modern database, and the new features we’re developing to enhance your experience.

Guidance and resources on how to migrate from MongoDB Atlas GraphQL and Hosting services.

Deprecating MongoDB Atlas GraphQL and Hosting Services

Diana Annie Jenosh

Senior Partner Solutions Architect

At Ignite, MongoDB announced a new milestone in their relationship with the integration of MongoDB Atlas with Microsoft Azure OpenAI Service. This significant integration is a testimony to the commitment of our collaboration and serves as a milestone to help customers on the journey of building generative AI applications grounded in enterprise data, providing for rich and meaningful user interactions.

## Advanced AI and rich data: Best of both worlds

Retrieval-augmented generation (RAG) is becoming the norm for generative AI applications. Without RAG, large language models (LLMs) can only answer based on the public data which they have been trained upon. This is a significant limitation and inhibits the use of these LLMs for production use cases. RAG helps overcome this challenge by grounding the LLM in the enterprise-owned data which the LLM otherwise had no knowledge of. 

Azure OpenAI Service helps to build state-of-the-art generative AI applications by choosing powerful models such as GPT-4o and adding more context to the LLMs from enterprise data, which now can be from MongoDB Atlas, to get more relevant and insightful conversations. Azure OpenAI Service is available as an API, SDK, and in Azure AI Foundry. Thus, customers using Azure AI Foundry can now add MongoDB Atlas vector index in the chat playground and evaluate the LLMs with the vector data stored in MongoDB Atlas collections. The API can directly be invoked from applications to easily build RAG applications grounded in MongoDB Atlas data.

Azure AI Foundry is a good starting point for enterprises aiming to build innovative generative AI applications in a secure and responsible way. The model catalog in Azure AI Foundry is a hub of models from Azure OpenAI Service and other leading providers, offering foundation, open, task, and industry-specific models. AI Foundry supports the entire lifecycle of designing an AI application starting from model evaluation and benchmarking, customization of the application,  flow creation, and debugging, to managing generative AI applications without compromising security and following responsible AI best practices. On the other hand, MongoDB Atlas is one of the world’s most popular document model databases with native vector search capabilities. [MongoDB Atlas Vector Search](https://www.mongodb.com/lp/cloud/atlas/try4?webxp579=true&utm_source=google&utm_campaign=search_gs_pl_evergreen_vector-search_product_prosp-brand_gic-null_ww-multi_ps-all_desktop_eng_lead&utm_term=mongodb%20atlas%20vector%20search&utm_medium=cpc_paid_search&utm_ad=p&utm_ad_campaign_id=21100816499&adgroup=162920454394&cq_cmp=21100816499&gad_source=1&gclid=Cj0KCQjwsoe5BhDiARIsAOXVoUuaMs42NEwXdNZYR3vcROrgzN-cQLCgsqmNWRc1nZSWbH_3J4UaqvkaAoe-EALw_wcB) was recognized by the [Retool Survey](https://www.mongodb.com/resources/solutions/use-cases/retool-2024-state-of-ai-in-production) as a leader with the highest Net Promoter Score (NPS) in 2023 and 2024\. MongoDB Atlas stores vector data alongside operational data without any additional overhead, owing to its inherently flexible document data model. MongoDB Atlas supports full text and hybrid searches along with vector search on dedicated search nodes in Azure which ensures that operational workloads are not impacted by generative AI application workloads.

## MongoDB Atlas with Azure OpenAI Service: RAG 

MongoDB Atlas is preferred as a vector database for RAG applications as it can store vectors seamlessly with the rest of the operational data, which means no ETL, reduced architectural complexity, and cost savings. MongoDB’s native vector search uses the uniform MongoDB API which implies it can be combined with other MongoDB Query API constructs to enable pre-filtering and post-filtering. MongoDB Atlas is designed for reliability, scalability, and security which are of prime importance when it comes to generative AI applications. Enabling easy access to MongoDB data via Azure OpenAI Service makes RAG implementations possible for even non-developers.

The diagram below shows the RAG architecture implemented through this integration. The user’s question is converted to embeddings using an embedding model such as “text-embedding-ada-002.” In the retrieval stage, the embeddings pertaining to the user query are used for a vector search in MongoDB Atlas to get similar chunks from the MongoDB Atlas collection. In the Augmented stage, these chunks retrieved from MongoDB Atlas are added to the actual user query as context to create a prompt for the LLM. In the Generation stage, the LLM is invoked passing it the augmented prompt, and now the LLM answers to the query grounded in the context provided in the prompt. The result is that now, the LLM provides accurate and relevant answers. Refer to the citations to see what data was retrieved from MongoDB Atlas that led to the answer provided by the LLM. 

![RAG with MongoDB Atlas and Azure OpenAI Service][1]

## MongoDB Atlas with Azure OpenAI Service: A seamless experience  

The integration is available as a REST API that can be invoked from your applications like any other REST API. Developers need to add the MongoDB collection and vector index information in the payload specifications along with the prompt and the system message/role information while invoking the API. The API will return the response along with the citations which point to the chunks retrieved from the MongoDB vector database along with the metadata of the chunk for easy cross verification. 

The integration is accessible from the chat playground in AI Foundry. It's a few clicks wizard experience to point the LLM to use the data from MongoDB Atlas collection while responding to the queries, providing for enhanced AI experience. MongoDB Atlas is available in the drop-down menu in the “Add your data” experience in the portal’s chat playground. Users just need to select MongoDB Atlas and point to the Atlas vector index to leverage the robust vector search capability of MongoDB Atlas and equip the LLMs with the precise information required to answer the queries. This makes creating chatbots and copilots for internal applications or customer-facing portals extremely easy.

Watch the video to see a [demo](https://youtu.be/ucOG5vdHbI8) of the integration in Azure AI Foundry and witness how easy it is now to bring your valuable data in MongoDB Atlas to your LLMs to build elegant conversational experiences.

> Join the [MongoDB AI Applications Program (MAAP)](https://www.mongodb.com/services/consulting/ai-applications-program) framework to accelerate your AI journey and let MongoDB help you take your AI application from ideation to production.
>
> Refer to the [Microsoft official documentation](https://aka.ms/OYDMongoDoc) for more details on the integration.


  [1]: https://images.contentstack.io/v3/assets/blt39790b633ee0d5a7/blt9f7f54f7cfa3032e/673bc2144ece01fcc8836450/image1.png

RAG Made Easy with MongoDB Atlas and Azure OpenAI

MongoDB is excited to present a mirroring solution for our customers who are looking to bring operational data from MongoDB Atlas to Microsoft Fabric for big data analytics, AI, and BI, combining it with the rest of the data estate of the enterprise. Mirroring in Fabric provides a unique way to bring in data from operational data stores into the uniform data layer of OneLake in Fabric. Open Mirroring is designed to enable Data ISVs to extend it because it is based on an open Delta Lake table format. In addition, Database Mirroring provides public APIs for Data ISVs to integrate with the open mirroring approach. Once mirroring is enabled for a MongoDB Atlas collection, the corresponding table in OneLake is kept in sync with the changes in source MongoDB Atlas collection, unlocking opportunities of varied analytics and AI and BI in near real-time. To learn more about the Open Mirroring capabilities, please visit [Introducing Open Mirroring in Microsoft Fabric](https://aka.ms/mirroring/intro-open-mirroring).

## MongoDB Atlas in Fabric ecosystem

Today, there are [multiple ways](https://www.mongodb.com/developer/products/atlas/unlock-value-data-mongodb-atlas-intelligent-analytics-microsoft-fabric/) data from MongoDB Atlas can be brought into OneLake in Microsoft Fabric. [Data pipelines](https://learn.microsoft.com/en-us/fabric/data-factory/connector-mongodb-atlas-overview) and [Dataflow Gen2](https://learn.microsoft.com/en-us/fabric/data-factory/connector-mongodb-atlas-sql-overview) are easy mechanisms to bring data from MongoDB Atlas to OneLake in batches or micro-batches. The Data Pipeline connector for MongoDB Atlas provides an easy experience via the [Copy data assistant](https://learn.microsoft.com/en-us/fabric/data-factory/create-first-pipeline-with-sample-data) which allows users to choose MongoDB Atlas as a source and OneLake as a target to push the operational data to OneLake. It also supports MongoDB Atlas as a target to ingest the analytics and enriched data back to MongoDB Atlas for persistence. Dataflow Gen2 is an [Atlas SQL-based connector](https://learn.microsoft.com/en-us/power-query/connectors/mongodb-atlas-sql-interface) and allows you to pull data from MongoDB Atlas to OneLake, do transformations such as filtering and flattening of data, and run power queries to create rich Power BI visualizations. Additionally, the [MongoDB Spark connector](https://www.mongodb.com/docs/spark-connector/v10.4/) can be used to create Fabric Spark notebooks that pull/push data from MongoDB Atlas in batch and streaming mode, as well. Data Pipeline Copy activity, Dataflow Gen2, and Spark notebooks can be orchestrated by Data Pipeline to build enterprise workflows with MongoDB as a key source and destination. 

## Mirroring MongoDB Atlas to Fabric OneLake

Joint customers will gain value from Mirroring, which will enable them with Spark-based analytics, SQL-based warehousing capabilities, SynapseML-based AI/ML predictions, and KQL-based, real-time intelligence on the current and up-to-date data in MongoDB Atlas.

The insert, update, and delete events occurring on the source MongoDB Atlas collection will be mirrored to the target OneLake table in near real-time. The conversion of the format to Parquet, data type conversions, and handling schema changes is all handled by mirroring. Mirroring generates a [SQL analytics endpoint](https://learn.microsoft.com/en-us/fabric/data-warehouse/get-started-lakehouse-sql-analytics-endpoint) to enable SQL analytics using T-SQL on the mirrored data tables in a read-only fashion. It also generates a [default semantic model](https://learn.microsoft.com/en-us/fabric/data-warehouse/semantic-models) to build Microsoft Power BI reports and dashboards out of the mirrored OneLake tables. 

The following diagram depicts the mirroring integration architecture. 

![MongoDB Atlas mirroring in Fabric][1]

## MongoDB Atlas Mirroring implementation

Our implementation of mirroring utilizes the mirroring open extensibility platform in Fabric and is intended to benefit customers who have been looking to achieve this functionality since it was pre-announced at Microsoft Ignite two years back. The mirroring open extensibility platform, announced at Ignite this year, provides a set of APIs to be used to create a MirrorDB. Once a Landing Zone is created within the MirrorDB in OneLake, then all the Parquet files pushed to the Landing Zone will be replicated into the corresponding MirrorDB tables.

Microsoft has published a set of APIs and formats for any third-party developer, partner, customer, or ISV to push change data to the Landing Zone. 

The solution requires a one-time execution of a simple Python script that invokes the Microsoft Fabric mirroring APIs from the mirroring open extensibility platform to create the MirrorDB for MongoDB Atlas. 

Once the Landing Zone in MirrorDB is created, you click the **Deploy to Azure** button, available at the bottom in the provided GitHub repo, as shown below.

![MongoDB Fabric Replication][2]

It will take you to an ARM template configuration screen in your Azure tenant. Once the required details are input and the **Create** button is selected, it will create an **App** **service**, deploy the Python app, and start the mirroring application. The mirroring application will trigger **Initial Sync** for the one-time historical data ingestion into OneLake and **Listening** which then keeps replicating changes in the source MongoDB Atlas collections to the target tables in the MirrorDB. 

The solution also can also be deployed in a VM in your Azure tenant or any bare metal server. If deployed in a VM, the Azure Vnet of the VM can be peered or private linked with MongoDB Atlas for secure communication. 

The following diagram depicts how the app service powered by Python script enables mirroring. 

![MongoDB Atlas mirroring Solution][3]

Refer to the [short video](https://youtu.be/SHsnhax9ISo) for the MongoDB Atlas to OneLake mirroring solution explanation.

## MongoDB Atlas mirroring use cases 

Mirroring will benefit multiple industries and their use cases where near real-time analytics are required. The mirrored MongoDB database in OneLake comes with a SQL analytics endpoint which simplifies the data warehousing experience by using T-SQL or the visual query editor. This can be used to combine data from other lakehouses and warehouses in Fabric for a holistic enterprise data-estate analysis. The semantic model can help build reports and dashboards using Power BI and the mirrored data. These reports and outputs can help take responsive actions in near real-time, especially in highly regulated industries. 

The mirrored data can be used in near real-time, ML-based analytics like credit scoring, fraud detection in the financial services industry, for dynamic pricing, inventory management in retail industry, or for predictive maintenance in the manufacturing industry.

## Conclusion

Mirroring for MongoDB Atlas will enable you to move your data from MongoDB Atlas collections to OneLake without any complex setup or ETL process involved. This near real-time data synchronization into the Delta format allows the mirrored data to be utilized by the variety of Fabric tools to gather accurate and meaningful insights.

[Get started with mirroring](https://github.com/mongodb-partners/MongoDB_Fabric_Mirroring) for your MongoDB Atlas data.


  [1]: https://images.contentstack.io/v3/assets/blt39790b633ee0d5a7/blt78131f4073929380/673bc3e634c039710e0242f7/image1.png
  [2]: https://images.contentstack.io/v3/assets/blt39790b633ee0d5a7/blt3668fe516d79c962/673bc4158581943ff80e099e/image3.png
  [3]: https://images.contentstack.io/v3/assets/blt39790b633ee0d5a7/blt883b92a85b788816/673bc434b35d667cc66d8f92/image2.png

Near Real-time Analytics Powered by Mirroring in Microsoft Fabric for MongoDB Atlas

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News & Announcements

Featured News & Announcements

Deprecating MongoDB Atlas GraphQL and Hosting Services

RAG Made Easy with MongoDB Atlas and Azure OpenAI

Near Real-time Analytics Powered by Mirroring in Microsoft Fabric for MongoDB Atlas

All News & Announcements