Sold by: MongoDB, Inc
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Trusted by global brands, MongoDB Atlas on AWS is a deeply integrated data platform that powers scaled, enterprise level AI applications across various industries.
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Overview
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MongoDB Atlas is the data foundation for the AI era, unifying operational, analytical, and AI workloads in a single database platform.
With MongoDB Atlas on AWS, enterprises can turn AI into ROI faster using proven technology, combined industry experience, and dedicated support from MongoDB and AWS.
Try MongoDB Atlas (Mongo as a Service) today with the free trial tier and get 512 MB of storage at no cost. Dedicated clusters start at just USD 0.08 per hour, and you can easily scale up or out to meet the demands of your application. Costs vary based on your specific cluster configurations, network usage, backup policies, and use of additional features. Get started today and see how MongoDB Atlas can help you build and scale your modern applications easily.
Highlights
- MongoDB Atlas integrates native vector search directly into an operational database, significantly simplifying the creation of RAG and agentic AI solutions. This eliminates the necessity for separate search infrastructure, enabling teams to accelerate iteration, optimize dynamically, and expedite the deployment of generative AI applications compared to traditional relational databases.
- MongoDB Atlas has a flexible document model that enables the storage and synchronization of varied data types - structured, unstructured, and semi-structured - even as these datasets change. This makes it an ideal foundation for AI-driven applications that depend on dynamic and diverse information.
- MongoDB Atlas provides robust, built-in security features that safeguard your data and ensure security by default. It complies with key industry standards like HIPAA, GDPR, ISO 27001, and PCI DSS, allowing you to build confidently with industry-leading data protection.
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Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/unit |
|---|---|
MongoDB Atlas Credits used | $1.00 |
Dimensions summary
MongoDB Atlas Credits are a flexible payment mechanism used to pay for services on the MongoDB Atlas cloud platform. One Atlas Credit is equivalent to $1 USD of usage and can be applied toward a wide range of resources, including database clusters, storage, data transfer, backups, and additional Atlas features. There is no upfront charge for Atlas, you simply pay as you consume MongoDB Atlas. This approach enables customers to scale usage based on their needs while maintaining predictable costs, especially when purchased and consumed through the AWS Marketplace.
Top-of-mind questions for buyers like you
How do MongoDB Atlas Credits work for billing purposes?
MongoDB Atlas Credits act as a flexible currency within the Atlas platform, where 1 credit equals $1 USD. With no upfront charges, customers only pay for what they use, credits are automatically deducted based on actual consumption of resources like database instances, storage, and features via AWS Marketplace.
What factors determine my MongoDB Atlas usage costs?
MongoDB Atlas usage costs are determined by factors like cluster tier, cloud provider, storage, IOPS, backup size, data transfer, and add-on features such as Atlas Search. You pay per hour or per operation, with no upfront charges, allowing scalable, flexible billing based on actual resource consumption and usage patterns.
Can I estimate my MongoDB Atlas costs before committing to a purchase?
MongoDB provides a pricing calculator on their website to estimate costs based on your expected workload and configuration needs. Additionally, you can start with a free tier to test the service, and Atlas offers real-time usage monitoring to help track and forecast your credit consumption.
Vendor refund policy
This is a pay as you go service. You will be invoiced based on your usage.
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By MongoDB, Inc
By SingleStore
Top
10
In Databases & Analytics Platforms, Generative AI
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10
In Data Analysis, Databases & Analytics Platforms, Databases
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10
In Analytic Platforms, Databases & Analytics Platforms, Databases
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
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AI generated from product descriptions
Vector Search Capability
Native vector search integration directly within the operational database, enabling efficient RAG and agentic AI solution development
Data Model Flexibility
Document-based model supporting storage and synchronization of structured, unstructured, and semi-structured data types with dynamic adaptability
Security Compliance
Built-in security features compliant with industry standards including HIPAA, GDPR, ISO 27001, and PCI DSS
AI Application Support
Unified platform for operational, analytical, and AI workloads with seamless data management capabilities
Database Architecture
Single database platform designed to support enterprise-level AI application development and scaling
Database Architecture
Multi-purpose database supporting key-value, JSON documents, SQL query, vectors, and full-text search capabilities
Scalability Model
Dynamic scaling with ability to scale out, in, up, and down across individual database services
Data Synchronization
Fully managed data synchronization for mobile and IoT use cases with edge device support
Security Framework
Advanced role-based access control (RBAC) with encryption for data in transit and at rest
Analytics Processing
Native JSON-based analytics with zero ETL transformation and real-time processing architecture
Distributed Database Architecture
Fully managed, distributed SQL database supporting transactional and analytical workloads in a single engine
Vector Search Capabilities
Integrated vector search functionality with indexed search for AI applications and generative AI use cases
High-Performance Data Ingestion
Ability to ingest millions of events per second using parallel, distributed lock-free pipelines
Concurrent Query Processing
Supports scaling access to tens or hundreds of thousands of concurrent users with super-low latency queries
Cloud-Native Infrastructure
Built on a modern, lock-free cloud-native architecture enabling elastic scalability and high-performance data processing
Validated by AWS Marketplace
FedRAMP
GDPR
HIPAA
ISO/IEC 27001
PCI DSS
SOC 2 Type 2
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Standard contract
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Customer reviews
Pranay Jain
Cloud database has supported charity platform growth and has improved team delivery speed
Reviewed on Jan 30, 2026
Review from a verified AWS customer
What is our primary use case?
MongoDB Atlas is a completely cloud-based database that can be used in real-life applications. I have mostly used it with the MEAN stack, with Angular as a front-end and Node with MongoDB as my primary database in the back-end.
I built a charity-based platform called Danam for orphanages. This platform is currently used by more than 10 orphanages to gain charity from different people, and I implemented MongoDB Atlas there.
What is most valuable?
Setting up MongoDB Atlas is very easy, as I simply select the cluster and the size I want. There are free clusters available for testing real applications, and it is a NoSQL-based database that works well for both web and mobile applications, providing real-time analytics-driven solutions where flexible schema is the main priority.
The best feature MongoDB Atlas offers is the flexible database schema that I can easily set up. It is easy to set up, has no server setup cost, requires no manual setting, and has no downtime. The platform automatically handles traffic spikes and includes built-in features such as storage, auto-scaling, automatic backup, and point-in-time recovery.
Auto-scaling and backups have been instrumental in my project using MongoDB Atlas, especially when I moved from UAT to production. My customer base of more than 10 lakhs increased, and the system automatically scaled to handle those requests, ensuring the database could respond in time.
MongoDB Atlas has an aggregation framework that handles complex analytics without requiring any extra tools. It also provides real-time updates and streaming capabilities.
MongoDB Atlas has positively impacted my organization by reducing operational costs, as I do not need database administrators or server maintenance. It follows a pay-as-you-go strategy, allowing me to increase cluster size by simply paying more, which results in faster development and deployment processes. High availability is also provided through built-in replications and automatic failovers.
My team consists of 18 people, and our uptime has improved to 99.9% and above. The delivery speed has increased, and I have reduced the delivery cycle by 30 to 50 percent.
What needs improvement?
An improvement I can suggest for MongoDB Atlas is achieving even faster query execution and smoother application performance. In terms of scalability, it handles system growth without failure, but it has experienced some outages that could affect uptime.
For how long have I used the solution?
I have been working with this solution for three years.
What's my experience with pricing, setup cost, and licensing?
Pricing-wise, MongoDB Atlas has a pay-as-you-go strategy. The documentation for MongoDB is very good; I have learned multiple things through reading it. The free tier is M0 for $0, which is suitable for basic applications. Shared clusters such as M2 or M5 are not costly. I also have access to M10 and M20 for larger usage, and I am currently using M40, which is the latest one.
What other advice do I have?
My advice for others considering MongoDB Atlas is that its scalability is exceptional. Even if you do not currently have schema knowledge, you can still use it. Its performance is quite good, as MongoDB allows you to change your data structure easily and supports fast deployment and development with simple JSON. I rate this product a 10.
Vashishth P.
Effortless Setup, Powerful Data Handling
Reviewed on Jan 21, 2026
Review provided by G2
What do you like best about the product?
I really appreciate MongoDB's ability to support multiple data structures and document-based storage. The partition tolerance and its high availability make it extremely powerful. The integration and setup with our microservices are super easy, and there is really good documentation available to get started with it. MongoDB Atlas cloud database is extremely great, and we're shifting our focus from on-premise hosting to a cloud-hosted database. Also, it's easy to make changes in the schema because of its document-based modeling.
What do you dislike about the product?
Nothing to dislike about MongoDB. Everything works well with this powerful noSQL database.
What problems is the product solving and how is that benefiting you?
MongoDB solves the problem of saving complex data structures with document-based modeling, making schema changes easy. MongoDB Atlas cloud database is excellent, shifting our focus from on-premise to cloud hosted solutions.
Nijat I.
Database with Strong Performance
Reviewed on Jan 08, 2026
Review provided by G2
What do you like best about the product?
MongoDB provides ease and flexibility when working with massive and unstructured data. MongoDB has a document structure that enables the complex data to be stored without schemas. The MongoDB platform scales smoothly and handles both small and big applications. The platform integrates easily with programming languages and environments. It promotes fast development. It offers a scalable and flexible means to manage database operations.
What do you dislike about the product?
The more complex queries will sometimes be less intuitive than what one would find in a traditional SQL database. Certain aspects of the system require further setup or the use of a paying account. Dealing with very large datasets might require a good amount of indexing and optimization. Aggregation pipelines will occasionally be a problem for the new user. It’s a good system, but these small problems occur.
What problems is the product solving and how is that benefiting you?
MongoDB tackles the challenge of handling unstructured and large-scale data with efficiency. It allows for flexible data modeling, fast development, and scaling without hassle. There's built-in replication and sharding for enhanced reliability and performance. All in all, this has saved time, simplified database administration, and supported application development in a scalable fashion.
Bilal M.
Effortless Setup, Needs Better Vectorization Support
Reviewed on Dec 31, 2025
Review provided by G2
What do you like best about the product?
I like the basic architecture of MongoDB and how easy it is to find my JSON with Python libraries. It provides a good score with Python libraries, making data export, encryption, and decryption very easy. The latest feature about vector databases is just amazing for me as an AI engineer and has changed the landscape for me. I no longer need to use any other vector database, and I'm really comfortable using MongoDB. The initial setup was very easy, especially with the Mongo Compass and the resources provided for Python, which make it easier than any other setup.
What do you dislike about the product?
The major thing with the record databases is that you need to set them up manually most of the time. I would prefer if there's a setup to define everything from Python code rather than having to go into the Mongo interface and change it there. They don't provide automatic integration of vectorized databases from the Python code, which is a bit of a setback for me.
What problems is the product solving and how is that benefiting you?
I use MongoDB to easily store non-SQL data like JSON objects. It streamlines storing embeddings and integrates well with Python, saving me time and effort. MongoDB's ease of use and vector database feature are game-changers for my AI work.
Rajiv .
Perfect for Developers: Flexible Schemas and Powerful Aggregation
Reviewed on Dec 26, 2025
Review provided by G2
What do you like best about the product?
Basically being as a developer i used it for making my schemas for backend in database and it have advantages of bson type structure which helps me to store the values of realtime type data and it helps me to implement aggregation pipeline as well as use the free tier of database of 512 mb which can be used in mongodb compass
What do you dislike about the product?
Nothing everything is fine just vpc connection is quite harder for new person
What problems is the product solving and how is that benefiting you?
Basically we are having chatting software as well as run time key addtion requirement at that time we have implement it for saving the data of different key as well as json type structure and it gives us benefits on pipeline of that schemas as. well