Amazon DynamoDB is a cloud-native, NoSQL, serverless database service.
$0
capacity unit per hour
Amazon RDS
Score 8.4 out of 10
N/A
Amazon Relational Database Service (Amazon RDS) is a database-as-a-service (DBaaS) from Amazon Web Services.
N/A
MongoDB
Score 8.9 out of 10
N/A
MongoDB is an open source document-oriented database system. It is part of the NoSQL family of database systems. Instead of storing data in tables as is done in a "classical" relational database, MongoDB stores structured data as JSON-like documents with dynamic schemas (MongoDB calls the format BSON), making the integration of data in certain types of applications easier and faster.
$0.10
million reads
Pricing
Amazon DynamoDB
Amazon Relational Database Service (RDS)
MongoDB
Editions & Modules
Provisioned - Read Operation
$0.00013
capacity unit per hour
Provisioned - Write Operation
$0.00065
capacity unit per hour
Provisioned - Global Tables
$0.000975
per Read Capacity
On-Demand Streams
$0.02
per 100,000 read operations
Provisioned - Streams
$0.02
per 100,000 read operations
On-Demand Data Requests Outside AWS Regions
$0.09
per GB
Provisioned - Data Requests Outside AWS Regions
$0.09
per GB
On-Demand Snapshot
$0.10
per GB per month
Provisioned - Snapshot
$0.10
per GB per month
On-Demand Restoring a Backup
$0.15
per GB
Provisioned - Restoring a Backup
$0.15
per GB
On-Demand Point-in-Time Recovery
$0.20
per GB per month
Provisioned - Point-in-Time Recovery
$0.20
per GB per month
On-Demand Read Operation
$0.25
per million requests
On-Demand Data Stored
$0.25
per GB per month
Provisioned - Data Stored
$0.25
per GB per month
On-Demand - Write Operation
$1.25
per million requests
On-Demand Global Tables
$1.875
per million write operations replicated
Amazon RDS for PostgreSQL
$0.24 ($0.48)
per hour, R5 Large (R5 Extra Large)
Amazon RDS for MariaDB
$0.25 ($0.50)
per hour, R5 Large (R5 Extra Large)
Amazon RDS for MySQL
$0.29 ($0.58)
per hour, R5 Large (R5 Extra Large)
Amazon RDS for Oracle
$0.482 ($0.964)
per hour, R5 Large (R5 Extra Large)
Amazon RDS for SQL Server
$1.02 ($1.52)
per hour, R5 Large (R5 Extra Large)
Shared
$0
per month
Serverless
$0.10million reads
million reads
Dedicated
$57
per month
Offerings
Pricing Offerings
Amazon DynamoDB
Amazon RDS
MongoDB
Free Trial
No
No
Yes
Free/Freemium Version
No
No
Yes
Premium Consulting/Integration Services
No
No
No
Entry-level Setup Fee
No setup fee
Optional
No setup fee
Additional Details
—
—
Fully managed, global cloud database on AWS, Azure, and GCP
More Pricing Information
Community Pulse
Amazon DynamoDB
Amazon Relational Database Service (RDS)
MongoDB
Considered Multiple Products
Amazon DynamoDB
Verified User
Engineer
Chose Amazon DynamoDB
DynamoDB provided an easy to use, schema-less, out of the box solution that can be used to spin up a full working implementation very easily. It doesn't require extra knowledge such as MongoDB query functions
Lesser flexibility but better performance, and more predictable development support are the key points where Amazon DynamoDB comes out on top, when compared to MongoDB.
We are always assembling our solutions on AWS and DynamoDB is a better fit for us because of its simplicity. DynamoDB has its ow sets of triggers that make this an integrated solution on AWS. Besides, we wanted to use a key-value solution for our simple edge DB, and we didn't …
DynamoDB's scalability is more automated and effortless, making it easier to handle rapid growth. Other tools require more manual configuration while DynamoDB simplifies database administration. Also, DynamoDB provides strong consistency while other tools like MongoDB and Apache…
MongoDB has some performance issues and can get corrupted from time to time and has needed to be rebuilt. We have not had that experience while using DynamoDB.
DynamoDB is a natural fit for anyone using the AWS environment for their code. If we were using Google or not tied to anything then Firebase might have been a better choice as it supports pub / sub among other things. It doesn't really act as a cache like redis does, but it can …
For our use case, we needed a noSQL that would work with AWS Lambdas of specific parts of the internal web applications. We optimized billing and uses , diversified databases for various parts; so it’s not very expensive.
Verified User
Engineer
Chose Amazon DynamoDB
i think both depends on usuability and app requirement
Performance at high scales is better and the cost at high scales is less. If one has a ton of data generated and has to work their way through it, I think Amazon DynamoDB should the go-to database. There are no compromises when it comes to performance at a huge scale. With any …
The Amazon Web Services managed Amazon DynamoDB has excellent features which makes it stand out from all the others in market right now. The management ease it offers is far superior than its competitors and on top of that the on-demand pricing model is an advantage which works …
Amazon DynamoDB supports larger throughput, with better SLA, also, we are considering unstructured data, so Amazon DynamoDB has become the final decision
DynamoDB offers strong consistency, more fine-grained control over read and write capacities, and integrates seamlessly with other AWS services. DynamoDB is designed for horizontal scalability and high throughput, making it a better choice for applications with rapidly changing …
The automation is much more subtle and it performs way better for internet-scale applications. No matter the number of connections, the performance doesn't dip even a bit.
Comparing RDS and Dynamo is not fully Apples to Apples comparison. RDS is a more flexible cloud-native solution that supports a wide range of engines that are relational. It is great for running older DB types like Oracle in the Cloud. Because it supports multiple engines, it …
DynamoDB is slightly different than both the above-stated DBs, with RDS being a relational database and Redshift being a data warehouse used for heavier jobs and analytics and vast data. DynamoDB lies in between both, with it being a no SQL base that can relatively store …
Mongo services are outside of our Vpc and are on a different network. Since most of our infra is on AWS, dynamo by AWS was a natural choice. Most of our engineers are familiar with AWS sdk and the console so that brought in a much smaller learning curve for our engineering team
It seamlessly integrates with Lambda, simplifying the deployment and management of serverless architecture. Both Lambda and DynamoDB are designed are highly scalable. Lambda functions can be triggered by various AWS services and events, such as changes in DynamoDB tables which …
The only direct comparison that I have professionally would be from a past life where we ran Microsoft SQL Server on Microsoft Servers, and while I served as a technical liaison between a vendor and my customers, there were constant issues within my customers' technical teams …
Running MySQL RDS was a simpler solution than running standalone MySQL servers as the semi-managed nature of RDS saved us the need to install, maintain, secure, and backup our database servers. Using MySQL RDS was in addition to running MongoDB Atlas workloads and allowed us to …
MongoDB is nosql database and some clients prefer it. In our presentation we try to persuade them to use RDS with its pros and cons. The type of selection depends upon the actual need.
Amazon Relational Database Service solves part of our architecture problem - more inclined towards on-line transactions and simple user data storage - whereas MongoDB is good for storing structured complex data. For most of the requirements we use Amazon Relational Database …
Because we have our whole architecure on AWS cloud so to provide close connectivity we have choose AWS RDS and also due to Features offered by AWS RDS.
Although the Rackspace service is not comparable, even though it is very good, it requires a lot of administration on my part. Regarding Atlas, although it is not the same as RDS in terms of provisioning and administration panel, I mention it because I found it simpler and more …
We have a strong preference for AWS managed services, and we find that RDS offers excellent integration with various AWS services, making it a seamless choice for our infrastructure. Furthermore, RDS supports integration with automation tools such as Terraform, enhancing our …
Installing, configuring, and managing Oracle Database can be challenging, especially for people who are new to Oracle products. Longer learning curves and higher operational overhead can be caused by this complexity. Amazon Relational Database Service is easy to understand and …
1: If your company is already deeply involved in the AWS ecosystem, such as AWS Lambda, Amazon S3, or Amazon Redshift, leveraging Amazon RDS might result in a more seamless integration of services. AWS offers a broad set of cloud services, which makes it easier to design and …
Every traditional rational Database requires server installation & accessing needs to be monitored periodically manually. But Amazon provides easy-to-access and monitor health and scale-up and scale-down option just by clicks without adding any additional hardware.
It is more suitable for our data structure and also has a lower management and implementation cost since we don`t have to do everything from scratch. It also offers great integration with other AWS services which makes it really good to work with.
As most of our infra is on AWS it is good that we use the same service provider as we want all of our infra to be in a single service provider for ease of maintaining. also, other services target very specific database engines vs RDS comes with lots of options which is …
We prefer RDS to spin up our own MySQL instances via traditional servers, EC2 instances, or containers, and RDS provides all of our DB needs compared to other database products AWS offers. As mentioned, the manageable, operational, security, and reliability features of RDS that …
I've used on-site MSSQL, Oracle, and IBM DB2 as well as MSSQL and postgresql in Azure, and RDS is much easier to setup than any of those aforementioned engines/setups. This includes initial setup, maintenance, security, and configurations. RDS also makes it easy to get …
Atlas is easier to use, but we selected RDS because of its ability to use Postgres and MySQL as opposed to a document database. Amazon offers a document database service, but ultimately it made more sense architecturally to choose a standard database.
amazon provides wide range of support for multiple database engines so we dont have to look for any other providers integration with aws ecosystem so we can seemlessly use other aws services connected to database aws have data points globally so it if data needs to be in …
People use both RDS and Redshift and both allow you to use your traditional database over cloud. But both RDS and Redshift have their own different usages. RDS is particularly suit[ed] for Online Transaction processing systems ( OLTP) whereas, Redshift is used for analytics and …
MS-SQL Server in Azure costs more and/or is slower...but even if you were in a position where the costs were close, you'd still have the fact that Amazon Relational Database Service is more mature and resilient in the Cloud Managed Database environment to tip the scales toward …
It's hard to identify how Amazon RDS stacks up against the databases they support, because to install and use a relational database in a production environment you need a Database Administrator to help install, configure and manage. Amazon RDS keeps the details simple enough …
We mainly used RDS because our infrastructure was already up and running on AWS so the networking between the systems was quite easy to set up and manage. For our Azure infrastructure, we used their SQL database option instead for the same reasons. If AWS made it easier to use …
Amazon Relational Database Service will probably give you everything you need from a traditional manual DB setup, except everything is managed for you. The only downside is having to pay the premium for the service; however, the trade-off of not having to deal with the …
Our other application components are all hosted within Amazon's systems already, and the tight coupling of RDS with the security groups and virtual private cloud offerings made locking down privacy and security much easier than integrating with an outside provider. The deeper …
MySQL is a great for querying related data, but it's unable to store structured data and has a fixed schema. Also SQL can be non-intuitive. DynamoDB, CouchDB and Redis all make querying the data quite difficult and lack important features. The problem CouchDB tries to solve is …
In our early development days we weighed NoSQL databases like MongoDB with RDBMS solutions like MySQL. We were more familiar with MySQL from past experience but also were wary of painful data migrations that slowed down development iterations and increased the risk of outages …
Your default choice should not be MongoDB in my opinion. Most user-facing systems are relational by nature so a well known and reliable SQL database would be easier to maintain and simpler to develop long term. If you highly value speed of development go with Firebase. If you …
It does not belong to certain cloud platforms. MongoDB is an independent program that works with any cloud platform including Amazon Web Services and the Google Cloud Platform. For companies who want to maintain a cloud agnostic structure, MongoDB is a great choice for NoSQL …
We tend to choose MongoDB when we're faced with a particular situation: we know that we need a NoSQL database in general, and want an open-source implementation that allows us to prevent against platform lock-in. Amazon's new DocumentDB product even allows us to choose to use …
MongoDB was the most full-featured NoSQL database we evaluated - that offered atomic transactions at a document level, built-in HA & DR, open source, robust queries, and enterprise level support.
Other platforms had specific parts of what we were looking for - MongoDB had it all.
Features
Amazon DynamoDB
Amazon Relational Database Service (RDS)
MongoDB
NoSQL Databases
Comparison of NoSQL Databases features of Product A and Product B
It’s great for server less and real-time applications. It would be great for gaming and mobile apps. However, if you need relational database and have fixed budget, do not use it. While budget can be managed, you need to be careful. Also this is not a tool for storing big data, there are other wide-column database types you could use for it ins the ad
If your application needs a relational data store and uses other AWS services, AWS RDS is a no-brainer. It offers all the traditional database features, makes it a snap to set up, creates cross-region replication, has advanced security, built-in monitoring, and much more at a very good price. You can also set up streaming to a data lake using various other AWS services on your RDS.
If asked by a colleague I would highly recommend MongoDB. MongoDB provides incredible flexibility and is quick and easy to set up. It also provides extensive documentation which is very useful for someone new to the tool. Though I've used it for years and still referenced the docs often. From my experience and the use cases I've worked on, I'd suggest using it anywhere that needs a fast, efficient storage space for non-relational data. If a relational database is needed then another tool would be more apt.
Automated Database Management: We use it for streamlining routine tasks like software patching and database backups.
Scalability on Demand: we use it to handle traffic spikes, scaling both vertically and horizontally.
Database Engine Compatibility: It works amazingly with multiple database engines used by different departments within our organization including MySQL, PostgreSQL, SQL Server, and Oracle.
Monitoring: It covers our extensive monitoring and logging, and also has great compatibility with Amazon CloudWatch
Being a JSON language optimizes the response time of a query, you can directly build a query logic from the same service
You can install a local, database-based environment rather than the non-relational real-time bases such a firebase does not allow, the local environment is paramount since you can work without relying on the internet.
Forming collections in Mango is relatively simple, you do not need to know of query to work with it, since it has a simple graphic environment that allows you to manage databases for those who are not experts in console management.
It is a little difficult to configure and connect to an RDS instance. The integration with ECS can be made more seamless.
Exploring features within RDS is not very easy and intuitive. Either a human friendly documentation should be added or the User Interface be made intuitive so that people can explore and find features on their own.
There should be tools to analyze cost and minimize it according to the usage.
An aggregate pipeline can be a bit overwhelming as a newcomer.
There's still no real concept of joins with references/foreign keys, although the aggregate framework has a feature that is close.
Database management/dev ops can still be time-consuming if rolling your own deployments. (Thankfully there are plenty of providers like Compose or even MongoDB's own Atlas that helps take care of the nitty-gritty.
It's core to our business, we couldn't survive without it. We use it to drive everything from FTP logins to processing stories and delivering them to clients. It's reliable and easy to query from all of our pipeline services. Integration with things like AWS Lambda makes it easy to trigger events and run code whenever something changes in the database.
We do renew our use of Amazon Relational Database Service. We don't have any problems faced with RDS in place. RDS has taken away lot of overhead of hosting database, managing the database and keeping a team just to manage database. Even the backup, security and recovery another overhead that has been taken away by RDS. So, we will keep on using RDS.
I am looking forward to increasing our SaaS subscriptions such that I get to experience global replica sets, working in reads from secondaries, and what not. Can't wait to be able to exploit some of the power that the "Big Boys" use MongoDB for.
Functionally, DynamoDB has the features needed to use it. The interface is not as easy to use, which impacts its usability. Being familiar with AWS in general is helpful in understanding the interface, however it would be better if the interface more closely aligned with traditional tools for managing datastores.
I've been using AWS Relational Database Services in several projects in different environments and from the AWS products, maybe this one together to EC2 are my favourite. They deliver what they promise. Reliable, fast, easy and with a fair price (in comparison to commercial products which have obscure license agreements).
NoSQL database systems such as MongoDB lack graphical interfaces by default and therefore to improve usability it is necessary to install third-party applications to see more visually the schemas and stored documents. In addition, these tools also allow us to visualize the commands to be executed for each operation.
It works very well across all the regions and response time is also very quick due to AWS's internal data transfer. Plus if your product requires HIPPA or some other regulations needs to be followed, you can easily replicate the DB into multiple regions and they manage all by it's own.
I have only had good experiences in working with AWS support. I will admit that my experience comes from the benefit of having a premium tier of support but even working with free-tier accounts I have not had problems getting help with AWS products when needed. And most often, the docs do a pretty good job of explaining how to operate a service so a quick spin through the docs has been useful in solving problems.
Finding support from local companies can be difficult. There were times when the local company could not find a solution and we reached a solution by getting support globally. If a good local company is found, it will overcome all your problems with its global support.
While the setup and configuration of MongoDB is pretty straight forward, having a vendor that performs automatic backups and scales the cluster automatically is very convenient. If you do not have a system administrator or DBA familiar with MongoDB on hand, it's a very good idea to use a 3rd party vendor that specializes in MongoDB hosting. The value is very well worth it over hosting it yourself since the cost is often reasonable among providers.
The only thing that can be compared to DynamoDB from the selected services can be Aurora. It is just that we use Aurora for High-Performance requirements as it can be 6 times faster than normal RDS DB. Both of them have served as well in the required scenario and we are very happy with most of the AWS services.
Amazon Relational Database Service (RDS) stands out among similar products due to its seamless integration with other AWS services, automated backups, and multi-AZ deployments for high availability. Its support for various database engines, such as MySQL, PostgreSQL, and Oracle, provides flexibility. Additionally, RDS offers managed security features, including encryption and IAM integration, enhancing data protection. The pay-as-you-go pricing model makes it cost-effective. Overall, Amazon RDS excels in ease of use, scalability, and a comprehensive feature set, making it a top choice for organizations seeking a reliable and scalable managed relational database service in the cloud.
We have [measured] the speed in reading/write operations in high load and finally select the winner = MongoDBWe have [not] too much data but in case there will be 10 [times] more we need Cassandra. Cassandra's storage engine provides constant-time writes no matter how big your data set grows. For analytics, MongoDB provides a custom map/reduce implementation; Cassandra provides native Hadoop support.
I have taken one point away due to its size limits. In case the application requires queries, it becomes really complicated to read and write data. When it comes to extremely large data sets such as the case in my company, a third-party logistics company, where huge amount of data is generated on a daily basis, even though the scalability is good, it becomes difficult to manage all the data due to limits.
Some developers see DynamoDB and try to fit problems to it, instead of picking the best solution for a given problem. This is true of any newer tool that people are trying to adopt.
It has allowed us to add more scalability to some of our systems.
As with any new technology there was a ramp up/rework phase as we learned best practices.
Open Source w/ reasonable support costs have a direct, positive impact on the ROI (we moved away from large, monolithic, locked in licensing models)
You do have to balance the necessary level of HA & DR with the number of servers required to scale up and scale out. Servers cost money - so DR & HR doesn't come for free (even though it's built into the architecture of MongoDB