AWS vs Azure Database Service Comparison

AWS and Azure are the market leaders for their database services over the cloud. Let’s have a detailed comparison of AWS vs Azure database services!

The role of public cloud in redefining the approaches followed by enterprises for hosting, management, and scaling of their database operations is highly prominent in present times. Now, enterprises don’t have to worry about the concerns of scalability that were evident in on-premises systems. 

Now, it is easier for them to provision new infrastructure with just a single click and that too, without going through a lengthy and troublesome procedure for procuring hardware. However, organizations have to constantly face the issue of selecting from the top players, AWS and Azure.

Therefore, the debate on AWS vs Azure database services is one of the notable points of attention for many enterprises. Even when the organizations don’t make large and upfront capital investments, they still find “value”. 

Understanding AWS vs Azure Database Services

The facility of different Database as a Service (DBaaS) offerings by the renowned public cloud vendors definitely provide advantages. Enterprises could now ensure the migration and maintenance of their databases. According to Gartner, enterprises should adopt cloud databases as the preferred model for the deployment of all new business applications, processes, and workloads. However, the differences between AWS vs Azure for database services create formidable confusion for enterprises. 

The following discussion reflects on the major highlights of the AWS vs Azure database battle. A comparison of the crucial features of the database services of the AWS and Azure can help you choose the right alternative according to your requirements. The comparison would follow key features in the database services of both AWS and Azure, such as types of databases, querying capabilities, and the underlying infrastructure.     

  • Relational Database Offering

The first point in the AWS vs Azure database comparison refers to the facility of a Relational DBaaS offering. You can notice that the dedicated DBaaS offering by AWS is the Relational Database Service (AWS RDS). On the other hand, the dedicated DBaaS offering of Microsoft Azure is the Azure SQL Database. Both the competitors exert equal strengths in this competition and provide exceptional functionalities. 

For example, both the services are capable of automatic replication alongside providing higher availability and durability. In addition, both services offer automated backups. Therefore, it is clearly evident that we don’t have a sure winner in this stage of the AWS vs Azure database comparison.

  • Supported Database Engines

The next pointer in the Azure vs AWS data services comparison is the support for database engines. The Relational Database Service (RDS) of AWS provides support for six database engines. These database engines are Amazon Aurora, MariaDB, Microsoft SQL Server, MySQL, PostgreSQL, and Oracle. 

However, in the case of Azure’s SQL Database, you will find support only for Microsoft SQL Server. The SQL Database service relies exclusively on the Microsoft SQL Server. The comparison of AWS and Azure is one-sided, especially when you consider this information. Furthermore, AWS also hosts the PostgreSQL, Microsoft SQL Server, MariaDB, Oracle, and MySQL database engines on Elastic Block Store (EBS) volumes. 

Since Amazon Aurora is the proprietary database engine of AWS, it utilizes a unique storage infrastructure. The storage infrastructure of Aurora can help in addressing different issues regarding scaling and replication in the case of traditional databases. Therefore, we can notice AWS as a clear winner in the AWS vs Azure database comparison in this category.

  • Resource Allocation

The third pointer for comparison in the Azure SQL Database vs AWS RDS battle refers to the allocation of resources. First of all, RDS works just like EC2 and uses the concept of instances for the allocation of computing resources to databases. Thereafter, users can ensure the provisioning of storage capacity distinctively according to their requirements. 

However, you should also notice that storage scalability is automatic with Amazon Aurora, and you have to pay only for the storage you consume. On the other end of the spectrum in the AWS vs Azure database battle, the SQL database follows a tier system. The tier system involves specific tiers for various types of workloads, including small-scale development or testing environments, as well as mission-critical applications. 

Every tier has different performance levels, and every performance level has a ranking. The ranking of the performance levels follows Microsoft’s unique unit of measure for resource capability, the Database Transaction Unit (DTU). Microsoft Azure also offers a Single Database pricing model and has recently brought Elastic Pools for users. 

The Elastic Pools service allows users to leverage a collective resource for hosting databases. As a result, users could resolve issues related to fluctuations in load. How? Elastic Pools allow for spreading resources throughout multiple databases, thereby improving utilization alongside ensuring cost reduction. Now, you can clearly note why Azure is better than AWS for database when it comes to resource allocation.

Also Read: Factors Affecting Disk Space Requirements for SQL Server

  • Scalability

The next crucial pointer for comparison between Azure SQL Database vs AWS RDS is scaling. Scalability is one of the foremost reasons for which enterprises prefer to migrate to the cloud. Therefore, outcomes of the comparison in terms of scalability have a very crucial role in determining the ideal cloud database service. 

Users should note that vertical scalability of an RDS or SQL Database deployment is possible through a simple API call and even the vendor’s console. However, SQL Database provides the facility of scaling up through the “Transact-SQL ALTER DATABASE” statement. AWS has different pricing for storage and compute. 

Standard RDS is capable of providing maximum storage capacity of 64TB. You should note that RDS does not provide automatic resizing capabilities. However, Aurora presents a flexible option alongside providing automatic scalability in the form of increasing 10GB. Aurora allows for a maximum storage capacity of 64TB. 

On the other end of the AWS vs Azure database comparison, the SQL Database includes the price of storage in the price of the tier and performance level you select. The SQL database, however, allows a maximum database capacity of 1TB alongside maximum total storage of 2.9TB for every elastic pool. 

It is also important to remember that individual limits are applicable in the case of each tier. Another notable point of comparison between AWS vs Azure database in terms of scalability is that RDS provides support only for read-only horizontal scaling. Therefore, users can add replicas to the improvement of query performance. On the other hand, Microsoft Azure follows a sharding approach with the Elastic Database tools it has. 

  • Target Customers

Now, the focus of Azure vs AWS data services comparison should move towards the target customers for each of them. In the case of Microsoft Azure, the primary target for SQL Database refers to business applications of the enterprise which utilize databases having a maximum capacity of 5GB. On the other hand, RDS presents better flexibility and targets a larger segment of users. In the case of Azure, users could get a maximum database capacity of 10GB. 

However, RDS permits for maximum storage of 1TB for every database instance. As discussed just now, Microsoft recommends sharding your data in the case of exceeding the capacity of 10GB. Regardless of the effort, you can notice why Azure is better than AWS for database services. The sharding of data across multiple servers helps in better scaling as compared to having all the data on a single, congested server. 

  • Support for the Cloud

Support for the cloud platform is also another crucial point of comparison in the AWS vs Azure database debate. SQL Database of Azure is basically native to the cloud platform. Actually, it has been designed with the cloud platform in mind, thereby exercising a formidable advantage. On the other hand, the Amazon RDS has to provide the MySQL database engine. As a result, you can notice that the SQL Database of Microsoft Azure is specifically ideal for the explicit utilization of any resource available on the cloud. 

  • Deployment

The deployment of AWS and Azure database services is also another notable pointer for comparing Azure SQL Database vs AWS RDS. As you must know by now, SQL Database does not follow the concept or server or database instances like RDS. Therefore, the servers created with SQL databases are primarily logical containers. The provisioning of the containers is exclusive to the user, and the containers would host only the user’s databases. 

In addition, users should remember that a physical node on the cloud and the servers created by users have a one-to-many relationship. As a result, you can host multiple servers created by different users on a single hardware platform in shared environments. Cloud enthusiasts might recognize this as the multi-tenant architecture. 

In the case of Microsoft Azure, you can find the advantage of the low pricing of the SQL Database. On the other hand, you have to face the setback of limits on tailoring a system to achieve higher performance. In addition, the 10GB limit in SQL Database requires users to make certain changes in their design. For example, a user may have to implement design decisions for applications that need larger databases like the splitting of data between multiple databases. 

The weight of AWS in this category of AWS vs Azure database comparison shifts to a completely different level. Both AWS and Azure follow the multi-tenant architecture for deployment. However, the difference in AWS architecture is that it follows the concept of instances. RDS ensures provisioning of a specialized EC2 instance for each AWS account. Then, users can create different, highly variable MySQL instances on the EC2 instance you have. 

The differences between database instances are evident in terms of storage and computing resources. Users can define the database instances with storage capacity privileges ranging up to 1TB and computing resources in the maximum limit of almost 26 ECUs and 68GB capacity of RAM. Most important of all, AWS RDS presents better flexibility to users with the allocation of complete control over database parameters. 

  • Compatibility with Existing Systems

The compatibility with existing systems is also a prominent point for AWS vs Azure database comparison. As of now, SQL Database provides support only for a particular subset of features accessible with the SQL Server. Amazon RDS presents comprehensive support for all features of MySQL, although without including replication. 

If you are using MySQL, then you will find that your applications would work seamlessly with Amazon’s RDS. On the other hand, SQL Database features support Transact-SQL alongside existing libraries such as ADO.NET, PHP, and ODBC for connection. 

  • Pricing and Value

One of the notable factors that should not escape an Azure SQL Database vs AWS Aurora comparison is the cost. In addition, it is also important to know that you are getting the appropriate value for the price you invest in. On a general basis, you can find that Azure is a winner in the AWS vs Azure database comparison when we consider AWS pricing and Azure pricing. Why? Azure pricing makes Microsoft Azure’s SQL Database a comparatively cheaper option than RDS or Aurora. 

The most credible feature of Azure’s SQL Database is the automatic replication of databases across multiple systems that provide read scale-outs alongside a transparent fail-over mechanism to deal with hardware failure. On the contrary, Amazon RDS disables replication on all MySQL instances specifically. 

Furthermore, SQL Database of Azure does not provide anything like the on-demand snapshot-based backup service that you can find with Amazon RDS. However, data in Azure’s SQL database follows automatic backup and provides restoration in the event of a disaster. The user can observe the process transparently, thereby assuring the promise of higher availability as implied by Azure SQL Database. 

  • NoSQL DBaaS Offerings

The comparison in AWS vs Azure database comparison also extends to the facility of NoSQL DBaaS on the platforms. Presently Amazon DynamoDB is the only NoSQL DBaaS of Amazon  Web Services (AWS) that helps in the storage of data at scale. On the other hand, Microsoft Azure provides two distinct NoSQL DBaaS products, such as Table Storage and DocumentDB. A reflection on the NoSQL DBaaS offerings of AWS and Azure could strengthen this discussion further. 

  • NoSQL DBaaS Database Models

The foremost comparison of AWS vs Azure database comparison for NoSQL DBaaS offerings should be for database models. Both DocumentDB and DynamoDB follow the document store database model and are the same as open-source solutions such as CouchDB and MongoDB. This factor in the design of DocumentDB and DynamoDB helps in accepting JSON structures.

On the other hand, Table Storage is a key-value store and works just like Couchbase and Redis. So, it uses a database model similar to DocumentDB and DynamoDB, albeit without any limits on document structure. So, Table Storage is ideal for data storage rather than query or processing tasks. In addition, Table Storage also assures better data consistency by returning the latest version of your data. 

It is specifically ideal for systems that involve concurrent users simultaneously accessing and updating the same information. In the case of DynamoDB, users can specify the needs for consistency, such as eventually consistent or highly consistent reads. DocumentDB provides four options for consistency, such as eventual and strong consistency alongside two intermediate levels with a compromise for alternative consistency. 

  • Scaling of NoSQL DBaaS Offerings

The comparison between AWS and Azure for database services in NoSQL DBaaS offerings also focuses on scaling. It is essential to note that DocumentDB and DynamoDB offer virtually infinite scalability. The Table Storage service has limits amounting to a maximum capacity of 500TB for every account. Users do have the privilege of extending the capacity by partitioning of data objects across multiple accounts. 

Table Storage is presently the only DBaaS offering that has the native auto-scaling feature. Therefore, you can observe why Azure is better than AWS for database services. Readers should also note that third-party solutions can help in automatic monitoring and scaling of DocumentDB and DynamoDB. 

  • Data Warehouse

Another critical pointer in the comparison between AWS vs Azure database services is the facility of warehouses. Amazon’s default data warehousing solution is RedShift, while the data warehouse of Azure is SQL Data Warehouse. Both of the data warehousing solutions are SQL-centric. However, they can also utilize big data technologies to their advantages such as data compression, massively parallel processing (MPP), and column-based storage. 

The capability for leveraging these big data technologies helps in enabling faster query performance according to scale. Resource allocation in the case of AWS RedShift is possible through the configuration of resources as a cluster of nodes or database instance types. Users can choose from four different types of nodes, among which two are Dense Compute instances, and the other two are Dense Storage instances. 

Users can fine-tune their infrastructure by selecting an ideal balance between the type of instance and a number of nodes. On the other hand, SQL Data Warehouse works in a different way as it ensures the decoupling of computing and storage. We have already shed light on this in our discussion with reference to the use of Database Transaction Units for allocation of resources for Relational Database offerings.

AWS vs Azure Database Comparison Table

You can find an abridged version of all the comparisons in this discussion in the following table. The AWS vs Azure database comparison becomes easier by focusing on the highlights of the differences between them. 


Microsoft Azure


Relational Database

SQL Database

Relational Database Service (RDS)

Support for Database Engines

Microsoft SQL Server

Amazon Aurora, MySQL, Microsoft SQL Server, PostgreSQL, MariaDB, and Oracle

Resource Allocation

SQL Database follows a tier system with each tier capable of addressing different workloads. All tiers further have different performance levels graded according to the measure of resource capability i.e. Database Transaction Unit (DTU)

Follows the concept of instances for the allocation of compute resources to databases. Aurora provides automatic storage scalability.


Scaling is possible through vendor’s console and simple API call as well as a “T-SQL ALTER DATABASE” statement. Storage pricing comes along with the selected tier and performance level.

Scaling is possible only through simple API call and vendor’s console. The pricing for storage is calculated differently from the Compute services. RDS does not provide the automatic resizing capability. Aurora can provide automatic scaling in margins of 10GB till reaching a maximum storage capacity of 64TB.

Target Customers

SQL Database is suitable for applications using databases with a capacity of less than 5GB. However, users can share data across multiple servers for better scaling in case of requirements exceeding the limit of 10GB.

Amazon RDS is highly flexible and suitable for a larger segment of users. RDS also provides the facility of the maximum storage capacity of around 1TB for every database instance.

Support for the Cloud

Azure’s SQL Database was designed especially with the cloud in mind. Therefore, SQL Database helps in utilizing any resources available on the cloud explicitly.

Amazon RDS provisions only MySQL thereby establishing the flexibility of its use on the cloud.


SQL Database doesn’t follow the concept of database or server instances. Servers on SQL Database are actually logical containers provisioned exclusively for specific users. The physical nodes in the cloud and servers created by users have a one-to-many relationship.

Amazon RDS also follows the multi-tenant architecture like SQL Database. However, RDS ensures provisioning of a particular EC2 instance for each AWS account. Then, users can create different, highly variable MySQL instances on the EC2 instance. Users also have comprehensive control over the database parameters.

Compatibility with Existing Systems

SQL Database provides support only for a specific subset of features found with SQL Server only.

AWS RDS provides comprehensive support for MySQL features, only with the exclusion of replication. If you are using MySQL presently, then applications will work smoothly with RDS.


SQL Database is a cheaper alternative with a facility for automatic replication of databases across multiple systems along with transparent fail-over mechanisms and multiple systems to provide for read scale-out. The transparent fail-over mechanism ensures higher availability.

Amazon RDS is costly. However, it also provides a unique feature of on-demand snapshot-based backup.

NoSQL DBaaS offerings

DocumentDB and Table Storage


Warehousing Facility

SQL Data Warehouse


AWS vs Azure Database Comparison – Final Thoughts

So, if you are looking for the best choice in the AWS vs Azure database comparison, then you have to search harder. The technical aspects of the DBaaS options that you are considering also matter significantly for making the right choice. For instance, users should find out the security and compliance concerns, alignment of the service with application architecture, and supported programming languages. 

Furthermore, you could also find out the better option in the AWS vs Azure database comparison by reflecting on the approach of each vendor to service delivery. For example, Microsoft provides hands-on product support and guidance through its sales representatives. However, in the case of AWS, you might have to rely on self-service. So, try every service and make a reasonable decision!                       

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