Best Practices for Scaling A Multi-Cloud Database Infrastructure

Ashraf Sharif


Man with glasses looking scared due to the challenge of scaling a multi-cloud database infrastructure.

Scaling a multi-cloud database infrastructure can be a complex and challenging task, but it’s essential for businesses that require high scalability, availability, and reliability.

With multi-cloud becoming increasingly popular, organizations are leveraging the benefits of cloud providers to achieve better performance, flexibility, and cost savings. 

However, scaling a multi-cloud database infrastructure requires careful planning, execution, and monitoring to ensure optimal resource utilization, data privacy, and security. 

Before you get started, it’s important to understand the main challenges of scaling a multi-cloud database infrastructure, as well as a few best practices that you’ll need to keep in mind.

We’ll cover all of this, plus the key database design considerations and techniques for auto-scaling, load balancing, and monitoring.

Let’s dive in.

Challenges of scaling a multi-cloud database infrastructure

Multi-cloud architectures come with a number of challenges. It’s no surprise, then, that scaling a multi-cloud database infrastructure also presents its own set of difficulties that need to be understood and overcome.


The first one is complexity. Managing a multi-cloud database infrastructure can be complex and challenging due to having to deal with multiple vendors, services, and APIs. It requires expertise in managing and configuring multiple database systems and ensuring they work together seamlessly.

As you increase the complexity of the environment and infrastructure, you get a higher probability that something unexpected happens. This, again, tends to add even more complexity – you want to have systems redundant to survive unexpected scenarios. Redundancy adds to complexity.


The next challenge is cost. Multi-cloud database infrastructure scaling can be costly, especially if not managed properly. Additional costs may be associated with data replication, data transfer, and data storage across multiple clouds.

We have mentioned complexity, which requires expertise. Expertise is not easy to find on the market and if you find it, it comes with a price tag, adding to the cost.


Ensuring consistent security policies and access control across multiple clouds can also be challenging, especially when dealing with different vendors and services.

Every infrastructure provider has specific details that make them unique. These idiosyncrasies make it difficult to design security solutions that fit all providers at once. What’s more, any required changes down the road can potentially pose security risks if you haven’t taken every unique detail into account.


Performance issues can arise when scaling multi-cloud database infrastructures. The increased complexity of the infrastructure can cause delays in data access and transfer times.

Downtime in one cloud can impact the entire infrastructure, leading to significant disruptions in business operations. This could lead to a chain reaction with data synchronization issues, especially when data is updated on multiple clouds simultaneously.

Best practices for scaling a multi-cloud database infrastructure

Whether you need to scale because of a sudden spike in activity or a more gradual increase in workload, there should be some kind of plan in place for how you will address the scenario. There are a few best practices to keep in mind when putting that plan together.

Let’s take a look at them one by one.

Choose the right cloud service providers

First and foremost, you’ll need to choose cloud providers that are compatible with each other and suitable for your business needs. This includes evaluating each cloud provider’s cost, performance, security, and scalability.

Some cloud providers have a specific market segmentation they are targeting. For example, if you just need a simple and straightforward cloud solution for your developers, you could start with Digital Ocean.

If you are dependent on Microsoft or Windows services like Microsoft Exchange, .NET framework, or Microsoft SQL Server, it is probably best to use Microsoft Azure Cloud solutions.

Establish a centralized management platform

Use a centralized management platform to manage and monitor your multi-cloud database infrastructure. This will allow you to view and manage all your databases and cloud resources from a single location.

For database-as-a-service, ClusterControl can manage database clusters across multiple cloud providers. The currently supported cloud providers are Microsoft Azure, Google Cloud Platform, and Amazon Web Services.

You can choose the traditional management approach via SSH (which can be achieved by exposing cloud instances or using site-to-site tunneling) or you can use the native cloud support via the cloud provider’s API and CLI tools.

ClusterControl allows you to standardize APIs to ensure compatibility across multiple clouds with its CMON RPC interface. This will enable you to manage and access your databases consistently across all your clouds.

Automate where possible

Another best practice when running on a multi-cloud infrastructure is to make use of automation tools, software, scripts, or processes to streamline your operations and reduce the risk of human error. This includes automating database monitoring, alerting, backups, scaling, and failover processes.

Monitor your multi-cloud database infrastructure closely and set up alerts for critical events. Proactive monitoring will help you identify and address issues before they become major problems.

Use replication and synchronization tools

Data consistency is essential when running on multi-cloud infrastructure.

Use replication and synchronization tools to ensure data consistency across multiple clouds. This includes ensuring that all your databases are in sync and that changes made in one cloud are reflected in all other clouds.

Optimize your databases for performance by using cloud-native services such as load balancers and auto-scaling. This will help you ensure high availability and performance across multiple clouds.

By following these best practices, you can help ensure that your multi-cloud database infrastructure scales efficiently, securely, and with optimal performance.

6 ways to scale your multi-cloud database infrastructure

Multi-cloud database infrastructure can be scaled in different ways to accommodate growing data needs, user traffic, and performance requirements. By implementing these scaling techniques, you can scale to meet business demand while maintaining optimal performance, availability, and reliability.

1. Vertical scaling

Here are six options for how to scale a multi-cloud database infrastructure.

Vertical scaling involves increasing the computing power of individual database servers by adding more CPU, memory, or storage. This can be done by upgrading the hardware of the servers or by moving to more powerful cloud instances.

Utilizing multiple cloud providers can become quite useful as different cloud vendors make different tiers of instances available. It may happen that, while you have maxed out in one cloud, you still may have an option to scale out in another one.

2. Horizontal scaling

Horizontal scaling involves adding more database servers to a cluster to distribute the workload across multiple servers. This can be done by adding more nodes to the database cluster or by deploying more instances of the database in different availability zones or cloud regions.

Multi-cloud deployments benefit from flexibility. Cloud is not infinite, no matter what people may expect. If you need to scale out promptly, given region limitations, you may run out of available resources in a particular high availability zone or region.

In that case, if you have another cloud provider available that has resources located closely, you can utilize those instances to scale out your environment.

It is very common to have a reverse proxy tier on top of the database tier to take advantage of horizontal scaling. A reverse proxy can act as a gateway, balancer, router, and firewall to the database service as it scales in size.

3. Sharding

Sharding involves splitting a large database into smaller shards or partitions, each of which is stored on a separate database server. This allows the workload to be distributed across multiple servers and can improve query performance, especially if your application supports parallelism or is write-intensive.

With sharding, it is possible to bring the data closer to the users by splitting the data across the shards using geographical designation (country codes, postal codes, IP addresses etc). In a multi-cloud environment, it is easier to build the data infrastructure closer to the users. For example, if one of your cloud providers does not have a data center in particular location, it is quite likely that some other CSP will have it.

4. Replication

Replication involves creating copies of a database on multiple servers in different cloud regions. This improves read performance by allowing users to read from the copy of the database closest to them. It also provides live backup and redundancy in case the primary database server is unavailable.

Just like with sharding, using multiple cloud providers lets you bring the data closer to your users.

5. Cloud-native services

Cloud-native services such as load balancers, auto-scaling, and caching can help improve the scalability of multi-cloud database infrastructure. Load balancers can distribute traffic across multiple servers, while auto-scaling can automatically add or remove servers based on demand.

Additionally, caching tier can be used to cache the most frequently accessed data to offload the database instances.

6. Hybrid cloud

A hybrid cloud approach can be used to scale multi-cloud database infrastructure. This involves using a combination of public cloud and private cloud resources to scale the database infrastructure as needed.

There are many reasons why you may want to go hybrid, including data protection laws that require PII to be stored in a particular location or country. Having the ability to span your infrastructure not only across multiple public clouds, but also on-premises, may make your life easier when you’ll have to deal with this type of regulatory requirement.

6 steps to a multi-cloud database infrastructure with auto-scaling

You can achieve a multi-cloud database cluster with auto-scaling, which can help improve your database infrastructure’s scalability, availability, and reliability while reducing costs and improving performance. 

Achieving a multi-cloud database cluster with auto-scaling involves six key steps.

1. Choose the right database system

Choose a database system that supports multi-cloud deployment and auto-scaling. Popular options include MySQL, PostgreSQL, MongoDB, and Cassandra.

Which one to choose depends on your workload and the type of data that you want to have stored and processed. This is an important step that will determine the ease of management and the performance of your database tier – make sure you make a good, well-thought decision.

2. Select compatible cloud providers

Opt for cloud providers that support the chosen database system and suit your business needs. This includes evaluating the cost, performance, security, and scalability of each cloud provider.

Verify what kind of interconnectivity is available for each of them – you will have to ensure that the traffic flows between them uninterrupted. You should also take into consideration the cost of that traffic.

3. Deploy your database nodes

Deploy database nodes across multiple clouds in different regions. Ensure that the database nodes are configured to communicate with each other and are synchronized with the latest data.

Keep an eye on the latency as network and latency that comes with keeping the data in multiple geographical locations is one of the important factors that affects the stability and performance of the multi-cloud environments.

4. Configure a load balancer

Configure a load balancer to distribute traffic across the database nodes. This can be done using cloud-native load-balancing services or by using third-party load-balancing solutions.

No matter what you will choose, make sure that load balancers are configured properly and the load balancing tier is highly available. If you use cloud-native services this is probably out of the box. For external, third-party software, you may have to take extra steps.

5. Enable auto-scaling

Enable auto-scaling on the database nodes to automatically add or remove nodes based on the workload. This can be done using cloud-native auto-scaling services or by using third-party auto-scaling solutions.

Make sure that you test and tune the auto-scaling solution to your liking. Do you expect a slow, gradual increase in the load? Do you predict fast and sudden spikes of load? Different scenarios may require different configurations and designs.

6. Monitor and optimize your cluster

Monitor the database cluster closely and optimize it for performance and cost. This includes setting up alerts for critical events, optimizing database queries, and scaling up or down based on workload patterns.

Monitoring the data tier is critical – databases are crucial for the data availability and the data availability is a requirement for any business to be able to operate.

Wrapping up 

Scaling a multi-cloud database infrastructure is crucial for businesses that require high scalability, availability, and reliability. However, it is complex and requires careful planning, execution, and monitoring for optimal resource utilization, data privacy, and security.

To efficiently scale, you must consider best practices such as database design, auto-scaling, load balancing, and monitoring to ensure efficient, secure, and optimal performance scaling.

If you’re considering a multi-cloud approach, make sure you’re aware of the common challenges and how to address them. Despite the challenges, organizations leverage the benefits of multi-cloud architecture for better performance, flexibility, and cost savings.

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