Calculating the Total Cost of Ownership for MySQL Management

Most organizations rarely conduct cost analysis for their databases, even though it can reveal valuable insights. A simple yet powerful way to understand database costs is through a Total Cost of Ownership (TCO) analysis. You may have assumptions about your biggest cost driver – but are they accurate?

Why should you run a TCO analysis? Like any good research, it challenges your assumptions and uncovers hidden costs. It’s a precise tool that often delivers surprising insights, helping you optimize spending and make smarter decisions.

Cost Factors for Databases

Database costs fall into two main categories: capital expenses (CAPEX) and operational expenses (OPEX). Both play a role throughout the infrastructure lifecycle, affecting long-term cost efficiency and scalability.

Capital Expenses (CAPEX)

CAPEX covers upfront costs during the acquisition phase, including:

• Hardware purchases (servers, storage, networking equipment)
• One-time licensing fees
• Replacement parts (though these occur later in the maintenance phase)

These costs remain constant in a Total Cost of Ownership (TCO) analysis and are spread over the lifespan of your database servers – typically three or more years. While the initial investment seems high, it often pays off over time.

Operational Expenses (OPEX)

OPEX includes recurring costs for running database servers, primarily during the maintenance phase:

• Data center or rack rental
• Power and cooling
• Network usage
• Personnel costs (SysOps, DBAs, training, office space)

Since OPEX grows over time, the longer you run your database servers, the larger its share in your overall costs.

Cloud Hosting Considerations

In the cloud, CAPEX is significantly lower because there’s no hardware to buy upfront. Instead, OPEX is higher due to monthly infrastructure costs. Some providers charge setup fees – these should be considered CAPEX.

Over time, the balance between CAPEX and OPEX shifts. A high upfront investment in hardware may seem costly but can be more economical in the long run compared to ongoing cloud expenses.

Example Calculation for Hardware

In this example, we will calculate the TCO for a small company (under 100 employees) that hosts its own hardware in a data center.

This company has two dedicated SysOps and one DBA with 1-4 years of experience. The DBA manages around 20 databases, while the SysOps handle about 200 hosts and oversee data center operations.

• The average DBA salary is $105,000, meaning the annual cost per database is $5,250.
• The SysOps earn an average of $65,000 and cost $325 per host per year.
• For simplicity, we will exclude facilitation costs (e.g., desks, office space) as they would make the calculation overly complex.

Example Cluster Setup:

We will use a three-node MySQL replication setup consisting of one primary and two replica nodes.

• Hardware: Dell R750 with 64GB memory and four 2TB SSDs, a popular model for this use case.
• Current price per server: $11,817.

Colocation Costs:

• Rack rental: A full rack costs $900 per month, making the colocation cost per U approximately $21 per month.
• Since the R750 is a 2U unit, the total colocation cost for our three servers is $126 per month.

Power Costs:

Modern colocation pricing separates power consumption, as it is a variable factor. Power prices vary but currently average $0.25 per kWh.

• The average database server consumes 300W, translating to 216 kWh per month per server.
• For three servers, this results in $162 per month in power costs.

This results in the following TCO:

Cost item	CAPEX	OPEX (per year)	TCO (3 years)
Purchase: hardware	$35,451
Professional support (DBA / Sysop)		$16,725
Colocation cost		$1,512
Power cost (200W)		$1,944
Replacement parts	$2,000
Total	$37,451	$20,181	$97,994

From this calculation, we can draw a few key conclusions:

• Colocation, power, and replacement parts have a minimal impact on the total cost compared to other factors.
• Support costs (DBA and SysOps salaries) make up more than half of the total expenses over the server’s lifetime.
• These support costs far exceed the initial hardware purchase price, emphasizing that labor is the dominant factor in long-term database management.

Example Calculation Cloud Hosting

In this example, we will calculate the TCO for a company hosting its database in the cloud. To ensure a fair comparison, we will again use a three-node MySQL replication setup, but this time on Amazon EC2.

Amazon provides a TCO calculator, which we used as the basis for the calculations below.

Cloud Infrastructure Setup

To match the specifications of the on-premises setup, we chose the i3.2xlarge instance type, which offers:

• 8 vCPUs
• 61GB of RAM
• 1.9TB of NVMe SSD storage

The current cost for this instance is $0.686 per hour, which translates to:

• ~$16.50 per day
• ~$6,010 per year (per instance)

Key Differences in Cloud TCO

Unlike on-premises hosting, there are no upfront CAPEX investments since hardware purchases are not required. However, reserved instances in AWS allow businesses to prepay for capacity, which reduces long-term costs. In this calculation, we assume on-demand pricing, meaning no reserved instances.

Additionally, OPEX should be lower since SysOps are not responsible for physical hardware installation or maintenance in a data center.

This results in the following TCO:

Cost item	CAPEX	OPEX (per year)	TCO (3 years)
Professional support (DBA only)		$15,750
AWS 3x i3.2xlarge		$18,030
Total	$0	$33,780	$101,340

While eliminating upfront costs and capital investments (CAPEX) is an advantage, the operational expenses (OPEX) in the cloud are significantly higher due to the premium pricing of high-performance AWS instances.

Over a three-year period, the TCO for cloud hosting will exceed the cost of owning your own hardware, making it a more expensive long-term option. The convenience and scalability of the cloud come at a cost, which organizations must weigh against their infrastructure and financial strategy.

OPEX has a Large Influence

These calculations highlight a key takeaway: operational costs (OPEX) over the server’s lifetime far exceed the initial capital investment (CAPEX). This is largely because organizations run and maintain their servers for multiple years.

Key Observations:

• When owning hardware, operational costs eventually surpass the upfront purchase cost of the servers.
• In the AWS example, the total cost of “owning” cloud-based instances is even higher than running on-premises hardware. This is the premium paid for flexibility, as the cloud allows you to upgrade to newer instances as needed.
• Professional support costs for managing databases are significant in both scenarios.
• SysOps scale efficiently when managing over 200 hosts, as they don’t handle DBA-specific tasks.

The real question is: how can we make DBAs more efficient? Reducing their workload or optimizing their tasks could significantly impact overall costs.

Making the DBA more Efficient

Fortunately, there are ways to increase DBA efficiency and enable them to manage more databases. The two main approaches are:

1. Offloading tasks to others (e.g., SysOps or automation tools).
2. Reducing manual work through automation.

The quickest wins come from automating time-consuming or error-prone tasks.

Time-Consuming DBA Tasks That Can Be Automated

• Provisioning & Deployments – Repetitive setup tasks can be scripted.
• Performance Tuning & Troubleshooting – Automated monitoring tools can detect and resolve issues faster.
• Backups & Restores – A backup manager can automate both backup execution and recovery.
• Scaling Clusters – Adding read replicas and setting up replication can be streamlined.

Error-Prone DBA Tasks That Benefit from Automation

• Replication Setup – A single typo can cause significant issues.
• Failover Handling – Automating failover prevents downtime and human mistakes.
• Schema Management – Controlled automation ensures safe schema changes.

The Challenge of Automation

While automation saves time in the long run, initial implementation requires effort. A DBA focusing on automation might become busier than before, especially if they are not experienced in scripting or development.

Instead of expecting DBAs to build their own automation, why not provide them with a ready-to-use toolset? Or better yet, enable SysOps to take over certain tasks, reducing the DBA’s workload while maintaining efficiency.

Do you even Need a DBA?

Not all companies have full-time DBAs, especially if they only manage a small number of databases. The DBA role is highly specialized, requiring deep expertise in:

• Hardware and OS tuning
• Database engines and SQL
• Replication, clustering, and high availability

For companies with just a few databases, hiring a dedicated DBA may not be cost-effective, as they would have little to do while still commanding a high salary.

The SysOps Alternative

A SysOp (or System Administrator), on the other hand, is a generalist responsible for:

• Hardware, operating systems, and networks
• Security and application management
• Storage and database administration

While SysOps may have experience with databases, they lack deep expertise in distributed setups, replication, and high availability. The salary gap between DBAs and SysOps reflects this skill difference – DBAs cost more and are harder to find.

The Challenge for SysOps

Without a DBA, SysOps must take on database management, which adds to their already broad set of responsibilities. Their key challenges include:

• Keeping databases performing well
• Troubleshooting issues efficiently
• Monitoring anomalies before they become failures
• Maintaining high availability
• Ensuring data integrity and backups (and verifying restorability)

This extra workload can quickly overwhelm a SysOp, especially in complex environments. The question then becomes: how can we bridge the gap between SysOps and DBAs without hiring specialized talent?

ClusterControl Saving Costs

With ClusterControl, the entire database lifecycle is already automated for the most popular open-source databases. This eliminates the need for a DBA to build and maintain custom automation, as ClusterControl handles it out of the box.

Key Cost-Saving Benefits:

• Eliminates the need for custom automation – DBAs no longer have to spend time scripting and testing automation for deployments, scaling, failover, and backups.
• Increases reliability – Unlike DBA-built scripts that are often tested directly in production, ClusterControl’s automation is battle-tested and proven to work.
• Reduces workload on DBAs – They can focus on higher-value tasks instead of repetitive maintenance.
• Empowers SysOps and generalists – Even teams with limited database expertise can now handle complex database operations with ease.

By implementing a full lifecycle management tool like ClusterControl, businesses reduce reliance on specialized DBA skills while improving efficiency and reliability. Whether it’s enabling DBAs to manage more databases or allowing SysOps to take on database management, ClusterControl makes database operations more cost-effective.

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