Build or Buy Data Pipelines? The Only Guide You’ll Need (+Detailed TCO Calculation)

“Why build when you can buy?”

To be honest, that is a valid question. Why spend so much time and resources building your data architecture when you can leverage well-engineered tools? The idea seems good at first—you’d be in charge of everything, be able to customize any part of it, and not have to pay any fees to vendors.  But teams sometimes wonder: Was building the best decision after all as projects span months and budgets explode? 

Building and managing data pipelines frequently takes up a large amount of a data engineer’s work. Wakefield Research did a study that showed that these jobs take up about 44% of the time of data engineers and cost companies about $520,000 a year. The enterprise software market is also booming. It was worth $242.17 billion in 2023 and is expected to reach a huge $650.28 billion by 2032, growing at a rate of 11.6% per year. This rapid growth is a sign of the rising dependence on off-the-shelf software solutions to meet complex business needs.

The debate on build or buy data pipelines rages on in the world of data engineering. While the easy answer is “it depends,” that’s not helpful. In this blog, we’ll break down why data teams often choose to build pipelines, while also touching on the circumstances where buying might be the preferred route. By the end, you’ll be better equipped to decide if building or buying is the right choice for your team.

As a bonus, here’s a decision tree you can use to settle the build vs buy debate:

What Is a Data Pipeline and Why Does It Matter?

Think of a data pipeline as a system that extracts data from multiple sources, transforms it into usable formats, and delivers it to a destination like a data warehouse or application. It is the main foundation of modern data-driven decision-making.

What does “Build vs Buy Data Pipelines” mean?

Build: This approach is when an organization decides to create a custom solution in data pipelines using its internal resources. When you build a data pipeline, for example, you have to design, code, and manage the infrastructure in-house.

Buy: This approach is when an organization opts for a third-party tool or service to meet its data pipeline needs, relying on the vendor for implementation, support, and updates. For instance, you can focus on using the pipeline by buying Hevo without worrying about its backend development.

Differences in Build or Buy Data Pipelines
Criteria	Build	Buy
Cost	High in initial development and having ongoing expenses	Subscription-based pricing, predictable costs
Customization	Fully customizable to organizational needs	Quick setup and ready to use
Implementation	Takes time to implement, probably months to years	Quick implementation
Vendor Lock-in	No issue	Full dependency on vendor
Expertise required	High technical expertise and resources required	Minimal technical expertise required; ideal for non-technical teams
Scalability	Requires re-engineering for growing data volumes and organizational changes	Designed to handle scaling
Security	Can tailor security measures according to organizational needs	Vendors often meet standards, but customization is limited
Maintenance	Internal team is required to maintain the pipelines	Vendors provide customer support and frequent updates

Here are a couple of major factors that determine the decision to buy or build:

1. Cost

Cost is often the most critical factor when deciding between building or buying a data pipeline. To make an in-house solution, you have to spend a lot of resources up front on things like hiring skilled engineers, purchasing infrastructure, and establishing resources for development and maintenance. There can be hidden costs such as debugging, downtime, and team turnover that can inflate budget.

In contrast, buying a ready-made solution typically involves predictable subscription costs, which makes budgeting easier. For many businesses, you can minimize long-term financial surprises by purchasing a cost-effective alternative like Hevo which provide transparent pricing.

2. Time to Market

If speed is a priority, buying a data pipeline solution wins hands down. It could take months or even years to plan, code, and test an in-house pipeline. Delays are common when internal teams are stretched thin or lack the necessary expertise.

On the other hand, you can start collecting and analyzing data in days after purchasing ready-made solutions like Hevo which are designed for rapid deployment. With a shorter time to market, businesses can focus on getting value from their data instead of building infrastructure.

3. Customization

Customization is one of the biggest advantages of building your own data pipeline. When you build in-house, you can tailor the pipeline to meet the business processes and needs.  This amount of flexibility does cost something, though—time and money. While buying a solution may not offer the same level of deep customization, many tools provide pre-built templates, configurations, and integrations that cover a wide range of use cases, making them flexible enough for most organizations.

4. Scalability

Scaling a data pipeline is critical as your business grows and data volume increases. Building an in-house solution gives you control over how your system scales, but it also requires additional development work, which can strain resources. In contrast, buying a pipeline tool offers built-in scalability. Using tools like Hevo, you can instantly adjust to growing amounts of data, making sure that processes run smoothly without having to do more development effort.

5. Maintenance and Support

Maintenance can be a hidden drain on resources for in-house solutions. If you build your own pipeline, your team will need to handle many operations like updates and debugging. This takes a lot of time and dedicated engineering effort. You can eliminate this burden by buying a solution like Hevo Data, which provides continuous support, upgrades, and documentation. You may focus on strategic tasks instead of operational challenges when you choose a vendor-backed solution, which gives you peace of mind.

6. Expertise

To build a data pipeline, you need members who are experts in data engineering, cloud computing, and pipeline architecture. It can be difficult and expensive to find and keep such talented people. On the other hand, buying a pipeline solution removes the need for deep technical expertise. It’s easier for businesses to focus on leveraging data insights instead of worrying about the technical backend because vendors take care of the complex parts of integrating and managing data.

7. Compliance and Security

Compliance and security are critical in today’s data-driven world. When businesses build their own solutions, they can use their own security protocols and make sure that their businesses meet industry standards. As a result, keeping up with best practices takes ongoing effort and expertise. With Hevo, you can prioritize security and compliance first, often exceeding what smaller in-house teams can do. This ensures your data remains protected without requiring additional resources.

Why do Data Teams Build Data pipelines?

Data teams often build data pipelines to meet unique business needs that off-the-shelf solutions cannot always address. There can be companies with very specific workflows and compliance requirements that require custom solutions to manage data. Building a pipeline offers complete control over architecture, customization, and security, allowing teams to design workflows that align with their exact operational and strategic goals.

As Solmaz Shahalizadeh, former Vice President of Data Science and Engineering at Shopify, explains:

It all comes down to power and control. You are not limited by the constraints of a vendor’s features. You can control how data flows, how it’s transformed and how it’s delivered.

Yes, granted that a third-party tool will automate everything for you on the spot but it might not be quite tailored to your exact needs as compared to an in-house data pipeline. 

To round off the long points, let’s go over the pros and cons of building a data pipeline.

Pros of Building Data Pipelines

1. Full Customization
   • Teams can create a pipeline that fits their needs, workflows, and use cases. This flexibility is very helpful for companies that have niche needs or complex processes.
     
2. Complete Control
   • Building allows organizations to have full control over the pipeline’s architecture, data processing logic, and security measures, ensuring alignment with internal standards and compliance requirements.
     
3. Scalability on Your Terms
   • Businesses can scale pipelines as they grow and decide how to use its resources based on their goals. This avoids reliance on vendor-defined scalability models.
     
4. No Vendor Lock-In
   • By building their own pipeline, businesses are not dependent on third-party vendors. They avoid the risks that come with vendors changing prices, limiting features, or discontinuing services.
     
5. Unique Competitive Advantage
   • Custom-built pipelines can be tailored to specific business objectives, offering insights or efficiency that competitors using standard technologies would miss.
     
6. Learning and Innovation
   • Teams can get more specialized knowledge, encourage new ideas, and improve their data engineering skills by building pipelines within their own organizations.

Cons of Building Data Pipelines

1. High Initial Cost
   • When you’re building a pipeline, it costs a lot of money to hire skilled engineers, set up infrastructure, and get development tools. Costs can rise quickly, especially when the use case is complex.
     
2. Time-Intensive
   • Designing, developing, and testing a custom pipeline can take months or even years, delaying the time-to-insights for businesses.
     
3. Ongoing Maintenance
   • Monitoring, fixing bugs, and updating in-house pipelines all the time can use up a lot of engineering resources and raise running costs.
     
4. Talent Dependency
   • An experienced team in data engineering, cloud architecture, and data security is needed to build pipelines. It can be hard and expensive to keep this talent.
     
5. Hidden Costs
   • Beyond development, businesses face hidden costs like cloud service bills, debugging, training new team members, and downtime during system failures.
     
6. Lack of Standardized Features
   • Unlike vendor solutions, which come with pre-built connectors, monitoring tools, and scalability options, building pipelines requires teams to develop these features from scratch, adding complexity.
     
7. Slower Adaptation to Market Trends
   • In-house teams might not have the means or time to quickly add the newest features, but vendor pipelines get regular updates that keep up with market trends.
     

Why Do Data Teams Buy Data Pipelines?

Data teams often buy data pipelines when they need a fast, scalable, and reliable solution without the burden of building and maintaining it themselves. Buying a pipeline tool helps many companies avoid the complex parts of development and focus on leveraging data to find insights. Ready-made solutions work best for businesses that want to quickly set them up, save money, and get high-quality support. By purchasing a pipeline, companies can benefit from the expertise of vendors who continuously update and optimize the tool to meet evolving data needs and compliance standards.

Pros of Buying a Data Pipeline Tool

1. Quick Implementation
   • Data pipelines that are ready to use can be installed quickly so data teams can start gathering and analyzing data straight away. This is particularly beneficial for businesses that need to act fast and avoid long development cycles.
     
2. Predictable Costs
   • When you buy a tool, you have to pay a fixed subscription or licensing fee. This makes budgeting easier and eliminates the risk of unexpected costs that come with developing in-house.
     
3. Scalability
   • Most companies that sell data pipelines offer cloud-based options that are easy to expand as your data volume grows. Companies don’t have to worry about building new facilities to meet the growing demands.
     
4. Ongoing Support
   • When you buy a solution, the company that made it gives you ongoing help, fixes, and updates. This makes things easier for your own team and makes sure that the tool always has the newest features and security measures.
     
5. Reduced Maintenance Effort
   • Your internal resources can work on more strategic tasks instead of the day-to-day operational challenges of managing a pipeline since the vendor takes care of system maintenance, bug fixes, and updates.
     
6. Expertise Built-In
   • Data pipeline vendors have a lot of knowledge that helps make sure that best practices are followed for managing data, integrating it, keeping it safe, and following rules. This is very helpful for teams that don’t have any specialized skills in-house.
     
7. Built-in Features and Integrations
   • It takes longer to build these features from scratch than to use pre-built connectors, integrations, and monitoring tools from most vendors. This means you can quickly and easily combine different data sources.
     
8. Continuous Innovation
   • With a purchased solution, you benefit from continuous innovation as vendors update their tools with new features, optimizations, and security enhancements. This ensures your pipeline evolves along with industry trends.

Cons of Buying a Data Pipeline Tool

1. Less Customization
   • Many businesses offer flexible configurations but bought solutions might not be as easy to customize as in-house pipelines. This could be a problem for companies with complex needs or unique work processes.
     
2. Vendor Lock-In
   • When you use just one vendor for your data pipeline, their prices, features, and business goals can change. If you need to change vendors, vendor lock-in can make it hard to do so.
     
3. Recurring Costs
   • While initial costs are predictable, subscription fees can add up over time. Depending on your data volumes and features required, the ongoing costs may become significant.
     
4. Limited Control
   • This means that when you buy a data pipeline, you are giving a third-party vendor control over your system’s architecture, security, and performance. This means you have less power over how your pipeline works and can’t change it as much.
     
5. Dependency on External Support
   • Support from the vendor’s team can be helpful, but it also makes you reliant on the vendor for fixes, updates, and new features. It could hurt your business if the vendor doesn’t deliver on time or changes the help services it offers.
     
6. Learning Curve
   • Your team may need some time to get used to a new tool, even if it comes with a ready-made solution. Some tools may not be as easy to use or may need a lot of training before they can be used effectively.
     
7. Security and Compliance Risks
   • If you give a vendor sensitive data, you have to trust their security measures, even if you have high-security standards. Companies that have strict rules about security or compliance may feel more comfortable taking care of these things themselves.
     
8. Less Control Over Updates
   • When a vendor updates their software, you may not have full control over when or how the changes are implemented. This could result in unexpected feature changes, downtime, or incompatibility with your existing workflows.
     

Build vs Buy Data Pipelines: Detailed Cost Analysis

Businesses often choose based on cost in the “build vs buy data pipeline” decision. A well-informed decision requires knowing the full cost implications, including both short-term and long-term costs. The prices of building and buying data pipelines are broken down in more detail below:

Costs of Building a Data Pipeline

It takes a lot of time and money to build a data pipeline from the start. The costs up front and over time can be very high, especially when you think about the secret costs that people often don’t think about. Here’s how it works:

1. Development Costs

• Engineering Team: You’ll need to hire skilled developers, architects, and data engineers. The pay for these jobs varies greatly based on where they work and their experience. For example, experienced engineers can make more than $100,000 a year. Having a team of 4 or 5 will cost a total of $400,000–$700,000.
• Time: Some custom pipelines could take years to build. Development delays can make the project take longer and cost more.

2. Infrastructure and Cloud Costs

• Hardware and Servers: For on-premise solutions, hardware purchases, storage, and servers are required. Cloud-based infrastructure, on the other hand, means paying for cloud services like AWS, Azure, or Google Cloud, with costs based on data volume, processing needs, and storage. For example, AWS charges based on usage, with costs escalating as data scales. Initial hardware costs for on-premise solutions could be anywhere from $50,000 to $200,000, based on the scale of the project.
• Software Licenses: For database management, ETL processes, and analytics, you may need more software. You can get software through a subscription, but each has its own pricing plan.

3. Maintenance and Updates

• Ongoing Monitoring and Troubleshooting: Maintaining a custom-built pipeline requires dedicated resources to ensure it’s running smoothly. This includes ongoing updates, bug fixes, and security patches. Annual costs could be $80,000–$150,000.
• Scaling Costs: The pipeline needs to be built to handle more data. This could mean upgrading the tools or adjusting the way the system works so that it can handle more data, which will cost more in the long run. Annual scaling cost could be $20,000–$100,000.

4. Hidden Costs

• Downtime and Disruptions: When errors, bugs, or system failures happen, data loss or interruptions can happen. This can be expensive because of the time and money spent on work or missed opportunities.
• Training and Onboarding: Your team will need to be trained on the custom system, which adds both time and cost to the project.

Costs of Buying a Data Pipeline Tool

Buying a pre-built data pipeline tool is often seen as a more economical and simpler option than making one from scratch. Buying a tool does lower the cost of growth, but it also comes with its own costs. Here is a full rundown of the costs:

1. Subscription or Licensing Fees

• Data Volume: The pricing of many tools, such as Fivetran or Airbyte, is based on the amount of data being transferred. As your data volume increases, the cost can escalate rapidly. You can check Hevo’s pricing structure for each plan and data volume cluster for yearly and monthly packages.
• Fixed Costs: Vendors typically charge subscription fees, either monthly or annually, based on data volume, the number of users, or features included. For instance, tools like Hevo Data have a tiered pricing model, with fees starting at a few hundred dollars per month for basic plans and going up depending on the data volume.

2. Integrations and Add-Ons

• Third-Party Integrations: Many vendors charge additional fees for connecting to third-party systems, databases, or cloud services. These integration costs can quickly add up, especially if your business uses multiple platforms.
• Premium Features: Some advanced features, such as data transformations, monitoring, or enhanced security, may be available only in higher-priced tiers.

3. Ongoing Support and Maintenance

• Support Services: Most vendors offer support, but this may come at an additional cost for premium services. However, these costs are usually less than hiring an internal team to provide ongoing support for a custom-built solution.
• System Updates: Updates and new features are typically included in the subscription, reducing the maintenance burden on your internal team.

4. Scalability Costs

• Scaling Costs: As your data grows, you might need to either improve your subscription or buy more services to keep up with the flow of data. This might be cheaper than building a new system from scratch, but it still adds to the costs over time.

5. Hidden Costs

• Long-Term Commitment: The regular subscription fees can add up over time. The starting costs are usually clear, but these tools tend to get more expensive as your data grows or as you need more features.
• Dependency on Vendor: Any increase in pricing or changes to the vendor’s business model can impact your budget. Shifting to a new vendor may involve migration costs and potential downtime.

Comparing the Costs: Build vs Buy Data Pipelines

Costs in Build vs Buy Data Pipelines
Criteria	Build	Buy
Initial Investment	$400000 – $1000000	$10000-$50000
Time to deploy	6-12 months	1-4 weeks
Annual Maintenance	$80000-$200000	$5000-$20000
Scaling Costs	$20000-$100000	$10000-$50000
Training	$10000-$20000	Minimal
Hidden Costs	Downtime, upgrades etc	Vendor dependency, data lock-in
Total Cost of Ownership	$1000000+ over 3-5 years	$50000-$300000 over 3-5 years

Short-Term Costs

• Building: Building a pipeline will cost a lot in the short run. It doesn’t take long for development, hiring, and infrastructure expenses to add up. If you have a large team, the costs could exceed hundreds of thousands of dollars in the first year.
• Buying: The initial cost of buying is much lower. Subscriptions start at a few hundred dollars per month for small businesses and increase based on data needs. You can get started quickly with a predictable cost model.

Long-Term Costs

• Building: Over time, the ongoing costs of maintenance, scaling, updates, and troubleshooting can be substantial. As your data volumes grow, you’ll need more resources to support the pipeline, which increases both infrastructure and personnel costs.
• Buying: While the cost is predictable, subscription fees can increase with data volume, and vendor changes can introduce unforeseen price hikes. Over time, the subscription fees may outpace the cost of a custom-built solution, especially if the business scales significantly.

Total Cost of Ownership (TCO)

• Building: The TCO of a custom pipeline can exceed the cost of buying when you factor in long-term maintenance, talent acquisition, and the need for continual scaling. However, building a pipeline might be more cost-effective in the long run if your business has unique needs and the expertise to manage it.
• Buying: The TCO is typically lower than building due to reduced maintenance costs and faster implementation. However, this can rise over time as the business grows and requires more features, higher data volumes, or additional integrations.

Conclusion

The “Build vs. Buy Data Pipelines” decision is very important for any business that wants to leverage data effectively. There isn’t just one answer; the best one relies on the resources and technical needs of your business.

Building a custom data pipeline might be the best option if your team has the technical expertise and resources. However, if your team lacks the technical capacity or if you have limited resources, buying an existing data pipeline solution can save you time and money while ensuring reliable performance.

Regardless of whether you choose to build or buy, it’s essential that the pipeline ensures secure data collection, management, and storage with scalability and cost-effectiveness in mind.

If buying a pipeline is the right move for your business, Hevo offers a great solution with its no-code, automated tool that replicates data in real-time. Hevo doesn’t lose any data and has customer service available 24/7. It’s a reliable choice for upgrading your data stack. Are you prepared to move forward? Set up a demo with Hevo right now!

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