# Hidden Costs of In-House ETL Solutions

> In-house ETL costs more than it seems. What to consider before deciding to build pipelines in-house.

Source: https://www.erathos.com/en/blog/hidden-costs-of-in-house-etl-solutions
Em português: https://www.erathos.com/blog/hidden-costs-of-in-house-etl-solutions
Published: 2026-08-07
Category: Data Engineering

![Iceberg representing the hidden costs of in-house ETL solutions](https://cms-media.erathos.com/rR1ny4nov8SGBjbDhigdRhkPXo-1.png)

## What does it actually cost to run ETL in-house?

During our sales validation calls, people often compare the cost of cloud to the price of our subscription. So I decided to talk about the hidden costs of building and maintaining in-house data pipelines.

First, a few important disclaimers, before you come for me in the comments:

- I'm not talking trash about the internal libraries I know you data engineers are addicted to using or building
- I'm also not talking trash about Airflow or using DAGs
- I'm specifically saying that the process of ingesting data can be optimized

So let's get into it, starting with the basics.

**Team hours**

It's easy to forget to factor in the hourly cost of your team building and maintaining data pipelines. How much time is someone on your data team going to spend building or maintaining data pipelines?

**Hiring and retention**

Data engineers are very hard to find (and expensive). Good engineers are rare, and let's face it, you're competing with foreign companies to hire and retain that talent. To build that team, on top of the engineers' own salaries, you'll have recruiting and selection costs that are rarely factored in.

**Turnover**

Data teams end up with a lot of turnover. Who hasn't had to fix a stone-age pipeline built by someone who's no longer at the company, with no documentation of what was done? Many data teams literally lose everything when one person leaves, because nobody else knew how that process worked. Sure, good documentation would solve the problem, but the reality is there are so many demands that teams end up putting it off. Another thing: engineers like to be challenged, nobody can stand doing little ETL jobs for very long.

**Pipeline maintenance**

Your team needs to keep pipelines running, and maintenance never ends. That means managing every API change from the systems your company uses. Are you ready to rebuild the Google Ads integration every 3 months? Outsourcing that problem seems like a much better alternative to me.

**Firefighting**

Not to mention the maintenance issues that catch everyone off guard, a CxO coming in hot because the dashboard didn't update. And there goes another day debugging the pipeline to figure out what happened, throwing off the whole development roadmap.

**Downtime**

Speaking of firefighting, how long does it take to find out something's broken and fix it? Obviously there's no such thing as a world without downtime, but ideally it goes unnoticed by the team consuming the data, because the fix is fast and your team catches the problem before anyone complains. [Data observability](https://www.erathos.com/en/blog/data-observability-breaking-the-firefighting-cycle) makes ALL the difference.

**Opportunity cost**

Would you really rather have an engineer reading API documentation than solving a core problem for your business? Maybe ingesting some business-specific data source, like raw open data files, building and optimizing transformations inside your analytics environment, or optimizing your embedding generation process if you use LLMs.

Sure, in some contexts building it internally makes sense, at least for some data sources. But in most cases, the hidden costs end up outweighing that choice. Talk it through with your data team, finance, and so on before deciding, and don't rush it. But for the sake of your company, don't skip considering the indirect costs. If you want to dig deeper into this reasoning, we wrote about it in [Data Pipelines: Buy or Build?](https://www.erathos.com/en/blog/data-pipelines-buy-or-build)

## Frequently asked questions about in-house ETL costs

**Is building ETL internally always cheaper than buying a tool?** Rarely, once you count everything: team hours, hiring, turnover, endless maintenance, rushed firefighting, and the opportunity cost of not solving a core business problem. What looks cheaper on the initial budget usually ends up more expensive after a few months in production.

**When does it still make sense to build internally?** For data sources very specific to your business, like raw open data files or something no market tool covers, or when your team already has spare capacity and wants full control over that specific pipeline.

**How do I know if in-house maintenance is costing more than it looks like?** A clear sign is constantly having to rebuild an API integration every few months, or losing all knowledge of a pipeline when someone on the team leaves without documentation. If that happens often, the hidden cost is already adding up.

If you're on the fence between building or buying data pipelines, [get in touch with our team](https://calendly.com/erathos-gelsonbagetti/erathos?utm_source=blog&utm_medium=article&utm_campaign=hidden_costs_etl_inhouse_en)!