Fivetran has been one of the dominant providers of cloud ELT tools for the last decade. As enterprises moved away from manual, on-premises ETL services, Fivetran became a go-to choice for teams that needed reliable, managed pipelines without the overhead of building and maintaining connectors themselves.
Over the last couple of years, that calculus has shifted. Pricing changes and a broader shift in focus from acquisitions have left some customers re-evaluating whether Fivetran is still the right fit. At the same time, the rise of enterprise AI for analytics has raised the bar for what data teams need to deliver.
Here are the four areas that come up most often in the search for an alternative.
Fivetran's Monthly Active Row (MAR) pricing charges based on the volume of rows processed each month. For teams with large or growing data volumes, that means costs that are difficult to predict and harder to control. A change in sync frequency, an upstream system pushing more records, or simply business growth can move the bill in ways that weren't budgeted for.
The pricing pressure also shapes how teams use Fivetran in ways that create problems downstream. To control costs, many customers end up selectively ingesting only portions of their data — skipping tables, limiting historical depth, or pre-processing data outside of Fivetran before it's synced. The result is a fragmented data landscape.
As Fivetran has scaled, some customers have found that the support experience has shifted — generally toward longer response cycles and more self-service resolution for issues.
For teams managing complex enterprise integrations, support responsiveness matters. A connector issue doesn't always arrive at a convenient moment.
Moving data into a warehouse is only a part of the job. Before AI can reliably answer business questions from that data, it needs context: the definitions, KPIs, relationships, and business logic that give raw rows meaning.
With a traditional pipeline like Fivetran, that context has to be built separately — through dbt, manual semantic modeling, or a combination of both. It's a significant project, often measured in months, and one that many teams find themselves deferring or rebuilding more than once.
Fivetran's connector catalog covers a range of common SaaS applications, but enterprise data landscapes are rarely standard. Teams working with systems like SAP Ariba, Infor, or industry-specific applications often find those sources missing or covered only through lite connectors.
When a critical source isn't available, the path forward is usually waiting on Fivetran's connector roadmap or building a custom connector — an investment that requires ongoing maintenance and still leaves the team dependent on Fivetran's infrastructure.
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