Fivetran’s consumption-based model calculates usage based on the number of monthly active rows (MAR) across every connection between a distinct source and destination, rather than based on raw throughput.

What is a monthly active row?

A monthly active row (MAR) is a row that’s been added, updated, or deleted in a data destination by any instance of a connector, i.e. connection. We only count a row as active once each month, not each time it’s updated. This means you’re not charged for multiple updates to a row in a single month. We always give customers historical syncs completely free for any new connection or table. Then, we only charge for MAR.

With the use of MAR, Fivetran’s pricing model is uniquely designed to drive the most value for our customers.

Active rows and pipeline efficiency

Fivetran’s 700+ connectors are designed to efficiently capture changes in the data source and perform incremental upserts wherever possible. We have found that, for typical pipelines, MAR is smaller than total synced rows by a factor of 5 to as much as a factor of 50. This ultimately reduces the cost of managing a cloud destination since only necessary data is replicated rather than every single row.

Total synced rows and update waste

Similarly, we have observed that monthly active rows add up to considerably less than total synced rows because a typical pipeline experiences waste, which happens when a row that wasn't updated (i.e. its values did not change) is repeatedly synced in a few ways:

• Multiple row updates: A single row, defined by a unique primary key, can be updated multiple times in a single month. Rows will undergo updates several times over the course of a month. Each update counts as a synced row. This generally occurs five times per month on average.
• Snapshot waste: This happens when a primary key that wasn't actually updated is synced (e.g. when you replicate a table using snapshots). Capturing updates is hard and many customers often resort to a snapshot approach, syncing all rows every time. This generally occurs 10 to 20 times per month on average.

Over the course of a month, or even years, a typical data pipeline can generate considerable bloat because it wasn’t built to handle incremental changes effectively.

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Calculating MAR vs. total synced rows

Now that we know the difference between MAR and total synced rows, we can compare how much they  can differ while updating the same amount of data. There are two components to estimating MAR:

• Rows at rest: Rows at rest is the total number of primary keys in the data source.
• Update rate: Update rate is the percentage of primary keys in the source that are updated or added at least once in a single month. 

Total rows at rest * update rate percent = MAR

‍For example, a database with 10 million rows at rest with a 10 percent update rate sees the following MAR: 

10,000,000 X 10 percent = 1 million MAR

‍The MAR method represents a hard ceiling. By contrast, total synced rows for this scenario can range from a floor of 1 million (assuming the pipeline incrementally captures each update) to multiple times 10 million, given the common use of snapshots and potential for repeated row updates.

What are typical update rates aka MAR?

Every source and every business is different. Application sources typically see update rates of 10 to 20 percent, whereas databases typically see anywhere from five to 10 percent, though very old databases can be as low as one to two percent.

Below, we share some examples of customers with different total connectors, total volumes, MAR and estimated total cost of ownership if they built these pipelines themselves:

*These are rough approximations. You can estimate the total cost of ownership of building pipelines yourself in Fivetran’s “Ultimate guide to data integration” or contact sales@fivetran.com for a more accurate calculation.

Ultimately, Fivetran customers can replicate millions and millions of historical rows for free and only pay for changed rows — all at a fraction of the cost of building themselves. 

“We have access to more sources and to richer data within the sources. For example, Fivetran imports history tables from HubSpot. That is millions of rows of historical data that we couldn’t explore as easily before. The alternative to Fivetran would be to hire approximately three data engineers to work full-time on our integrations.”
-Jakob Kristensen, Product Manager at Pleo

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Get a free usage estimate

The most accurate way to estimate MAR is to connect your sources. If you’re new to Fivetran, you can try Fiveran completely free for 14 days. During your free trial, you’ll get a usage and pricing estimate based on the sources you connected during your trial in our new pricing calculator.

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