How to Reduce Fivetran and Data Ingestion Tool Costs

You reduce Fivetran and data ingestion tool costs by lowering the number of distinct rows you move, because Fivetran bills on Monthly Active Rows (MAR) rather than on volume of data or number of syncs. A MAR is a unique primary key that the platform inserts, updates, or deletes in a destination during a calendar month, counted once no matter how many times it changes, as documented on the Fivetran pricing page and its usage-based pricing docs. In 2026 the per-million rate steps down with volume, from roughly 2.50 dollars per million MAR at the low end toward about 1.00 dollar per million at high volume, with a 5 dollar base charge on each standard connection that uses between one and one million MAR. Most overspend is breadth: tables and columns synced that nothing downstream queries, and duplicate connectors pulling the same source twice. Trim the breadth and the bill falls without breaking a single model.

This guide is part of our complete guide to SaaS and data platform cost optimization, the cluster pillar it links up to. It pairs with how to optimize dbt Cloud and data transformation costs, its sibling guide, since ingestion and transformation spend climb together across the ELT stack.

What drives the cost of Fivetran?

Fivetran cost is driven by Monthly Active Rows, the count of unique rows changed across all your connectors in a month, plus a small base charge per connection. The key property is that MAR is a breadth measure, not a frequency measure: a row that updates a thousand times in a month still counts as one active row, while a thousand rows that each change once count as a thousand. This is why the most expensive habit is syncing wide, high-cardinality tables and unused columns that you never model downstream, and why running two connectors against the same source quietly doubles the active rows. Sync frequency, by contrast, mostly affects freshness and the compute your warehouse spends loading the data, not the MAR itself. Knowing the bill follows distinct rows tells you exactly where to start: prove which connectors and tables produce the most MAR, then cut the ones that earn nothing.

How do you cut a Fivetran bill, step by step?

Cut the bill by mapping Monthly Active Rows first, then removing the breadth that earns nothing. The sequence below is the one we run on a data ingestion cost engagement.

1. Map Monthly Active Rows by connector and tablePull the MAR breakdown from the Fivetran usage dashboard so you can see exactly which connectors and tables drive the bill. The result is a ranked target list, because a few high-cardinality sources usually produce most of the cost.
2. Sync fewer tables and columnsDeselect tables and columns that no downstream model, dashboard, or report consumes, since every active row in them is billed. The result is an immediate MAR reduction with zero impact on anything anyone uses.
3. Eliminate duplicate and overlapping connectorsRemove connectors that pull the same source twice, and retire abandoned test or staging connectors that still run. The result is the end of paying for the same rows more than once.
4. Filter rows at the sourceWhere the connector allows, limit syncs to the records you actually use, such as recent partitions or active accounts, rather than the full history every month. The result is fewer distinct active rows for the same business coverage.
5. Right-size sync frequency for compute and freshnessMatch each connector's sync frequency to how fresh its destination table truly needs to be, rather than running everything at the fastest interval. The result is lower warehouse load and fewer wasted syncs, even though MAR itself barely moves.
6. Govern it so it stays cutSet usage alerts and review MAR per connector each month so a newly added high-volume source is caught the same week it lands. The result is a bill that holds instead of creeping back up as new connectors arrive.

Does lowering sync frequency reduce the Fivetran bill?

Lowering sync frequency reduces the Fivetran bill only slightly, because MAR counts a changed row once per calendar month no matter how many syncs touched it. This surprises teams who slow their connectors down expecting a large saving and see little movement. Frequency does matter for two other reasons: it sets how fresh the data in your warehouse is, and it drives the downstream compute your warehouse spends loading and merging each batch, which shows up on the Snowflake or warehouse bill rather than on Fivetran. So treat frequency as a freshness and warehouse-compute decision, and treat the count of distinct synced rows as the Fivetran cost decision. Slowing a connector that feeds a daily report from fifteen minutes to hourly is reasonable; expecting it to halve your MAR is not.

How do you stop a Fivetran bill from creeping back up?

You stop a Fivetran bill from creeping back up by governing new connectors and watching MAR per connector on a monthly cadence. Ingestion spend drifts because adding a connector is easy and self-service, so a single new source with millions of high-cardinality rows can reset months of savings in one sync. The guardrails that hold are simple: a usage alert that fires when total MAR crosses a threshold, a monthly review of the per-connector MAR table so an unexpected climber is visible early, and a lightweight approval step before a new high-volume source goes live. This is the same govern discipline from our See, Cut, Lock, Run method, applied to data ingestion: cut once, then lock the result so the breadth you removed does not quietly return.

The short version

Reduce Fivetran and data ingestion tool costs by mapping Monthly Active Rows, syncing fewer tables and columns, removing duplicate connectors, filtering to the rows you use, and governing MAR with alerts and a monthly review. MAR is a breadth measure, so cut breadth, not cadence. Compare it with the transformation layer in how to optimize dbt Cloud and data transformation costs and return to the SaaS and data platform cost pillar for the rest of the stack.