Fivetran Pricing in 2026: How Monthly Active Rows Affect Your True ETL Cost

You thought you had the budget figured out. You looked at the row counts in your source databases, estimated a reasonable growth rate, and signed the contract. It all made sense on paper.

Then the invoice arrived six months later and the number was completely disconnected from your estimates.

If you are running Fivetran in 2026, you have probably had a version of this conversation. The problem is rarely that your company suddenly generated twice as much data. The problem is usually that the rules around how that data is counted have changed, and those changes happen in ways that are incredibly difficult to forecast.

Fivetran's Monthly Active Row model is supposed to be simple. You pay for what changes. But after a series of pricing updates across 2025 and 2026, MAR has become one of the most unpredictable metrics in the modern data stack.

Here is how the pricing actually works now and why your bill might be climbing faster than your data volume.

How Fivetran Monthly Active Rows Actually Work

The core concept of MAR is straightforward. Fivetran counts the number of distinct primary keys that are inserted, updated, or deleted in your connected sources during a calendar month. Each row is counted once per month, even if it changes multiple times.

That sounds like a fair deal. You are not paying for data that just sits there.

The complexity comes from the details. MAR is not calculated across your whole account. It is calculated per connection. Initial syncs are free, but resyncs triggered by schema changes can sometimes cause spikes. And the way Fivetran handles high-frequency changes means certain types of data will cost you significantly more to move than others.

If you are trying to understand why your bill looks the way it does, you have to look at the three major changes Fivetran rolled out over the last two years.

The 2025 Shift to Per-Connector Billing

Before March 2025, Fivetran aggregated your MAR across your entire account. This was a massive benefit for teams with a lot of data sources. If you had one massive database and thirty smaller SaaS tools, the volume from the big database pushed you down the pricing curve, making the thirty smaller tools very cheap to run.

Fivetran changed that. Now, each connection sits on its own pricing curve.

If you have forty connectors, you are essentially starting at the most expensive tier of the pricing curve forty different times. You lose the aggregate volume discount. For teams with a sprawling SaaS footprint or multiple staging and production environments syncing the same data, this change alone caused bills to jump significantly.

The 2026 Updates That Inflated Row Counts

In January 2026, Fivetran introduced three new rules that fundamentally changed what counts as billable activity.

Deletes Now Count as Paid MAR

Previously, if a row was deleted in your source system, Fivetran did not charge you for it. Now, deletes are treated exactly like inserts and updates. If you have tables where rows are frequently created and then purged, think event logs, temporary order states, or high-churn CRM records, your billable MAR will be much higher than the actual size of the table.

History Mode Is Billed Per Change

History mode tracks every change to a row over time, keeping a record of what the data looked like at any given moment. It is incredibly useful for auditing and point-in-time reporting.

It is also incredibly expensive now. Fivetran used to count a history mode row once per month, regardless of how many times it updated. Now, every single update to a row within a month counts toward your paid MAR. If you turn on history mode for a table that sees frequent updates, your bill will spike almost immediately.

The New Base Charge

Fivetran also added a $5 monthly base charge for every connection generating between 1 and 1 million MAR. It sounds small, but if you have a hundred low-volume connectors, that is $500 a month in base charges before you even look at the data volume costs.

The Hidden Costs Beyond MAR

When you are budgeting for Fivetran, the ingestion cost is only the first line item. The total cost of ownership usually includes three other things that do not show up on the Fivetran invoice.

The first is transformation. Fivetran extracts and loads. You still have to transform the data, which means you are paying for dbt Cloud or managing the open-source version yourself.

The second is reverse ETL. Fivetran gets data into the warehouse. Getting it back out to Salesforce or HubSpot requires a separate tool like Census or Hightouch, which comes with its own contract and its own pricing model to manage.

The third is observability. When a schema changes upstream and Fivetran pushes that change into the warehouse, downstream models break. Catching those issues requires a standalone observability tool like Monte Carlo, adding another vendor to the stack.

When you add it all up, the true cost of moving data is often double or triple the MAR estimate you started with.

How to Get Your ETL Costs Under Control

If your Fivetran bill is growing faster than your business, you have a few options.

You can spend engineering time optimizing what you sync. You can turn off history mode on tables that do not strictly need it. You can reduce sync frequencies to try and batch updates. You can build custom scripts to filter out deletes before Fivetran sees them.

Or you can look for a platform that does not penalize you for having messy, high-churn data.

The teams that successfully get their costs under control usually do it by moving away from pricing models that charge per connector or penalize frequent updates. They look for platforms that offer predictable billing and consolidate the fragmented pieces of the data stack.

Why Data Teams Are Switching to Matia

Matia is a unified DataOps platform built by data engineers who were tired of unpredictable MAR bills and fragmented infrastructure. It combines ETL, Reverse ETL, Observability, and Data Catalog in one place.

Matia bills on Monthly Active Rows, but without the traps. There are no per-connector penalties. There are no delete charges. There are no history mode surprises. You get predictable pricing that scales with your actual data footprint, not the frequency of your updates.

For teams moving from Fivetran, the migration is fully backwards compatible. Pipelines, connectors, and configs carry over in days.

Ramp reduced its data platform costs by approximately 40% after moving to Matia. Lopay stood up more than 100 integrations in just 3 days during its migration. Obligo saw an 83% increase in team productivity after consolidating onto Matia. Recharge cut its Snowflake costs by 32% after switching to Matia's platform.

Book a demo and we will walk through your current Fivetran setup, your MAR spend, and what a migration realistically looks like for your team.