Fivetran

Fivetran is the right tool when you need connectors for mainstream SaaS applications (Salesforce, HubSpot, NetSuite, Marketo, Jira) and you value engineering time over per-row cost. The connector quality for these popular sources is high. Schema changes are handled automatically. New fields appear in your warehouse without code changes. For a team with 10-15 SaaS connectors and moderate data volumes, Fivetran's total cost of ownership is often lower than building and maintaining the equivalent with custom code.

The breadth of the connector catalog is a genuine advantage. If you need to add a new SaaS source next quarter, Fivetran almost certainly has a pre-built connector for it. That predictability matters for organizations that are growing their data platform and want to add sources without an engineering project for each one.

Fivetran also handles schema drift well. When upstream sources change their API or schema, Fivetran adjusts. This is the kind of maintenance burden that is invisible until you are doing it yourself and a Friday night API change breaks your pipeline.

Fivetran's Monthly Active Row (MAR) pricing is most economical for sources with moderate row counts and low change frequency. For sources that generate high volumes of change events (e-commerce transactions, website event streams, IoT data, and application logs), it is worth checking that the per-row cost stays in line with the value the data delivers.

We see this pattern most often with Shopify, GA4, and event-tracking platforms. A subscription apparel brand was paying Fivetran prices driven primarily by two high-volume connectors. We migrated those specific connectors to Estuary and reduced ELT costs by 80%, while keeping Fivetran for the lower-volume SaaS connectors where its pricing was reasonable.

Another optimization opportunity is aligning what you sync with what you use. Fivetran defaults to syncing all tables for many connectors. If your downstream dbt models use 10 of the 50 tables Fivetran syncs, tightening the table selection means you only sync what your reporting reads. An audit of table selections and sync frequencies is often the quickest path to cost reduction.

Do not migrate away from Fivetran reflexively. The hidden cost of running your own ingestion is maintenance: monitoring, debugging, handling API changes, managing state. If your team does not have the engineering capacity to maintain custom pipelines, the cost of Fivetran is often cheaper than the cost of pipeline failures and engineering time.

We start by optimizing within Fivetran (table selection, sync frequency, connector configuration) so it is right-sized for each source before considering anything else. For a small number of very high-volume connectors, a specialized tool (Estuary for CDC and high-volume sources, dlt for custom APIs) can be a better fit, and where that is the case we always run both tools in parallel and validate data equivalence before decommissioning anything.