Fivetran vs Matillion

Quick verdict

Fivetran is a managed extract and load service. Its documentation describes the model plainly: it extracts and loads raw data into the destination and transforms it after the load, and it does not support arbitrary in-flight transformations. The pricing page lists 700+ connectors. Fivetran completed its all-stock merger with dbt Labs on 1 June 2026; the combined company operates as Fivetran + dbt Labs. In 2025 it also agreed to acquire Census, whose reverse ETL product is now integrated as Fivetran Activations for sending warehouse data back to business applications.

Matillion sells a cloud pipeline platform launched as the Data Productivity Cloud, which matillion.com now presents as Maia Foundation (formerly Data Productivity Cloud) within its Maia AI data automation platform; the pricing page still uses the Data Productivity Cloud name, and the docs are at docs.maia.ai. You build orchestration pipelines that load and schedule data and transformation pipelines that reshape it, on a low-code canvas with SQL and Python components. Matillion says transformations execute directly within the target platform. It supports Snowflake, Databricks, Google BigQuery and Amazon Redshift.

Side by side

Key differences

Ingestion service plus dbt versus one pipeline designer

Fivetran takes the extract and load step away from you: you choose a source and destination, Fivetran creates and maintains the schema and keeps it in sync. Transformation is deliberately a separate layer. Fivetran's documentation lists pre-built Quickstart Data Models, Fivetran Data Models for dbt projects, a Fivetran-hosted dbt Core integration, and orchestration of third-party dbt Cloud or Coalesce jobs; it says transformations run in your destination. Since the dbt Labs merger, the most common transformation layer for Fivetran users is in the same company, which in our view makes the Fivetran plus dbt pairing the default modular stack rather than an integration between two vendors.

Matillion puts the load and the transformation in one product. Orchestration pipelines load data and control flow; transformation pipelines use components that mirror SQL operations (filter, join, aggregate, pivot) and run as SQL in the platform. You can still use dbt through its dbt Core component. The trade-off is skills and control: a dbt project is code in Git that any dbt user can read, while a Matillion pipeline is quicker to assemble for people who prefer a visual tool but lives in Matillion's format.

-- A dbt model (Snowflake SQL with Jinja), the typical Fivetran pairing.
-- Saved as models/daily_orders.sql; dbt runs it inside the warehouse.
SELECT CAST(created_at AS DATE) AS order_date,
       COUNT(*)                AS orders,
       SUM(amount)             AS revenue
FROM {{ source('shop', 'orders') }}
GROUP BY 1

In Matillion, the same aggregation would usually be a transformation pipeline: a table input, an aggregate component and a write component, which generates and runs equivalent SQL in the warehouse.

Connector breadth and maintenance

Fivetran's main selling point is its managed connector catalogue: Fivetran builds and maintains the connectors and handles schema changes in the destination, and the Census acquisition added reverse ETL in the other direction. Matillion lists fewer pre-built connectors and offers custom and Flex connectors for REST APIs; its documentation notes that custom, Flex, data source and file connectors do not automatically adapt to schema drift. If most of your work is bringing in data from many SaaS applications, Fivetran covers more of that out of the box; if most of your work is shaping data that is already in the warehouse, Matillion covers more of that.

Destinations and portability

Fivetran loads into many warehouses, lakes and databases, including its own Managed Data Lake Service (Fivetran notes that its hosted dbt transformations do not support Managed Data Lake Service destinations). Because dbt models are SQL, they can be adapted if you change warehouse. Matillion targets Snowflake, Databricks, BigQuery and Redshift only, and pipelines are built for one of them. If the warehouse decision is still open, see Snowflake vs Databricks.

Pricing models: changed rows versus run time

Fivetran bills by Monthly Active Rows, which its documentation defines as the number of distinct rows synced from the source to the destination in a calendar month, tracked by primary key, so a row updated many times in a month counts once. Its pricing page says each connection follows a separate cost curve, with volume discounts as usage grows, and a base charge per connection on paid plans; transformations are billed per model run and activations separately. Cost therefore follows how much data changes in each source.

Matillion bills credits for task hours, the execution time of the tasks a pipeline breaks into, charged only when pipelines run, plus extra developer users. With both tools, the transformation compute itself is billed by your warehouse. In our view, Fivetran costs are easier to predict per source, and Matillion costs are easier to predict per pipeline schedule; neither can be estimated without your own volumes.

Deployment and control

Fivetran documents two deployment models: SaaS, where processing happens in the Fivetran cloud, and Hybrid Deployment, where data is processed in your own network while Fivetran's cloud handles configuration and orchestration. Matillion offers Full SaaS (Matillion hosts the runner on AWS) and Hybrid SaaS (you run the runner in AWS, Azure, Google Cloud or Snowflake and keep secrets in your own secrets manager); the pricing page lists hybrid deployment on the Scale plan. Both can therefore keep data processing inside your network if compliance requires it.

Pricing and licensing

Fivetran. The pricing page lists Free, Standard, Enterprise and Business Critical plans. The Free plan covers up to 500,000 MAR for connections, 3,500 MAR for activations and 5,000 model runs per month. Paid plans price each connection per million MAR with a USD 5 base charge per connection, and transformations at USD 0.01 per model run in the 5,001 to 30,000 band, with lower rates at higher volumes. As one example from the pricing page, a Facebook Ads connection with 34,479 MAR is shown at USD 22.06 per month on Standard. New connections get 14 days of free use, and annual contracts carry discounts.

Matillion. The pricing page lists Developer (1 developer user, SaaS only), Teams (5 developer users, SaaS only) and Scale (5 developer users, hybrid deployment, data lineage, streaming CDC, custom SSO) as fixed annual packages with included credits, consumed by pipeline task hours and extra users. No prices are published; contact Matillion for a quote. The documented free trial lasts 14 days with 500 credits.

For both, warehouse compute for transformations is billed by your cloud data platform.

Pricing checked on the vendors' official pages on 7 October 2026. Prices change; confirm before buying.

Where each one leads

Limitations

When to choose each

When neither is right

• You want to self-host open source ingestion: see Airbyte vs Fivetran and Airbyte vs Matillion.
• You need an enterprise integration suite with on-premises and mainframe sources, data quality and API integration: see Fivetran vs Talend and Matillion vs Talend.
• Your data lives in one or two databases and reports can run on a replica: SQL jobs and views may be enough without a pipeline tool. See ETL vs ELT.
• You want a shortlist of other managed options: see Fivetran alternatives.

Final recommendation

Frequently asked questions

Sources

• Fivetran pricing
• Fivetran documentation: Core concepts
• Fivetran documentation: Transformations
• Fivetran documentation: Deployment models
• Fivetran documentation: Usage-based pricing (MAR)
• Fivetran documentation: Activations FAQ
• Fivetran press release: Fivetran and dbt Labs complete merger
• Fivetran press release: agreement to acquire Census
• Matillion homepage (Maia and Maia Foundation)
• Matillion pricing
• Matillion: Data transformation
• Maia documentation: Full SaaS vs Hybrid SaaS
• Maia documentation: Connectors overview
• Maia documentation: Free trial

Checked October 2026.

How we research comparisons: our editorial method.