Matillion VS Rivery

Matillion ETL is an on-premises ETL platform that was founded before the advent of cloud data warehouses, and is still primarily on premises. But its main destinations today are cloud data warehouses such as Snowflake, Amazon Redshift, and Google BigQuery.

Matillion combines many features to extract, transform, and load (ETL) data. More recently Matillion has been adding cloud options as part of the Matillion Data Productivity Cloud. It consists of a Hub for administration and billing, a choice of working with the on-premises Matillion ETL deployed as “private cloud” or Matillion Data Loader, a free cloud batch and CDC replication tool built on Matillion ETL but lacking many of its capabilities including transforms.

As with most of the mature ETL tools, Matillion has a strong set of features, but is harder to learn and use and is more expensive.

Perhaps one of the biggest advantages of Matillion is its ETL and orchestration, especially when compared to various ELT tools.

• Advanced transforms: Matillion ETL supports a variety of transform options, from drag-and-drop to code editors for complex transformations.
• Orchestration: Matillion offers advanced graphical workflow design and orchestration.
• Pushdown optimization: Matillion ETL can push down transformations to the target data warehouse.
• Reverse ETL: Matillion provides the ability to extract data from a source, cleanse it, and insert data back into the source.

• SaaS: Matillion ETL, its flagship product, is on-premises only. It does offer Data Loader, which is built on ETL, as a free cloud service for replication. There is also integration between Matillion ETL and the Matillion Cloud Hub for billing. While you can migrate work in Data Loader to ETL if you choose, it is a migration from the cloud to your own managed environment. 
• Free tier: Matillion Data Loader is free, but it’s limited and doesn’t support transforms. This can make it challenging to fully evaluate the tool before committing to a paid plan.
• Connectors: Matillion has fewer connectors than most (150+ in total). You can invoke external APIs to access other systems, but access to all your sources and destinations can become an issue. Matillion is only used for loading data warehouses. 
• No CDC: Matillion ETL CDC, which was based on Amazon DMS (in turn based on Attunity) has been deprecated. So right now there is no CDC option with Matillion. 
• Schema evolution: Matillion does support adding columns to existing destination tables, deleting a column, and handling data type changes as sources change. But adding a table requires creating a new pipeline and there is no automation for schema evolution.
• dbt integration for SaaS: While Matillion ETL has a connector for dbt, there is no integration between Data Loader and dbt.
• Pricing: Compared to more modern ELT vendors, Matillion is expensive. It starts at $1000/month for 500 credits where each credit is a virtual core-hour similar to an AWS, Azure, or Google virtual core. This is really in the $1000s per month minimum. Data productivity Cloud consumes a credit per running task every 15 minutes, and only consumes when tasks are running. The smallest ETL unit is two cores, which means you consume 2 cores an hour, or nearly 3x the 500 credits every month.

Matillion doesn’t have a pay-as-you-go model. It starts at $1000/month for 500 credits where each credit is a virtual core-hour similar to an AWS, Azure, or Google virtual core. Pricing increases 25% per credit for advanced and 35% for enterprise with higher base commitments.

This is really in the $1000s per month minimum. Data productivity Cloud consumes a credit per running task every 15 minutes, and only consumes when tasks are running. The smallest ETL unit is two cores, which means you consume 2 cores an hour, or nearly 3x the 500 credits every month.

Rivery was founded in 2019. Since then it has grown to 100 people and 350+ customers. It’s a multi-tenant public cloud SaaS ELT platform. It has some ETL features, including inline Python transforms and reverse ETL. It supports workflows and can also load multiple destinations.

But Rivery is also similar to batch ELT. There are a few cases where Rivery is real-time at the source, such as with CDC, which is its own implementation. But even in that case it ends up being batch because it extracts to files and uses Kafka for file streaming to destinations which are then loaded in minimum intervals of 60, 15, and 5 minutes for the starter, professional, and enterprise plans.

If you’re looking for some ETL features and are OK with a public cloud-only option, Rivery is an option. It is less expensive than many ETL vendors, and also less expensive than Fivetran. But its pricing is medium-high for an ELT vendor.

Rivery's future offerings, policies, and pricing may be uncertain as they undergo an acquisition with Boomi.

• Modern data pipelines: Rivery is the one other modern data pipeline platform in this comparison along with Estuary.
• Transforms: You have an option of running Python (ETL) or SQL (ELT). You do need to make sure you use destination-specific SQL.
• Orchestration: Rivery lets you build workflows graphically.
• Reverse ETL: Rivery also supports reverse ETL.
• Load options: Rivery supports soft deletes (append only) and several update-in-place options including switch-merge (to merge updates from an existing table and switch), delete-merge (to delete older versions of rows), and a regular merge.
• Costs: Rivery is lower cost compared to other ETL vendors and Fivetran, though it is still higher than several ELT vendors.

• Batch only: While Rivery does extract from its CDC sources in real-time, which is the best approach, it does not support messaging sources or destinations, and only loads destinations in minimum intervals of 60 (Starter), 15 (Professional), or 5 (Enterprise) minutes.
• Data warehouse focus: While Rivery supports Postgres, Azure SQL, email, cloud storage, and a few other non data warehouse destinations, Rivery’s focus is data warehousing. It doesn’t support the other use cases as well.
• Public SaaS: Rivery is public cloud only. There is no private cloud or self-hosted option.
• Limited schema evolution: Rivery had good schema evolution support for its database sources. But the vast majority of its connectors are API-based, and those do not have good schema evolution support.

Rivery charges per credit, which is $0.75 for Starter, $1.25 for Professional, and negotiated for Enterprise. You pay 1 credit per 100MB of moved data from databases, and 1 credit per API call. There is no charge for connectors. If you have low data volumes this will work well. But by the time you’re moving 20GB per month it starts to get more expensive than some others.

Estuary is a managed data integration platform for CDC, streaming, and batch pipelines. It supports database CDC, SaaS and API ingestion, event streams, files, warehouses, data lakes, operational systems, and AI destinations, with delivery ranging from real-time streaming to scheduled batch intervals.

At the center of Estuary is its runtime, where captured data is written to durable collections and can be independently read by multiple destinations and transformations. This separates source capture from downstream delivery and allows data to be reused for additional destinations, backfills, and reprocessing without repeatedly extracting it from the source.

Estuary offers 200+ fully managed connectors, no-code capture and materialization setup through the web application, and streaming transformations using SQL, Python, and TypeScript. It also supports dbt Cloud, materialization triggers, Agent Skills for AI-assisted pipeline development, and Public, Private, and Bring Your Own Cloud (BYOC) deployment options.

• CDC, streaming, and batch in one platform: Estuary supports log-based CDC, SaaS and API ingestion, event streams, files, and scheduled batch movement for analytical and operational use cases.
• Real-time runtime with durable collections: Estuary's runtime separates captures from materializations through durable collections, allowing data to be reused for multiple destinations, transformations, backfills, and replay without re-extracting it from the source.
• Low-latency delivery: Supported real-time pipelines can deliver data with sub-second latency, while delivery can also be scheduled when lower freshness is sufficient.
• No-code setup with advanced transformation options: Pipelines can be configured through the web application, while SQL, Python, and TypeScript transformations, dbt Cloud, APIs, and flowctl support more advanced workflows.
• Built-in monitoring and AI-assisted operations: Estuary includes logs, latency and usage metrics, pipeline alerts, OpenMetrics integrations, and Agent Skills for AI-assisted pipeline development and troubleshooting.
• Flexible deployment and predictable pricing: Public, Private, and BYOC deployments provide different levels of infrastructure control, while pricing is based on data volume and prorated connector task hours rather than Monthly Active Rows.

• Smaller managed connector catalog than some large ELT platforms: Estuary offers 200+ fully managed connectors, but some larger platforms have broader packaged coverage, particularly for long-tail SaaS and legacy enterprise applications. Custom connector development is available with Enterprise.
• No visual transformation canvas: Pipeline setup is no-code, and fields can be selected or renamed in the UI, but more complex streaming transformations use SQL, Python, or TypeScript rather than a drag-and-drop transformation canvas.
• Advanced workflows have a learning curve: Basic capture and materialization pipelines can be built through the web application, but advanced derivations, schemas, CLI workflows, and GitOps-style deployments require more data engineering familiarity. Some Estuary users on G2 also mention a learning curve for more advanced configurations.

Estuary pricing is based on data volume moved and connector instance usage. Data movement is priced at $0.50 per GB. The first six connector instances are priced at $100 per month each, while additional connector instances are $50 per month each, with usage prorated based on active connector instances.

The permanent free tier includes up to 10 GB of data movement per month and two concurrent connector instances, while the 30-day trial provides access to the full Cloud plan.

Enterprise plans can include volume discounts, Private or BYOC deployments, custom SLAs, private networking, dedicated support, and custom connector development.

Because pricing is based on GB moved plus connector usage rather than row-change metrics such as MAR, costs are relatively predictable from data volume and connector usage.