Fivetran VS Rivery

Fivetran is a managed data movement platform founded in 2012. It focuses on automating data ingestion from databases, SaaS applications, files, and other sources into cloud warehouses, databases, and data lakes. In June 2026, Fivetran completed its merger with dbt Labs, bringing its data movement platform together with dbt's data transformation ecosystem.

Fivetran offers 700+ fully managed connectors, including standard and Lite connectors. Lite connectors are generally designed for narrower API-based use cases and may support fewer endpoints, but generally available Lite connectors are maintained and supported by Fivetran. For high-volume database replication, Fivetran also offers High-Volume Agent (HVA) connectors with log-based CDC for selected databases.

• Broad managed connector catalog: Fivetran offers 700+ fully managed connectors across SaaS applications, databases, files, warehouses, and other systems.
• Low operational overhead: Managed connectors automate extraction, retries, schema handling, and connector maintenance, reducing the amount of pipeline infrastructure teams need to operate themselves.
• Strong transformation ecosystem: Fivetran supports hosted dbt Core transformations, dbt Cloud and Coalesce orchestration, and pre-built data models. Fivetran and dbt Labs are now one company.
• CDC for database sources: Fivetran supports log-based CDC for supported databases, with HVA available for selected high-volume and lower-latency replication workloads.
• Flexible enterprise deployment: Hybrid Deployment allows data processing to remain inside the customer's environment while Fivetran manages orchestration through its cloud control plane.
• Data activation: Fivetran Activations, built from its acquisition of Census, supports reverse ETL from warehouses and lakes into business applications.

• MAR pricing can be difficult to predict: Fivetran primarily charges for data ingestion using Monthly Active Rows (MAR). Each connection contributes separately to MAR, and qualifying connections with 1 to 1 million MAR also have a monthly base charge. Recent Fivetran users on G2 have also cited unpredictable or rapidly increasing MAR costs for frequently updated datasets.
• Lowest sync frequencies are limited by plan and connector: Fivetran Standard supports 15-minute syncs, while 1-minute syncs require Enterprise or Business Critical and are not supported by every standard connector or any Lite connector.
• Real-time CDC has narrower coverage: Fivetran's High-Volume Agent (HVA) provides real-time log-based replication for selected high-volume database sources, rather than across its entire connector catalog.
• Hybrid deployments require customer-side infrastructure: Fivetran Hybrid Deployment runs data processing inside the customer's environment, but requires a Hybrid Deployment Agent on customer-managed Kubernetes, Docker, or Podman infrastructure.
• Support experience can vary: Fivetran provides managed support, but some recent G2 reviewers report difficulty reaching support or resolving issues beyond documentation-based troubleshooting.

Fivetran uses usage-based pricing. Data ingestion is primarily measured using Monthly Active Rows (MAR), which count distinct primary keys inserted, updated, or deleted and synced during a calendar month.

Fivetran offers Free, Standard, Enterprise, and Business Critical plans. The Free plan includes up to 500,000 connector MAR per month, while Standard includes 15-minute syncs and the managed connector catalog. Enterprise adds capabilities such as 1-minute syncs for eligible connectors, HVA, and additional governance features, while Business Critical adds advanced security and compliance capabilities.

Fivetran changed parts of its pricing model in 2025 and 2026. MAR volume tiering is now calculated at the connection level rather than across the entire account, and standard connections generating between 1 and 1 million MAR can have a minimum monthly connection charge. As a result, total cost depends not only on overall data volume but also on the number of connections and how frequently records change.

For those looking for Fivetran alternatives, it's worth considering solutions that offer lower costs, real-time streaming, or more flexibility in schema control.

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.