Fivetran VS Meltano

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.

Meltano was founded in 2018 as an open source project within GitLab to support their data and analytics team. It’s a Python framework built on the Singer protocol. The Singer framework was originally created by the founders of Stitch, but their contribution slowly declined following the acquisition of Stitch by Talend (which in turn was later acquired by Qlik).

Meltano is focused on configuration-based ELT using YAML and the CLI.

• Open source ELT: Meltano is the main successor to Stitch if you’re looking for a Singer-based framework.
• Configuration-driven: If you are looking for a configure-driven approach to ELT, Meltano may be a great option for you.
• Connectivity: Meltano and Airbyte collectively have the most connectors, which makes sense given their open source history with Singer. Meltano supports Singer and has an SDK wrapper for Airbyte, giving it 600+ open source connectors in total. Open source connectors have their limits, so it’s important to test out carefully based on your needs.

• Not low-code: If you’re looking for a more graphical, low-code approach to integration, Meltano is not a good choice.
• Latency: Meltano is batch-only. It does not support streaming. While you can reduce polling intervals down to seconds, there is no staging area. The extract and load intervals need to be the same. Meltano is best suited for supporting historical analytics for this reason.
• Reliability: Some will say Meltano has less issues when compared to Airybte. But it is open source. Connectors may not be maintained and if you have issues you can only rely on the open source community for support.
• Scalability: There isn’t as much documentation to help with scaling Meltano, and it’s not generally known for scalability, especially if you need low latency. Various benchmarks show that larger batch sizes deliver much better throughput. But it’s still not the level of throughput of Estuary or Fivetran. It’s generally minutes even in batch mode for 100K rows.
• ELT only: Meltano supports open source dbt and can import existing dbt projects. Its support for dbt is considered good. It also has the ability to extract data from dbt cloud. Meltano does not support ETL.
• Deployment options: Meltano is deployed as self-hosted open source. There is no Meltano Cloud, though Arch is offering a broader service with consulting.
• DataOps: Data engineers generally automate using the CLI or the Meltano API. While it is straightforward to automate pipelines, there isn’t much support for schema evolution and automating responses to schema changes.

Meltano is open source. There is no pricing. But it’s not really free. You’ll need to spend more on data engineering resources to stand up, build, and maintain Meltano. If you need scalability, there isn’t a lot of documentation on how to scale. Make sure you evaluate carefully and find some Meltano expertise.

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.