Hevo Data VS Meltano

Hevo is a cloud-based ETL/ELT service for building data pipelines that, unlike Fivetran, started as a cloud service in 2017, making it more mature than Airbyte. Like Fivetran, Hevo is designed for “low code”, though it does provide a little more control to map sources to targets, or add simple transformations using Python scripts or a new drag-and-drop editor in ETL mode. Stateful transformations such as joins or aggregations, like Fivetran, should be done using ELT with SQL or dbt.

While Hevo is a good option for someone getting started with ELT, as one user put it, “Hevo has its limits”.

• Ease of use: Like several other modern ELT tools, Hevo is intuitive and easy to use, especially compared to traditional ETL tools. 
• ELT and ETL: Hevo has started to add ETL support including Python scripts and a new drag-and-drop editor. This is limited mostly to row-level transformations. Hevo’s main transformation support is dbt (ELT).
• Reverse ETL: Hevo supports the ability to insert source data back into the source once it’s been cleansed. This might be good for you if you’re looking for this feature. It is a very specific use case where you write modified data back directly into the source. A more general-purpose solution is to have a pipeline write back to the sources, which is not supported by most modern ETL/ELT vendors. It is supported by iPaaS vendors.

• Connectivity: Hevo has one of the lowest number of connectors at slightly over 150. You should consider what sources and destinations you need for your current and future projects to make sure it will support your needs. 
• Latency: Hevo is still mostly batch-based connectors on a streaming Kafka backbone. While data is converted into “events” that are streamed, and streams can be processed if scripts are written for any basic row-level transforms, Hevo connectors to sources, even when CDC is used, is batch. There are starting to be a few exceptions. For example, you can use the streaming API in BigQuery, not just the Google Cloud Storage staging area. But you still have a 5 minute or more delay at the source. Also, there is currently no common  scheduler. Each source and target frequency is different. So latency can be longer than the source or target when they operate at different intervals.  
• Costs: Hevo can be comparable to Estuary for low data volumes in the low GBs per month. But it becomes more expensive than Estuary and Airbyte as you reach 10s of GBs a month. Costs will also be much more as you lower latency because several Hevo connectors do not fully support incremental extraction. As you reduce your extract interval you capture more events multiple times, which can make costs soar.
• Reliability: CDC is batch mode only, with the minimum interval being 5 minutes. This can load the source and even cause failures. Customers have complained about Hevo bugs that make it into production and cause downtime.
• Scalability: Hevo has several limitations around scale. Some are adjustable. For example, you can get the 50MB Excel, and 5GB CSV/TSV file limits increased by contacting support. 
  But most limitations are not adjustable, like column limits. MongoDB can hit limits more often than others. A standalone MongoDB instance without replicas is not supported. You need 72 hours or more of OpsLog retention. And there is a 4090 columns limit that is more easily hit with MongoDB documents. 
  There are ingestion limits that cause issues, like a 25 million row limit per table on initial ingestion. In addition there are scheduling limits that customers hit, like not being able to have more than 24 custom times.
  For API calls, you cannot make more than 100 API calls per minute.
• DataOps: Like Airbyte, Hevo is not a great option for those trying to automate data pipelines. There is no CLI or “as code” automation support with Hevo. You can map to a destination table manually, which can help. But while there is some built-in schema evolution that happens when you turn on auto mapping, you cannot fully automate schema evolution or control the rules. There is no schema testing or evolution control. New tables can be passed through, but many column changes can lead to data not getting loaded in destinations and moved to a failed events table that must be fixed within 30 days or the data is permanently lost. Hevo used to support a concept of internal workflows, but it has been discontinued for new users. You cannot modify folder names for the same “events”. 

Hevo is more expensive than Airbyte and Estuary, but still less expensive than Fivetran and various ETL vendors.

• Free: Limited to 1 million free events per month with free initial load, 50+ connectors, and unlimited models
• Starter ($239/mo for 5M rows): Offers 150+ connectors, on-demand events, and 12 hours of support as an SLA. Additional rows are $10 or more per million (~1GB)
• Business (Custom Pricing): HIPAA compliance with a dedicated data architect and dedicated account manager

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 the right-time data platform that replaces fragmented data stacks with one dependable system for data movement. Teams use it to move data from databases, SaaS apps, files, and streams into warehouses, lakes, operational stores, and AI systems at the cadence they choose: sub-second streaming, near real-time, or scheduled batch. Founded in 2019, Estuary is built on Gazette, an open-source streaming broker developed by the same founding team that lets Estuary mix CDC, streaming, and batch in a single catalog with exactly-once delivery, deterministic recovery, and targeted backfills.

Unlike traditional ELT tools that focus on batch loads, Estuary stores every event in collections that can be reused for multiple destinations. Captured changes are written once to durable storage and fanned out to any number of targets without reloading the source, which reduces load on primary systems and makes replay easy when schemas change. Estuary runs as a multi-tenant cloud service, private data plane, or BYOC, and ships with 200+ fully-managed native connectors plus support for open-source Airbyte, Meltano, and Stitch connectors.

For AI-native workflows, Estuary ships Agent Skills that work with Claude Code, Cursor, OpenAI Codex, GitHub Copilot, and Gemini CLI, letting developers create captures, materialize into Snowflake, BigQuery, Redshift, or Databricks, and troubleshoot pipelines through natural-language prompts. A separate MCP server handles docs-aware Q&A inside the same assistants.

Customers include Glossier, which cut data costs by 50%; Xometry, which reduced integration costs by 60% with private deployment; Headset, which cut Snowflake ingestion costs by 40% after replacing Airbyte; and Prodege, which built Apache Iceberg pipelines.

• Right-time pipelines from millisecond streaming to scheduled batch. Choose cadence per pipeline so cost and freshness match each workload. Most ELT tools default to 15-minute or hourly intervals.
• One platform for CDC, batch, and streaming. Replaces the typical 3-4 tool stack of Debezium plus Kafka plus Airbyte plus dbt with a single system, reducing tool sprawl and operational overhead.
• Dependable replication built on Gazette. Exactly-once delivery, deterministic recovery, and targeted backfills keep pipelines stable through schema changes and source failures.
• Efficient log-based CDC with collection reuse. Captures inserts, updates, and deletes once, then fans out to any number of destinations without re-reading the source database, reducing load on production systems.
• Predictable usage-based pricing. $0.50 per GB moved plus $100 per connector instance per month for the first 6 instances, then $50 per instance for additional ones. No MAR-based surprises and no per-row charges.
• Agent-native developer experience. Open-source Agent Skills let Claude Code, Cursor, OpenAI Codex, GitHub Copilot, and Gemini CLI build and operate Estuary pipelines from natural language, with an MCP server for docs-aware Q&A in the same tools.

• No graphical transformation UI. Estuary focuses on SQL and TypeScript transformations alongside dbt integration. Teams that need point-and-click visual ETL like Matillion or Informatica PowerCenter will find this a gap, though dbt covers most warehouse-side needs.
• On-premises connectivity is narrower than legacy ETL vendors. For mainframe, SAP ECC on-premises, or other proprietary on-premises systems, vendors like Informatica or Talend may have broader native coverage. Verify legacy on-premises coverage during evaluation.
• Smaller market presence than category incumbents. Fivetran, Informatica (now Salesforce), and Talend (now Qlik) have larger enterprise customer bases and longer procurement track records. Estuary fits teams able to evaluate on technical merit, but buyers requiring a Gartner Magic Quadrant leader may need to factor this in.

Estuary uses a straightforward usage-based pricing model. Data movement is charged at $0.50 per GB sourced or delivered. Connector instances are $100 per month for the first 6 instances, then $50 per month for each additional instance. A Developer tier is free indefinitely up to 10 GB per month and 2 concurrent connector instances, and Cloud-tier customers can request a 30-day free trial.

Use the Estuary pricing calculator to model your specific workload. For larger deployments, Enterprise plans add volume-based discounts, SOC 2 and HIPAA compliance reports, SSO, custom SLA terms, private deployments, and dedicated support.