You’re comparing Weld vs AWS Glue vs Meltano. Explore how they differ on connectors, pricing, and features. 
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| Feature | Weld | AWS Glue | Meltano |
|---|---|---|---|
| Core Platform | |||
| Price | $79 / 5M Active Rows | $0.44 per DPU-hour (plus job runtime costs) | Free (self-hosted), Meltano Cloud is custom pricing |
| Free tier | No | Yes | Yes |
| Location | DK, (EU) | AWS Global (multi-region) | US |
| Connectors & Sync | |||
| Connectors | 200+ | 50+ | 600+ |
| Extract data (ETL) | Yes | Yes | Yes |
| Sync to HubSpot, Salesforce, Klaviyo, Excel (reverse ETL) | Yes | No | No |
| Two-Way Sync | Yes | No | No |
| Transformations & AI | |||
| Transformations | Yes | Yes | No |
| AI Assistant | Yes | No | No |
| dbt Core Integration | Yes | Yes | Yes |
| dbt Cloud Integration | Yes | No | No |
| Governance & DevOps | |||
| Orchestration | Yes | Yes | Yes |
| Lineage | Yes | Yes | No |
| Version control | Yes | No | Yes |
| On-Premise | No | No | Yes |
| OpenAPI / Developer API | Yes | No | Yes |
| Integrations | |||
| Load to/from Excel | Yes | Via S3 CSV ingestion | No |
| Load to/from Google Sheets | Yes | No | No |
| Ratings | |||
| G2 rating | 4.8 | 4.1 | 4.6 |
Overview
Weld is a unified ELT and data activation platform that combines ingestion, modeling, transformations, orchestration, lineage, and reverse ETL in a single SaaS interface. With premium in-house–built connectors, an intuitive UI, and near real-time syncs, Weld enables both technical and non-technical users to create and manage data workflows efficiently. Weld also includes an AI assistant to support SQL modeling, generate transformations, and streamline repetitive tasks. Teams can ingest data from a wide range of sources—including marketing platforms, CRMs, databases, Google Sheets, Excel, and APIs—into their cloud data warehouse and activate it back into business tools.
Lineage, orchestration, and workflow features included by default
Handles large datasets and near real-time data sync
ELT and reverse ETL in one platform
User-friendly interface with minimal setup required
Flat, predictable monthly pricing model
200+ in-house–built, high-quality connectors
AI assistant for modeling and transformations
Some SQL knowledge is useful for advanced modeling
Optimized for cloud-warehouse workflows (Snowflake, BigQuery, Redshift, etc.)
Feature set is streamlined for modern ELT/activation use cases
A reviewer on G2 said:
Overview
AWS Glue is a fully managed, serverless ETL platform from AWS that automates data discovery, cataloging, and transformation using the Glue Data Catalog and Apache Spark (PySpark/Scala). It integrates tightly with AWS services such as S3, Redshift, RDS, and DynamoDB, and offers both batch and streaming ETL. Glue includes visual tools like Glue Studio and low-code data preparation via DataBrew, along with job scheduling, orchestration, and CloudWatch monitoring.
Serverless architecture—no infrastructure to manage; AWS automatically provisions compute (Spark-based).
Glue Data Catalog provides schema discovery, metadata storage, versioning, and integration with Athena and Redshift Spectrum.
Supports Python (PySpark) and Scala for complex ETL with transformation APIs.
Deep integration with AWS services including CloudWatch, IAM, S3 events, Step Functions, and Redshift.
Costs can be unpredictable for long-running or resource-intensive jobs due to DPU billing.
Debugging jobs can be challenging; logs spread across CloudWatch and Spark outputs.
Connecting on-prem or multi-cloud sources often requires additional networking configuration.
G2 Reviews:
“My team built a framework in AWS Glue to fetch data from multiple platforms and store it in S3 in the format we specified. It streamlined our integration and data collection.”
“It does not support XML file formats.”
Overview
Meltano is an open-source data integration and orchestration platform built around the Singer ecosystem. It focuses on engineering workflows, version control, and reproducible pipelines. Meltano offers extensive connector coverage through Singer taps and targets, along with strong support for dbt, Airflow, and Dagster. While it provides high flexibility for technical teams, it requires more setup and maintenance than fully managed ELT tools.
Open-source platform
Large connector coverage via the Singer ecosystem
SDK for building and maintaining Singer taps/targets
Strong engineering workflow support (CLI-first, version-controlled)
Flexible deployment options including Meltano Cloud
No fully polished, mature managed option (Meltano Cloud is early-stage)
High maintenance requirements, especially for Singer connectors
Connector quality varies significantly across the community
No built-in reverse ETL or native transformations
Less suitable for non-technical teams
As a user on G2 puts it::
“All the managerial tasks are handled under the hood, leaving you to focus on getting or consuming the data you need.”
“With so many features baked into Meltano, navigating the documentation can be challenging. However, I've gotten around this by using Bing AI search, which brings me the answer immediately.”
Side-by-side
Weld’s interface is built for clarity and speed, enabling users with varying levels of technical experience to manage data pipelines and models efficiently. Its built-in lineage and orchestration tools provide transparency across workflows.
Glue Studio offers a visual job builder, while more advanced workflows require writing PySpark or Scala code. The AWS console can feel complex for users unfamiliar with AWS services.
Meltano is simple and easy to use for those with technical expertise, particularly due to its portability and command-line usability, but may be challenging for less technical users.
Side-by-side
Weld’s interface is built for clarity and speed, enabling users with varying levels of technical experience to manage data pipelines and models efficiently. Its built-in lineage and orchestration tools provide transparency across workflows.
Glue Studio offers a visual job builder, while more advanced workflows require writing PySpark or Scala code. The AWS console can feel complex for users unfamiliar with AWS services.
Meltano is simple and easy to use for those with technical expertise, particularly due to its portability and command-line usability, but may be challenging for less technical users.
Side-by-side
Weld offers a simple and predictable pricing model starting at $79 for 5 million active rows. This flat, usage-transparent structure makes budgeting straightforward for small and medium-sized teams.
Pricing is based on DPU-hours used by ETL jobs. Short jobs can be inexpensive, but longer Spark workloads or high-concurrency environments can increase costs without careful tuning.
Meltano is open-source and free to use, making it highly affordable, but requires significant investment in deployment and maintenance, especially without a fully managed option.
Side-by-side
Weld offers a simple and predictable pricing model starting at $79 for 5 million active rows. This flat, usage-transparent structure makes budgeting straightforward for small and medium-sized teams.
Pricing is based on DPU-hours used by ETL jobs. Short jobs can be inexpensive, but longer Spark workloads or high-concurrency environments can increase costs without careful tuning.
Meltano is open-source and free to use, making it highly affordable, but requires significant investment in deployment and maintenance, especially without a fully managed option.
Side-by-side
Weld provides ELT ingestion, SQL-based transformations, reverse ETL activation, data lineage, orchestration, and workflow management in a single platform. Its AI assistant accelerates modeling and transformation tasks.
AWS Glue includes the Glue Data Catalog, PySpark/Scala ETL jobs, Glue Studio for visual development, DataBrew for low-code data preparation, Glue Workflows for orchestration, and support for both batch and streaming ETL.
The platform offers extensive integration options, including support for data transformation and orchestration, but relies heavily on the Singer framework, which can limit capabilities.
Side-by-side
Weld provides ELT ingestion, SQL-based transformations, reverse ETL activation, data lineage, orchestration, and workflow management in a single platform. Its AI assistant accelerates modeling and transformation tasks.
AWS Glue includes the Glue Data Catalog, PySpark/Scala ETL jobs, Glue Studio for visual development, DataBrew for low-code data preparation, Glue Workflows for orchestration, and support for both batch and streaming ETL.
The platform offers extensive integration options, including support for data transformation and orchestration, but relies heavily on the Singer framework, which can limit capabilities.
Side-by-side
Users can model data using SQL enhanced by Weld’s AI assistant, automate workflows, and build custom connectors to any API. This provides strong flexibility for teams that want to tailor integrations and transformations within one platform.
Glue supports custom PySpark ETL scripts, additional Python libraries, event-based triggers, and integration with Lambda and Step Functions. Local development is possible but limited compared to full Spark environments.
Meltano is highly flexible for advanced users who can manage their own deployments and build on the platform, but it requires substantial maintenance and lacks a fully managed option.
Side-by-side
Users can model data using SQL enhanced by Weld’s AI assistant, automate workflows, and build custom connectors to any API. This provides strong flexibility for teams that want to tailor integrations and transformations within one platform.
Glue supports custom PySpark ETL scripts, additional Python libraries, event-based triggers, and integration with Lambda and Step Functions. Local development is possible but limited compared to full Spark environments.
Meltano is highly flexible for advanced users who can manage their own deployments and build on the platform, but it requires substantial maintenance and lacks a fully managed option.
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CUSTOMER STORIES
“One of the biggest impacts has been unlocking new ways to buy media. Before, we didn’t have the data to back up strategic decisions – now we do.”
“Before Weld, we had to rely on custom Python scripts and manual processes that were time-consuming and error-prone.”
“We’re still a team of three, but we’re often doing far more than the equivalent of three full-time employees. That’s down to how we're able to leverage systems, data, and processes.”
“We’ve always been a KPI-driven company. But we wanted to scale that mindset across every team member, every team, every decision.”
“Before Weld, our data infrastructure was limited and we relied heavily on Excel files and custom Python scripts.”
“Weld has saved us a ton of time, from not having data ready to having a fully functional data warehouse and connectors.”
“By integrating Weld, Amazon S3, and Databricks, Soundboks built a modern data pipeline that automates data ingestion, improves reporting, and provides up-to-date visibility into sales performance”
“We didn’t have a good data setup, so we lacked the business insights we needed. Weld has allowed us to set up a structured data infrastructure and access insights quickly.”
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