You’re comparing Weld vs Alooma vs Mozart Data. Explore how they differ on connectors, pricing, and features. 
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| Feature | Weld | Alooma | Mozart Data |
|---|---|---|---|
| Core Platform | |||
| Price | $79 / 5M Active Rows | N/A (product retired; GCP service pricing applies) | Starts around $1,000/mo (includes Snowflake + ETL up to ~250k MAR) |
| Free tier | No | No | Yes |
| Location | DK, (EU) | Sunnyvale, CA, USA (pre-acquisition) | US |
| Connectors & Sync | |||
| Connectors | 200+ | 100+ | 150+ |
| 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 | Yes |
| AI Assistant | Yes | No | No |
| dbt Core Integration | Yes | No | Yes |
| dbt Cloud Integration | Yes | No | No |
| Governance & DevOps | |||
| Orchestration | Yes | Yes | Yes |
| Lineage | Yes | No | No |
| Version control | Yes | No | No |
| On-Premise | No | No | No |
| OpenAPI / Developer API | Yes | No | No |
| Integrations | |||
| Load to/from Excel | Yes | No | Yes |
| Load to/from Google Sheets | Yes | No | Yes |
| Ratings | |||
| G2 rating | 4.8 | — | 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
Alooma (acquired by Google Cloud in 2019) was a streaming ETL platform that enabled real-time ingestion of data from various sources into BigQuery. It provided a visual pipeline editor to map, transform, and route data with minimal code, automatically handling schema changes and ensuring exactly-once delivery. While Alooma as a standalone product is retired, many of its features have been integrated into Google Cloud’s Dataflow and Pub/Sub pipelines.
Real-time streaming ETL with automatic schema drift handling.
Minimal coding: visual pipeline UI with built-in connectors to databases, Kafka, APIs, and SaaS apps.
Exactly-once delivery guarantees to BigQuery, eliminating duplicate data.
Standalone Alooma product is discontinued—functionality now lives in GCP services (e.g., Dataflow, Data Fusion).
Migrating legacy Alooma pipelines to GCP-native services requires rework, as UI and features differ from original Alooma.
Google Cloud’s Dataflow (Alooma integration):
“Alooma’s ease of connecting live streaming data sources directly into BigQuery with automated schema management was revolutionary for our real-time analytics.”
“Since Google integrated Alooma into its native services, the standalone product no longer exists, so new users must migrate to Dataflow or Data Fusion.”
Overview
Mozart Data is a managed data stack platform that combines ETL connectors (via embedded partners such as Fivetran and Portable), a fully managed Snowflake data warehouse, and dbt-based transformations in a single subscription. It is designed to help teams set up a complete analytics stack quickly without requiring engineering resources.
Managed Snowflake warehouse bundled with connectors and dbt transformations.
150+ connectors (via Fivetran and Portable integrations) managed behind the scenes.
Very fast onboarding—stack can be operational in under an hour.
Hands-on customer support and onboarding assistance through Mozart Assist.
Pricing scales with both warehouse compute and Monthly Active Rows, which can become costly at larger volumes.
Limited flexibility for custom connector development—requests must be routed through the Mozart team.
Smaller ecosystem and fewer third-party learning resources compared to standalone tools.
Mozart Data Reviews (G2):
“Mozart Data provided a turnkey stack where Snowflake, connectors, and transformations were already configured. We were able to start building dashboards rapidly without DevOps work.”
“Costs can increase with higher data volumes, and adding niche connectors typically requires requesting support from their team rather than configuring them directly.”
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.
Alooma’s web-based pipeline builder allowed users to drag-and-drop connectors for streaming or batch data, apply transformations, and route data to BigQuery with just a few clicks. The interface auto-generated SQL when possible.
Mozart Data abstracts away infrastructure management entirely. Users select sources through a simple UI, and the platform configures Snowflake, connectors, and transformations automatically, reducing setup time for non-technical teams.
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.
Alooma’s web-based pipeline builder allowed users to drag-and-drop connectors for streaming or batch data, apply transformations, and route data to BigQuery with just a few clicks. The interface auto-generated SQL when possible.
Mozart Data abstracts away infrastructure management entirely. Users select sources through a simple UI, and the platform configures Snowflake, connectors, and transformations automatically, reducing setup time for non-technical teams.
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.
No longer available as a separate product. Users adopt equivalent GCP services (Dataflow, Data Fusion) which have pay-as-you-go pricing under the GCP pricing model.
Mozart uses a bundled pricing model that starts around $1,000 per month for smaller workloads. It can be cost-effective for teams that value reduced operational overhead but may be more expensive for high-volume or highly custom requirements.
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.
No longer available as a separate product. Users adopt equivalent GCP services (Dataflow, Data Fusion) which have pay-as-you-go pricing under the GCP pricing model.
Mozart uses a bundled pricing model that starts around $1,000 per month for smaller workloads. It can be cost-effective for teams that value reduced operational overhead but may be more expensive for high-volume or highly custom requirements.
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.
Alooma supported real-time ingestion from Kafka, databases (MySQL, PostgreSQL), logs, REST APIs, and SaaS apps, with built-in transformations (masking, enrichment). It automatically handled schema changes, and could write to BigQuery partitions.
The platform includes a managed Snowflake warehouse, automated ELT connectors via partners, dbt-based transformations, monitoring tools, and scheduling capabilities. It supports incremental loading and basic orchestration without additional infrastructure.
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.
Alooma supported real-time ingestion from Kafka, databases (MySQL, PostgreSQL), logs, REST APIs, and SaaS apps, with built-in transformations (masking, enrichment). It automatically handled schema changes, and could write to BigQuery partitions.
The platform includes a managed Snowflake warehouse, automated ELT connectors via partners, dbt-based transformations, monitoring tools, and scheduling capabilities. It supports incremental loading and basic orchestration without additional infrastructure.
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.
Users could write custom JavaScript transforms or Python UDFs for complex logic. The platform managed infrastructure, but custom connectors required Eloqua code or support.
Mozart supports SQL and dbt for transformations but restricts more advanced customization. New connectors or unsupported APIs require submitting a request, and users cannot directly create custom connectors or deploy code-based logic inside the platform.
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.
Users could write custom JavaScript transforms or Python UDFs for complex logic. The platform managed infrastructure, but custom connectors required Eloqua code or support.
Mozart supports SQL and dbt for transformations but restricts more advanced customization. New connectors or unsupported APIs require submitting a request, and users cannot directly create custom connectors or deploy code-based logic inside the platform.
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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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