You’re comparing Weld vs Google Cloud Dataflow vs Informatica Cloud. Explore how they differ on connectors, pricing, and features. 
Loved by data teams from around the world
| Feature | Weld | Google Cloud Dataflow | Informatica Cloud |
|---|---|---|---|
| Core Platform | |||
| Price | $79 / 5M Active Rows | Billed per vCPU-second, memory, and storage; ~$0.0106 per vCPU-minute, with additional streaming costs | Subscription-based (custom quotes); typically starts around ~$20k/year for core ETL usage |
| Free tier | No | No | No |
| Location | DK, (EU) | GCP Global (multi-region) | US |
| Connectors & Sync | |||
| Connectors | 200+ | 30+ | 200+ |
| 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 | No |
| dbt Cloud Integration | Yes | No | No |
| Governance & DevOps | |||
| Orchestration | Yes | No | Yes |
| Lineage | Yes | No | Yes |
| Version control | Yes | No | Yes |
| On-Premise | No | No | No |
| OpenAPI / Developer API | Yes | No | Yes |
| Integrations | |||
| Load to/from Excel | Yes | Yes (via CSVs in Cloud Storage) | Yes (flat-file connectors) |
| Load to/from Google Sheets | Yes | No | Yes (Google Sheets connector) |
| Ratings | |||
| G2 rating | 4.8 | 4.5 | 4.4 |
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
Google Cloud Dataflow is a fully managed batch and stream data processing service built on Apache Beam. It enables developers to write pipelines in Python or Java using Beam’s unified programming model, which Dataflow executes on serverless, autoscaling infrastructure. It integrates natively with GCP services including Pub/Sub, BigQuery, and Cloud Storage, supporting large-scale ETL workloads with dynamic scaling and built-in streaming features.
Unified batch and streaming data processing model via Apache Beam SDK.
Serverless execution with autoscaling and dynamic work rebalancing.
Native integration with Pub/Sub, BigQuery, Cloud Storage, Spanner, and more.
Supports exactly-once processing, windowing, triggers, and stateful operations for streaming workloads.
Steep learning curve due to Apache Beam concepts (PCollections, DoFns, pipelines).
Debugging and monitoring streaming jobs can be complex and requires multiple console tools.
Costs can rise quickly for high-throughput streaming workloads without careful optimization.
G2 Reviews:
“Google Cloud Dataflow automatically optimizes and manages resources. It supports multiple programming languages including Python and Java, making it easy for developers to focus on writing code.”
“It can be costly compared to other solutions, especially for long-running streaming pipelines.”
Overview
Informatica Intelligent Cloud Services (IICS) is a cloud-native integration platform (iPaaS) offering ETL/ELT, application integration, API management, and data quality capabilities in a unified SaaS environment. It provides hundreds of connectors for SaaS applications, cloud databases, and on-premises systems through Secure Agents. The platform supports both batch and real-time integrations, with a low-code visual designer, workflow orchestration, and monitoring dashboards.
Hundreds of SaaS, cloud database, and on-prem connectors via Secure Agents.
Unified services: ETL/ELT, data quality, application integration, API management, and B2B/EDI.
Low-code, drag-and-drop mapping designer with templates for common integrations.
Hybrid integration support through Secure Agents for connecting cloud and on-prem systems.
Pricing can be difficult to estimate due to multiple components (connectors, environments, usage).
High-throughput workloads often require premium add-ons for optimized performance.
Learning curve for advanced features such as API Designer, Data Quality rules, and orchestration.
G2 reviews:
“Some standout aspects include a user-friendly interface, scalability with a cloud-native architecture, and strong automation and scheduling features.”
“Informatica CDI is quite expensive compared to other cloud-based data integration tools, particularly for smaller organizations.”
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.
Dataflow pipelines are authored programmatically in Java or Python through Apache Beam. There is no drag-and-drop UI, developers write, test, and debug pipelines in code and monitor them via Cloud Console. This provides flexibility but requires engineering skill.
The IICS web console provides a unified low-code interface for building mappings and tasks. Pre-built templates help accelerate onboarding, though data quality and advanced API features require additional learning.
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.
Dataflow pipelines are authored programmatically in Java or Python through Apache Beam. There is no drag-and-drop UI, developers write, test, and debug pipelines in code and monitor them via Cloud Console. This provides flexibility but requires engineering skill.
The IICS web console provides a unified low-code interface for building mappings and tasks. Pre-built templates help accelerate onboarding, though data quality and advanced API features require additional learning.
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.
Dataflow uses per-vCPU-second and memory pricing. Streaming pipelines incur continuous charges. Autoscaling and FlexRS discount options help reduce cost, but inefficient pipelines can lead to high spend, particularly for real-time workloads.
Pricing follows a subscription model with fees for environments, connectors, and usage. While comprehensive for enterprise needs, smaller teams may find the entry price high.
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.
Dataflow uses per-vCPU-second and memory pricing. Streaming pipelines incur continuous charges. Autoscaling and FlexRS discount options help reduce cost, but inefficient pipelines can lead to high spend, particularly for real-time workloads.
Pricing follows a subscription model with fees for environments, connectors, and usage. While comprehensive for enterprise needs, smaller teams may find the entry price high.
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.
Key features include the unified batch and streaming model, windowing, triggers, exactly-once semantics, autoscaling, dynamic work rebalancing, FlexRS for discounted batch processing, and Dataflow SQL for SQL-based pipeline authoring. Integrates closely with Pub/Sub and BigQuery.
Core features include ETL/ELT mappings, real-time and application integrations, API management, data quality and profiling, data masking, and B2B/EDI. Includes monitoring dashboards, alerts, and SLA management.
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.
Key features include the unified batch and streaming model, windowing, triggers, exactly-once semantics, autoscaling, dynamic work rebalancing, FlexRS for discounted batch processing, and Dataflow SQL for SQL-based pipeline authoring. Integrates closely with Pub/Sub and BigQuery.
Core features include ETL/ELT mappings, real-time and application integrations, API management, data quality and profiling, data masking, and B2B/EDI. Includes monitoring dashboards, alerts, and SLA management.
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.
Custom transformations, UDFs, and stateful processing are supported through Apache Beam. Pipelines can integrate with VPC, IAM, and KMS for security. Advanced workloads requiring custom logic or connectors are fully supported through Beam’s programming APIs.
Custom connectors can be created via REST or SOAP. Secure Agents allow hybrid integration with on-prem systems. Mapping Designer supports extensibility through Java or Groovy expressions.
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.
Custom transformations, UDFs, and stateful processing are supported through Apache Beam. Pipelines can integrate with VPC, IAM, and KMS for security. Advanced workloads requiring custom logic or connectors are fully supported through Beam’s programming APIs.
Custom connectors can be created via REST or SOAP. Secure Agents allow hybrid integration with on-prem systems. Mapping Designer supports extensibility through Java or Groovy expressions.
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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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