You’re comparing Weld vs Google Cloud Dataflow vs Twilio Segment. Explore how they differ on connectors, pricing, and features. 
Loved by data teams from around the world
| Feature | Weld | Google Cloud Dataflow | Twilio Segment |
|---|---|---|---|
| Core Platform | |||
| Price | $79 / 5M Active Rows | Billed per vCPU-second, memory, and storage; ~$0.0106 per vCPU-minute, with additional streaming costs | $120/month for 10,000 visitors |
| Free tier | No | No | Yes |
| Location | DK, (EU) | GCP Global (multi-region) | US |
| Connectors & Sync | |||
| Connectors | 200+ | 30+ | 300+ |
| Extract data (ETL) | Yes | Yes | Yes |
| Sync to HubSpot, Salesforce, Klaviyo, Excel (reverse ETL) | Yes | No | Yes |
| Two-Way Sync | Yes | No | No |
| Transformations & AI | |||
| Transformations | Yes | Yes | No |
| AI Assistant | Yes | No | No |
| dbt Core Integration | Yes | No | Yes |
| dbt Cloud Integration | Yes | No | Yes |
| Governance & DevOps | |||
| Orchestration | Yes | No | No |
| Lineage | Yes | No | No |
| Version control | Yes | No | No |
| On-Premise | No | No | No |
| OpenAPI / Developer API | Yes | No | Yes |
| Integrations | |||
| Load to/from Excel | Yes | Yes (via CSVs in Cloud Storage) | No |
| Load to/from Google Sheets | Yes | No | No |
| Ratings | |||
| G2 rating | 4.8 | 4.5 | 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
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
Twilio Segment is a customer data platform (CDP) focused on collecting, unifying, and activating customer event data across products and channels. It centralizes behavioral data from web, mobile, and server sources, and uses identity resolution to build unified customer profiles. Segment is heavily oriented toward marketing, product, and analytics use cases, offering pre-built integrations and Reverse ETL capabilities to sync cleaned data back into operational tools. While powerful for customer data workflows, Segment is less suited for teams looking for a general-purpose ELT platform.
Strong real-time event tracking and data collection
Large catalog of fully managed marketing and product integrations
Identity resolution and audience management features
Reverse ETL powered by native integrations and dbt model sync
Becomes expensive quickly as usage scales
Not ideal as a standalone ELT platform
Optimized for marketing and product tools rather than general data engineering
Customization and complex integrations can require significant setup effort
A reviewer on G2 said::
“Segment connects to all the various platforms that we're using which makes it very easy to send data around. No engineering needed.”
“Pricing could be a bit more affordable, especially if you want to use ETL processes.”
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.
Segment offers an accessible interface for collecting and routing customer data, but advanced setups—such as identity merging, custom sources, or Reverse ETL modeling—can require more technical configuration.
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.
Segment offers an accessible interface for collecting and routing customer data, but advanced setups—such as identity merging, custom sources, or Reverse ETL modeling—can require more technical configuration.
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.
Segment provides a free tier, but pricing scales rapidly with event volume and advanced features. Many businesses find it expensive once usage grows or enterprise features are enabled.
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.
Segment provides a free tier, but pricing scales rapidly with event volume and advanced features. Many businesses find it expensive once usage grows or enterprise features are enabled.
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.
Segment includes real-time event tracking, identity resolution, profiles, and activation capabilities. It offers extensive sources and destinations, but it is less suited for traditional ELT or broader data engineering workflows.
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.
Segment includes real-time event tracking, identity resolution, profiles, and activation capabilities. It offers extensive sources and destinations, but it is less suited for traditional ELT or broader data engineering workflows.
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.
While Segment is flexible for customer-data workflows, it is more limited outside those use cases. Custom pipelines, transformations, or complex modeling typically require external tools like dbt or a data warehouse.
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.
While Segment is flexible for customer-data workflows, it is more limited outside those use cases. Custom pipelines, transformations, or complex modeling typically require external tools like dbt or a data warehouse.
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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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