AWS Glue alternatives

4 tools to consider instead of AWS Glue, shown against it.

AWS Glue Azure Data Factory Google Cloud Dataflow Matillion Talend
Vendor Amazon Web Services Microsoft Google Matillion Qlik
Pricing model Usage-based Usage-based Usage-based Quote only Quote only
Free tier Yes Yes No No No
Deployment Cloud Cloud Cloud Cloud Cloud, Self-hosted
Open source No No No No No
Best for AWS-native teams needing serverless, pay-per-use ETL and a shared metadata catalog across analytics services. Organizations standardized on Azure needing pipeline orchestration across cloud and on-premises sources. Teams running Apache Beam pipelines that need one engine for both batch and streaming without managing infrastructure. Analytics engineering teams wanting a visual, low-code ELT builder with warehouse-native transformation. Larger enterprises needing integration bundled with data quality and governance, not just pipeline movement.
Pricing

Billed per Data Processing Unit-hour (DPU-hour) for ETL jobs and crawlers, by the second; Data Catalog storage and requests are free up to the first million objects/accesses monthly.

ETL jobs & crawlers $0.44 per DPU-hour
Data Catalog Free for first 1M objects and 1M requests/month
DataBrew $1.00 per 30-min interactive session; $0.48 per node-hour for jobs

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Consumption pricing billed separately for pipeline orchestration (per 1,000 runs), data movement (per Data Integration Unit-hour), Data Flow execution (per vCore-hour) and external activity execution; first five low-frequency activities per month are free.

Orchestration & execution Billed per 1,000 activity/trigger/debug runs
Data movement Billed per Data Integration Unit-hour (DIU-hour)
Data Flow execution Billed per vCore-hour

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Metered pricing billed for worker vCPU-hours and memory GiB-hours, plus separate per-GB charges for Dataflow Shuffle (batch) or Streaming Engine (streaming) data processed; rates vary by region and machine type.

Batch, streaming & FlexRS Usage-based: billed per vCPU-hour and per GiB-hour of worker memory
Dataflow Shuffle / Streaming Engine Usage-based: billed per GB of data processed

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Consumption-based credit system tied to pipeline task hours and developer seats; only the entry Developer tier's trial is self-serve, the rest require a sales quote.

Developer Free trial
Teams Contact sales
Scale Contact sales

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Capacity-based subscription across four editions (Starter, Standard, Premium, Enterprise), metered by data volume, job executions and duration; no public price list.

Checked on the vendor's own page on September 21, 2026: no prices are published. Expect to be quoted.

Features
  • Serverless Apache Spark and Python ETL jobs
  • Auto-populated Hive-compatible Data Catalog
  • Crawlers for automatic schema discovery
  • Visual job authoring via Glue Studio
  • Zero-ETL integrations with other AWS services
  • Glue DataBrew for no-code data preparation
  • Schema Registry for streaming data
  • Visual pipeline designer and code-first authoring
  • Spark-backed Mapping Data Flows
  • Self-Hosted Integration Runtime for on-premises data
  • 90+ built-in connectors
  • Native orchestration of Databricks and HDInsight activities
  • Git-based CI/CD integration
  • Trigger-based and event-based scheduling
  • Unified batch and streaming via Apache Beam
  • Fully managed autoscaling, no cluster management
  • Streaming Engine for offloaded state management
  • Dataflow Shuffle for offloaded batch shuffle
  • Flexible Resource Scheduling (FlexRS) for cheaper batch runs
  • Native integration with BigQuery and Pub/Sub
  • Low-code drag-and-drop pipeline canvas
  • Push-down transformation execution in the warehouse
  • Built-in Git version control
  • SQL and Python scripting components
  • Native dbt Core integration
  • Streaming change data capture (Scale tier)
  • Cross-cloud support for Snowflake, Databricks, BigQuery, Redshift
  • ETL/ELT pipeline design
  • Change data capture at scale (Standard+)
  • Data quality and profiling tools
  • Master data management
  • Data governance and cataloging (Enterprise)
  • Broad target/format support for transformation
  • AI-assisted data quality features

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