Google Cloud Dataflow alternatives

4 tools to consider instead of Google Cloud Dataflow, shown against it.

Google Cloud Dataflow AWS Glue Azure Data Factory Apache Kafka Google Cloud Pub/Sub
Vendor Google Amazon Web Services Microsoft Apache Software Foundation Google
Pricing model Usage-based Usage-based Usage-based Open source + paid options Usage-based
Free tier No Yes Yes Yes Yes
Deployment Cloud Cloud Cloud Self-hosted Cloud
Open source No No No Yes (Apache-2.0) No
Best for Teams running Apache Beam pipelines that need one engine for both batch and streaming without managing infrastructure. 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. Engineering teams building event-driven architectures who want full control over a self-managed streaming backbone. GCP-native teams needing serverless, low-maintenance event ingestion feeding analytics pipelines.
Pricing

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.

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.

Free and open source with no vendor pricing; operating costs depend entirely on self-hosted infrastructure or a chosen managed distribution.

Pricing has not been verified yet — see the vendor's site.

Billed per TiB of message throughput delivered, plus per GiB-month for retained message storage; the first 10 GiB of standard throughput per month is free.

Message delivery $40 per TiB
BigQuery / Cloud Storage subscriptions $50 per TiB
Storage $0.27 per GiB-month

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

Features
  • 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
  • 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
  • Partitioned, replicated, append-only topic logs
  • Horizontal scalability across brokers
  • KRaft consensus (ZooKeeper-free clusters)
  • Kafka Connect for source/sink integration
  • Kafka Streams for stream processing
  • Exactly-once delivery semantics
  • Long-term event retention and replay
  • Serverless, autoscaling topics and subscriptions
  • Push and pull delivery
  • Exactly-once processing support
  • Message ordering keys
  • Direct BigQuery and Cloud Storage subscriptions
  • Global availability and replication

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