Head to head · Figures checked September 2026
Apache Auron vs Apache Gluten
ETL Compare staff · Figures checked September 2026 · Sourced from vendor docs, project pages and public benchmarks · Published 29 September 2026
In brief
Apache Gluten has the higher ETL fit score on our published weights (3.9 against 3.5 out of 5). Apache Gluten scores higher on adoption effort, platform and instance portability, published evidence and maturity and community, and Apache Auron does not score higher on any criterion. They tie on stage coverage, tuning and operating burden and cost model transparency. The score measures fit for speeding up existing Spark ETL without rewrites, not raw speed, so test both on one of your own pipelines before you decide.
On our weights Apache Gluten ranks 1st of 7 and Apache Auron ranks 5th of 7. Which one fits depends on the criteria below and on the platform you run.
Apache Auron (formerly Blaze) is an incubating open-source engine that maps Spark physical plans onto DataFusion native execution, with its own shuffle format. Apache Gluten (with Velox) is an open-source plugin that offloads Spark SQL execution to a native C++ engine (Velox or ClickHouse). Neither requires changes to Spark SQL or DataFrame code, according to its own documentation.
3.5 / 5
ETL fit score (editorial assessment, 0-5)ETL fit score (editorial assessment, 0-5)
Rank 5 of 7
Open-source option with its own shuffle and memory layer
3.9 / 5
ETL fit score (editorial assessment, 0-5)ETL fit score (editorial assessment, 0-5)
Rank 1 of 7
Highest ETL fit score: open-source native engine for self-managed Spark
How do Apache Auron and Apache Gluten score on each criterion?
| Criterion | Auron | Gluten + Velox | Higher score |
|---|---|---|---|
| Adoption effort 20% | 3.4 | 3.8 | Gluten + Velox |
| Stage coverage 20% | 3.8 | 3.8 | Tie |
| Tuning and operating burden 15% | 2.6 | 2.6 | Tie |
| Platform and instance portability 10% | 3.8 | 4.3 | Gluten + Velox |
| Cost model transparency 10% | 5.0 | 5.0 | Tie |
| Published evidence 15% | 3.4 | 3.8 | Gluten + Velox |
| Maturity and community 10% | 3.2 | 4.4 | Gluten + Velox |
| ETL fit score | 3.5 | 3.9 | Gluten + Velox |
Why each score
- Adoption effort
- Auron (3.4): No code changes, but you enable it through Spark settings and a custom shuffle manager and install it yourself.
- Gluten + Velox (3.8): No application code changes; you add the Gluten JAR, set spark.plugins, enable off-heap memory and switch the shuffle manager, per the Velox getting-started page.
- Stage coverage
- Auron (3.8): Native vectorized execution on DataFusion, compacted shuffle formats and multi-level memory management are all documented.
- Gluten + Velox (3.8): Offloads execution to the Velox native engine with a columnar shuffle manager; spilling is documented as experimental.
- Tuning and operating burden
- Auron (2.6): Self-managed with community support through the Apache mailing list.
- Gluten + Velox (2.6): You size off-heap memory yourself (the docs example uses 20g) and rely on community channels; no vendor support contract is described on the project pages.
- Platform and instance portability
- Auron (3.8): Runs on standard CPU instances and supports JDK 8, 11, 17 and 21; the project says it is adapted to Spark mainline versions without listing them on the page we reviewed.
- Gluten + Velox (4.3): Supports Spark 3.4, 3.5, 4.0 and 4.1 on x86_64 and aarch64 Linux on standard CPU instances; managed-platform guides are not listed, so you install it yourself.
- Cost model transparency
- Auron (5.0): Apache License 2.0, no license fee.
- Gluten + Velox (5.0): Apache License 2.0, no license fee; cost is your existing compute plus engineering time.
- Published evidence
- Auron (3.4): The project states about 2x faster than Spark 3.5 on TPC-DS with about 50% cluster resources saved; project-published.
- Gluten + Velox (3.8): Publishes TPC-H and TPC-DS results (Velox backend 2.71x overall, tested June 2023) with the benchmark named; results come from the project.
- Maturity and community
- Auron (3.2): Still incubating at the Apache Software Foundation (latest listed release v8.0.0-incubating), with a history under the Blaze name.
- Gluten + Velox (4.4): Apache top-level project since March 2026, started by Intel and Kyligence in 2022, with contributors including Alibaba Cloud, Meituan, Microsoft, IBM and Google.
What do Apache Auron and Apache Gluten cost, as published?
| Auron | Gluten + Velox | |
|---|---|---|
| License | Apache License 2.0 | Apache License 2.0 |
| Published price | No license fee | No license fee |
| Instances | Standard CPU instances | Standard CPU instances (x86_64 or aarch64) |
| Runs on | Self-managed Spark on JDK 8, 11, 17 or 21 | Self-managed Spark 3.4 to 4.1 on Linux, on any platform where you control Spark config |
| Code changes | None; Spark settings and custom shuffle manager | None; JAR plus Spark configuration |
Prices and terms as published on the pages we reviewed, 27 September 2026. Neither vendor's figure is a quote.
Sources: auron.apache.org, apache/auron on GitHub, kwai/blaze on GitHub, gluten.apache.org, Velox backend getting started, apache/incubator-gluten on GitHub · Fetched 27 Sep 2026
Where are they documented to run?
| EMR | Databricks | Google Cloud (Dataproc) | AWS Glue | Self-managed Spark / Kubernetes | |
|---|---|---|---|---|---|
| Auron | Not documentedself-install | Not documented | Not documentedself-install | Not documented | Documented |
| Gluten + Velox | Not documentedself-install | Not documented | Not documentedself-install | Not documented | Documented |
Documented means the vendor or project lists the platform on the pages we reviewed. Self-install means you can usually add an open-source plugin to a platform that lets you set Spark configuration and classpath, but the project does not publish a guide for that platform. None of the vendor pages we reviewed list AWS Glue.
Which job stages does each address?
| Accelerator | Read | Transform | Shuffle | Spill | Write |
|---|---|---|---|---|---|
| Gluten + Velox | Partial | Documented | Documented | Partial | Not stated |
| Auron | Partial | Documented | Documented | Partial | Not stated |
- Documented: the vendor's own documentation says it addresses this stage
- Partial: indirect or experimental, or covered only by an end-to-end claim
- Not stated: not found on the pages we reviewed
This shows what each vendor says, not what we measured. Sources are listed on each review.
Choose Apache Auron if
- You want no license fee: Auron is released under the Apache License 2.0
- You want an open-source engine with its own compacted shuffle format and multi-level memory management
- You run on self-managed Spark or Kubernetes
Choose Apache Gluten if
- You want no license fee: Gluten is released under the Apache License 2.0
- You want the most widely contributed open-source native engine in this set, an Apache top-level project since March 2026
- You run on self-managed Spark or Kubernetes
Frequently asked questions
Which has the higher ETL fit score, Apache Auron or Apache Gluten?
Apache Gluten, with 3.9 against 3.5 out of 5 on our published weights. Apache Gluten scores higher on adoption effort, platform and instance portability, published evidence and maturity and community, and Apache Auron does not score higher on any criterion. They tie on stage coverage, tuning and operating burden and cost model transparency. The score measures fit for speeding up existing Spark ETL, not raw speed.
Do Apache Auron and Apache Gluten run on the same platforms?
Both are documented for self-managed Spark or Kubernetes. Open-source plugins without a guide for a platform can often be self-installed where you control Spark configuration.
What do Apache Auron and Apache Gluten cost?
Apache Auron: No license fee (Apache License 2.0). Apache Gluten: No license fee (Apache License 2.0). For a like-for-like comparison, work out cost per run on one of your own jobs: see Spark cost per job, explained.