ETL CompareETL Compare

Head to head · Figures checked September 2026

Apache Gluten vs RAPIDS Accelerator

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 3.7 out of 5). Apache Gluten scores higher on adoption effort, platform and instance portability, cost model transparency and published evidence; RAPIDS Accelerator scores higher on stage coverage, tuning and operating burden and maturity and community. 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 RAPIDS Accelerator ranks 3rd of 7. Which one fits depends on the criteria below and on the platform you run.

Apache Gluten (with Velox) is an open-source plugin that offloads Spark SQL execution to a native C++ engine (Velox or ClickHouse). RAPIDS Accelerator for Apache Spark (now NVIDIA cuDF for Apache Spark) is an open-source NVIDIA plugin that runs supported Spark SQL and DataFrame operations on GPUs. Neither requires changes to Spark SQL or DataFrame code, according to its own documentation.

Apache Gluten

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

RAPIDS Accelerator

3.7 / 5

ETL fit score (editorial assessment, 0-5)

ETL fit score (editorial assessment, 0-5)

Rank 3 of 7

Best for teams that already run GPU capacity

How do Apache Gluten and RAPIDS Accelerator score on each criterion?

Apache Gluten and RAPIDS Accelerator by criterion. Scores are editorial, 0-5.
CriterionGluten + VeloxRAPIDS AcceleratorHigher score
Adoption effort 20%3.83.4Gluten + Velox
Stage coverage 20%3.84.2RAPIDS Accelerator
Tuning and operating burden 15%2.63.0RAPIDS Accelerator
Platform and instance portability 10%4.33.2Gluten + Velox
Cost model transparency 10%5.04.2Gluten + Velox
Published evidence 15%3.83.6Gluten + Velox
Maturity and community 10%4.44.6RAPIDS Accelerator
ETL fit score3.93.7Gluten + Velox
Why each score
Adoption effort
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.
RAPIDS Accelerator (3.4): No code changes, but the job has to move to NVIDIA GPU instances and the cluster needs GPU-specific configuration.
Stage coverage
Gluten + Velox (3.8): Offloads execution to the Velox native engine with a columnar shuffle manager; spilling is documented as experimental.
RAPIDS Accelerator (4.2): Documents GPU execution for group by, joins, sorts and windows, Parquet and ORC writing, CSV reading and a RAPIDS Shuffle Manager; unsupported operations fall back to CPU.
Tuning and operating burden
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.
RAPIDS Accelerator (3.0): Qualification and Profiling tools help, but GPU sizing and plugin configuration are your team's job.
Platform and instance portability
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.
RAPIDS Accelerator (3.2): The widest platform list in this set (EMR, Databricks, Dataproc, GKE, Azure Synapse, Kubernetes, on-premises, OCI), held back because every one of them needs GPU instances.
Cost model transparency
Gluten + Velox (5.0): Apache License 2.0, no license fee; cost is your existing compute plus engineering time.
RAPIDS Accelerator (4.2): The plugin is Apache 2.0 with no fee; the cost question becomes GPU instance price against runtime saved, which you can model from public cloud prices.
Published evidence
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.
RAPIDS Accelerator (3.6): The Qualification Tool estimates fit from your own event logs; headline benchmark figures were not reviewed for this edition.
Maturity and community
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.
RAPIDS Accelerator (4.6): A long-running NVIDIA project with more than 9,000 commits on main, now published as NVIDIA cuDF for Apache Spark.

What do Apache Gluten and RAPIDS Accelerator cost, as published?

 Gluten + VeloxRAPIDS Accelerator
LicenseApache License 2.0Apache License 2.0
Published priceNo license feeNo plugin fee; GPU instance pricing applies
InstancesStandard CPU instances (x86_64 or aarch64)NVIDIA GPU instances (Volta or later)
Runs onSelf-managed Spark 3.4 to 4.1 on Linux, on any platform where you control Spark configAmazon EMR, Databricks, Dataproc, GKE, Azure Synapse, Kubernetes, on-premises, OCI
Code changesNone; JAR plus Spark configurationNone; plugin replaces internal physical plan parts

Prices and terms as published on the pages we reviewed, 27 September 2026. Neither vendor's figure is a quote.

Sources: gluten.apache.org, Velox backend getting started, apache/incubator-gluten on GitHub, spark-rapids overview, RAPIDS Accelerator user guide, RAPIDS Accelerator FAQ, NVIDIA/spark-rapids on GitHub · Fetched 27 Sep 2026

Where are they documented to run?

 EMRDatabricksGoogle Cloud (Dataproc)AWS GlueSelf-managed Spark / Kubernetes
Gluten + VeloxNot documentedself-installNot documentedNot documentedself-installNot documentedDocumented
RAPIDS AcceleratorDocumentedDocumentedDocumentedNot documentedDocumented

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?

Spark ETL job stages and which stages each accelerator documents addressingA waterfall of five Spark ETL job stages (read, transform, shuffle, spill, write) with illustrative proportions, and below it a grid showing, for each accelerator, whether its own documentation says it addresses that stage.Anatomy of a Spark ETL jobRead: scan and decode filesTransform: filter, join, aggregateShuffle: write and fetch between stagesSpill: memory pressure pushes data to diskWrite: encode and commit outputIllustrative proportions, not measured data. Your own split comes from the Spark UI: see Profile a slow Spark job.
Stage coverage by accelerator, from vendor documentation
AcceleratorReadTransformShuffleSpillWrite
Gluten + VeloxPartialPartialDocumentedDocumentedDocumentedDocumentedPartialPartialNot statedNot stated
RAPIDS AcceleratorDocumentedDocumentedDocumentedDocumentedDocumentedDocumentedNot statedNot statedDocumentedDocumented
  • DocumentedDocumented: the vendor's own documentation says it addresses this stage
  • PartialPartial: indirect or experimental, or covered only by an end-to-end claim
  • Not statedNot 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 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

Choose RAPIDS Accelerator if

  • You want a long-running project with more than 9,000 commits on main
  • You want GPU execution documented for joins, sorts, aggregations, window functions, Parquet and ORC writing and shuffle
  • You run on Amazon EMR, Databricks, Google Cloud (Dataproc) and self-managed Spark or Kubernetes

Frequently asked questions

Which has the higher ETL fit score, Apache Gluten or RAPIDS Accelerator?

Apache Gluten, with 3.9 against 3.7 out of 5 on our published weights. Apache Gluten scores higher on adoption effort, platform and instance portability, cost model transparency and published evidence and RAPIDS Accelerator on stage coverage, tuning and operating burden and maturity and community. The score measures fit for speeding up existing Spark ETL, not raw speed.

Do Apache Gluten and RAPIDS Accelerator run on the same platforms?

Both are documented for self-managed Spark or Kubernetes. RAPIDS Accelerator is also documented for Amazon EMR, Databricks and Google Cloud (Dataproc). Open-source plugins without a guide for a platform can often be self-installed where you control Spark configuration.

What do Apache Gluten and RAPIDS Accelerator cost?

Apache Gluten: No license fee (Apache License 2.0). RAPIDS Accelerator: No plugin fee; GPU instance pricing applies (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.

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