Head to head · Figures checked September 2026
Apache Auron 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
RAPIDS Accelerator has the higher ETL fit score on our published weights (3.7 against 3.5 out of 5). Apache Auron scores higher on platform and instance portability and cost model transparency; RAPIDS Accelerator scores higher on stage coverage, tuning and operating burden, published evidence and maturity and community. They tie on adoption effort. 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 RAPIDS Accelerator ranks 3rd 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. 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.
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
3.7 / 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 Auron and RAPIDS Accelerator score on each criterion?
| Criterion | Auron | RAPIDS Accelerator | Higher score |
|---|---|---|---|
| Adoption effort 20% | 3.4 | 3.4 | Tie |
| Stage coverage 20% | 3.8 | 4.2 | RAPIDS Accelerator |
| Tuning and operating burden 15% | 2.6 | 3.0 | RAPIDS Accelerator |
| Platform and instance portability 10% | 3.8 | 3.2 | Auron |
| Cost model transparency 10% | 5.0 | 4.2 | Auron |
| Published evidence 15% | 3.4 | 3.6 | RAPIDS Accelerator |
| Maturity and community 10% | 3.2 | 4.6 | RAPIDS Accelerator |
| ETL fit score | 3.5 | 3.7 | RAPIDS Accelerator |
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.
- 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
- Auron (3.8): Native vectorized execution on DataFusion, compacted shuffle formats and multi-level memory management are all documented.
- 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
- Auron (2.6): Self-managed with community support through the Apache mailing list.
- RAPIDS Accelerator (3.0): Qualification and Profiling tools help, but GPU sizing and plugin configuration are your team's job.
- 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.
- 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
- Auron (5.0): Apache License 2.0, no license fee.
- 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
- Auron (3.4): The project states about 2x faster than Spark 3.5 on TPC-DS with about 50% cluster resources saved; project-published.
- 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
- Auron (3.2): Still incubating at the Apache Software Foundation (latest listed release v8.0.0-incubating), with a history under the Blaze name.
- 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 Auron and RAPIDS Accelerator cost, as published?
| Auron | RAPIDS Accelerator | |
|---|---|---|
| License | Apache License 2.0 | Apache License 2.0 |
| Published price | No license fee | No plugin fee; GPU instance pricing applies |
| Instances | Standard CPU instances | NVIDIA GPU instances (Volta or later) |
| Runs on | Self-managed Spark on JDK 8, 11, 17 or 21 | Amazon EMR, Databricks, Dataproc, GKE, Azure Synapse, Kubernetes, on-premises, OCI |
| Code changes | None; Spark settings and custom shuffle manager | None; 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: auron.apache.org, apache/auron on GitHub, kwai/blaze 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?
| EMR | Databricks | Google Cloud (Dataproc) | AWS Glue | Self-managed Spark / Kubernetes | |
|---|---|---|---|---|---|
| Auron | Not documentedself-install | Not documented | Not documentedself-install | Not documented | Documented |
| RAPIDS Accelerator | Documented | Documented | Documented | 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 |
|---|---|---|---|---|---|
| RAPIDS Accelerator | Documented | Documented | Documented | Not stated | Documented |
| 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 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 Auron or RAPIDS Accelerator?
RAPIDS Accelerator, with 3.7 against 3.5 out of 5 on our published weights. Apache Auron scores higher on platform and instance portability and cost model transparency and RAPIDS Accelerator on stage coverage, tuning and operating burden, published evidence and maturity and community. They tie on adoption effort. The score measures fit for speeding up existing Spark ETL, not raw speed.
Do Apache Auron 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 Auron and RAPIDS Accelerator cost?
Apache Auron: 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.