Head to head · Figures checked September 2026
Flarion 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 3.1 out of 5). Flarion scores higher on adoption effort and tuning and operating burden; Apache Gluten scores higher on stage coverage, platform and instance portability, cost model transparency, published evidence 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 Flarion ranks 7th of 7. Which one fits depends on the criteria below and on the platform you run.
Flarion is a commercial DataFusion-based, Arrow-native execution engine for Spark, Hadoop and Ray. 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.
3.1 / 5
ETL fit score (editorial assessment, 0-5)ETL fit score (editorial assessment, 0-5)
Rank 7 of 7
Commercial plugin with a broad managed-platform list
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 Flarion and Apache Gluten score on each criterion?
| Criterion | Flarion | Gluten + Velox | Higher score |
|---|---|---|---|
| Adoption effort 20% | 4.2 | 3.8 | Flarion |
| Stage coverage 20% | 2.8 | 3.8 | Gluten + Velox |
| Tuning and operating burden 15% | 3.8 | 2.6 | Flarion |
| Platform and instance portability 10% | 4.2 | 4.3 | Gluten + Velox |
| Cost model transparency 10% | 2.0 | 5.0 | Gluten + Velox |
| Published evidence 15% | 1.8 | 3.8 | Gluten + Velox |
| Maturity and community 10% | 2.0 | 4.4 | Gluten + Velox |
| ETL fit score | 3.1 | 3.9 | Gluten + Velox |
Why each score
- Adoption effort
- Flarion (4.2): Flarion states "zero code and infrastructure changes" and a 6-minute setup by adding configuration parameters.
- 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
- Flarion (2.8): Describes a DataFusion-based, Arrow-native execution engine plus caching; shuffle, spill and write behavior are not described on the pages we reviewed.
- 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
- Flarion (3.8): A commercial product with performance monitoring and anomaly detection described; support terms are not published.
- 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
- Flarion (4.2): Lists Databricks, AWS EMR, GCP Dataproc, Azure HDInsight and on-premises, on standard instances.
- 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
- Flarion (2.0): Listed on AWS Marketplace; no price on the homepage.
- Gluten + Velox (5.0): Apache License 2.0, no license fee; cost is your existing compute plus engineering time.
- Published evidence
- Flarion (1.8): Headline figures (3x performance, 60% cost reduction) without a published methodology on the pages we reviewed.
- 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
- Flarion (2.0): Company details such as founding date and funding are not published on the pages we reviewed.
- 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 Flarion and Apache Gluten cost, as published?
| Flarion | Gluten + Velox | |
|---|---|---|
| License | Commercial | Apache License 2.0 |
| Published price | Not on homepage; listed on AWS Marketplace | No license fee |
| Instances | Standard instances | Standard CPU instances (x86_64 or aarch64) |
| Runs on | Databricks, AWS EMR, GCP Dataproc, Azure HDInsight, on-premises | Self-managed Spark 3.4 to 4.1 on Linux, on any platform where you control Spark config |
| Code changes | None stated; configuration parameters | 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: flarion.io, 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 | |
|---|---|---|---|---|---|
| Flarion | Documented | Documented | Documented | Not documented | Documentedon-premises |
| 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 |
| Flarion | Partial | Documented | Not stated | Not stated | 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 Flarion if
- You want a commercial plugin that Flarion states needs zero code and infrastructure changes
- You need one commercial product across Databricks, Amazon EMR, Google Cloud Dataproc, Azure HDInsight and on-premises
- You run on Amazon EMR, Databricks, Google Cloud (Dataproc) and 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, Flarion or Apache Gluten?
Apache Gluten, with 3.9 against 3.1 out of 5 on our published weights. Flarion scores higher on adoption effort and tuning and operating burden and Apache Gluten on stage coverage, platform and instance portability, cost model transparency, published evidence and maturity and community. The score measures fit for speeding up existing Spark ETL, not raw speed.
Do Flarion and Apache Gluten run on the same platforms?
Both are documented for self-managed Spark or Kubernetes. Flarion 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 Flarion and Apache Gluten cost?
Flarion: Not on homepage; listed on AWS Marketplace (Commercial). 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.