Head to head · Figures checked September 2026
Apache Auron vs Databricks Photon
ETL Compare staff · Figures checked September 2026 · Sourced from vendor docs, project pages and public benchmarks · Published 29 September 2026
In brief
Apache Auron has the higher ETL fit score on our published weights (3.5 against 3.4 out of 5). Apache Auron scores higher on adoption effort, platform and instance portability, cost model transparency and published evidence; Databricks Photon scores higher on tuning and operating burden and maturity and community. They tie on stage coverage. Photon only runs inside Databricks, so for most teams the platform they use decides this comparison before the scores do.
On our weights Apache Auron ranks 5th of 7 and Databricks Photon ranks 6th 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. Databricks Photon is a Databricks-native vectorized C++ query engine inside the Databricks Runtime. 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
3.4 / 5
ETL fit score (editorial assessment, 0-5)ETL fit score (editorial assessment, 0-5)
Rank 6 of 7
Best for teams already on Databricks
How do Apache Auron and Databricks Photon score on each criterion?
| Criterion | Auron | Photon | Higher score |
|---|---|---|---|
| Adoption effort 20% | 3.4 | 2.6 | Auron |
| Stage coverage 20% | 3.8 | 3.8 | Tie |
| Tuning and operating burden 15% | 2.6 | 4.6 | Photon |
| Platform and instance portability 10% | 3.8 | 1.6 | Auron |
| Cost model transparency 10% | 5.0 | 3.2 | Auron |
| Published evidence 15% | 3.4 | 3.2 | Auron |
| Maturity and community 10% | 3.2 | 4.6 | Photon |
| ETL fit score | 3.5 | 3.4 | Auron |
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.
- Photon (2.6): One checkbox (or on by default on serverless) with no code changes if you already run on Databricks; for a team on EMR or self-managed Spark it means moving to Databricks first.
- Stage coverage
- Auron (3.8): Native vectorized execution on DataFusion, compacted shuffle formats and multi-level memory management are all documented.
- Photon (3.8): Documents native scans (Parquet, Delta, CSV, JSON), filters, joins, aggregates, windows and writes, with fallback to the Spark runtime; no UDF, RDD or stateful streaming support.
- Tuning and operating burden
- Auron (2.6): Self-managed with community support through the Apache mailing list.
- Photon (4.6): Managed by Databricks inside its runtime, so there is no plugin for your team to install or version.
- 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.
- Photon (1.6): Available only inside Databricks (on AWS, Azure and Google Cloud).
- Cost model transparency
- Auron (5.0): Apache License 2.0, no license fee.
- Photon (3.2): Databricks publishes DBU rates, but Photon compute consumes DBUs at different rates, so the net effect has to be modelled per job.
- 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.
- Photon (3.2): Databricks documents where Photon does not help (queries under two seconds, UDFs); performance comparisons outside Databricks are not possible because it only runs there.
What do Apache Auron and Databricks Photon cost, as published?
| Auron | Photon | |
|---|---|---|
| License | Apache License 2.0 | Commercial (part of Databricks) |
| Published price | No license fee | Databricks DBU pricing; Photon changes DBU consumption |
| Instances | Standard CPU instances | Supported Databricks instance types, including Graviton |
| Runs on | Self-managed Spark on JDK 8, 11, 17 or 21 | Databricks only (AWS, Azure, Google Cloud) |
| Code changes | None; Spark settings and custom shuffle manager | None on Databricks; platform move otherwise |
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, Photon documentation · Fetched 27 Sep 2026
Where are they documented to run?
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 |
|---|---|---|---|---|---|
| Auron | Partial | Documented | Documented | Partial | Not stated |
| Photon | Documented | Documented | Not stated | Not stated | Documented |
- 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 Databricks Photon if
- Your jobs already run on Databricks and you want no plugin to install or version
- You want the engine that is the default on Databricks serverless and SQL warehouses
- You run on Databricks
Frequently asked questions
Which has the higher ETL fit score, Apache Auron or Databricks Photon?
Apache Auron, with 3.5 against 3.4 out of 5 on our published weights. Apache Auron scores higher on adoption effort, platform and instance portability, cost model transparency and published evidence and Databricks Photon on tuning and operating burden and maturity and community. They tie on stage coverage. The score measures fit for speeding up existing Spark ETL, not raw speed.
Do Apache Auron and Databricks Photon run on the same platforms?
They share no documented platform on the pages we reviewed. Apache Auron is also documented for self-managed Spark or Kubernetes. Databricks Photon is also documented for Databricks. Open-source plugins without a guide for a platform can often be self-installed where you control Spark configuration.
What do Apache Auron and Databricks Photon cost?
Apache Auron: No license fee (Apache License 2.0). Databricks Photon: Databricks DBU pricing; Photon changes DBU consumption (Commercial (part of Databricks)). For a like-for-like comparison, work out cost per run on one of your own jobs: see Spark cost per job, explained.