Head to head · Figures checked September 2026
DataPelago 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
Databricks Photon has the higher ETL fit score on our lead weights (3.46 against 3.14 out of 5). DataPelago scores higher on adoption effort and platform and instance portability; Databricks Photon scores higher on stage coverage, tuning and operating burden, cost model transparency, published evidence and maturity and community. Photon only runs inside Databricks, so for most teams the platform they use decides this comparison before the scores do.
On our lead weights Databricks Photon ranks 5th of 9 and DataPelago ranks 8th of 9. Which one fits depends on the criteria below and on the platform you run.
DataPelago Accelerator for Spark is a commercial Spark accelerator (DPA-S) built on the DataPelago Nucleus engine, using CPU, GPU or FPGA hardware. 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.
3.14 / 5
ETL fit score (editorial assessment, 0-5)ETL fit score (editorial assessment, 0-5)
Rank 8 of 9
Compute-heavy Spark SQL and DataFrame jobs on Parquet or ORC in Google Cloud or AWS
3.46 / 5
ETL fit score (editorial assessment, 0-5)ETL fit score (editorial assessment, 0-5)
Rank 5 of 9
Best for teams already on Databricks
How do DataPelago and Databricks Photon score on each criterion?
| Criterion | DataPelago | Photon | Higher score |
|---|---|---|---|
| Adoption effort 20% | 3.8 | 2.6 | DataPelago |
| Stage coverage 20% | 3.0 | 3.8 | Photon |
| Tuning and operating burden 20% | 3.0 | 4.6 | Photon |
| Platform and instance portability 10% | 3.2 | 1.6 | DataPelago |
| Cost model transparency 5% | 3.0 | 3.2 | Photon |
| Published evidence 15% | 3.0 | 3.2 | Photon |
| Maturity and community 10% | 2.6 | 4.6 | Photon |
| ETL fit score | 3.14 | 3.46 | Photon |
Why each score
- Adoption effort
- DataPelago (3.8): DataPelago states existing Spark code runs unchanged with no data migration, configured through standard Spark settings and bought through the Google Cloud or AWS marketplace.
- 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
- DataPelago (3.0): Its page names good fits (compute-intensive jobs, Parquet, ORC, SQL and DataFrame) and says to avoid heavy RDD and UDF use; shuffle, spill and write behavior are not described.
- 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
- DataPelago (3.0): A vendor-run accelerator configured with standard Spark settings; tuning guidance and support terms are not published on the pages we reviewed.
- 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
- DataPelago (3.2): Supports multiple Spark versions on Google Cloud and AWS and any hardware from CPU to GPU, by the vendor's statement; specific managed services such as Amazon EMR, Dataproc or Databricks are not named.
- Photon (1.6): Available only inside Databricks (on AWS, Azure and Google Cloud).
- Cost model transparency
- DataPelago (3.0): Pay as you go through cloud marketplaces; the AWS Marketplace listing shows a vCPU-hour dimension and a $100,000 per month contract entry, but no per-job rate you can model without a quote.
- 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
- DataPelago (3.0): Vendor claims of 10x faster at 80% cost reduction, customer quotes with figures (Akad Seguros, ShareChat) and a benchmark page against NVIDIA cuDF, all vendor-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.
- Maturity and community
- DataPelago (2.6): The Accelerator for Spark launched on 5 August 2025, on the Nucleus engine unveiled in October 2024; offices in Mountain View and Hyderabad; no open-source community of its own.
- Photon (4.6): The default engine on Databricks serverless and SQL warehouses, backed by Databricks.
What do DataPelago and Databricks Photon cost, as published?
| DataPelago | Photon | |
|---|---|---|
| License | Commercial | Commercial (part of Databricks) |
| Published price | Pay as you go through the Google Cloud and AWS marketplaces; the AWS Marketplace listing shows a vCPU-hour dimension and a $100,000 per month one-month contract entry | Databricks DBU pricing; Photon changes DBU consumption |
| Instances | Any available hardware, CPU to GPU (vendor statement) | Supported Databricks instance types, including Graviton |
| Runs on | Multiple Spark versions on Google Cloud and AWS; Amazon EMR, EKS and Databricks not named on the DPA-S page | Databricks only (AWS, Azure, Google Cloud) |
| Code changes | None stated; standard Spark settings | 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: datapelago.ai, DPA-S page, DataPelago press page, DataPelago company page, AWS Marketplace listing, Photon documentation · Fetched 27 Sep 2026
Where are they documented to run?
| EMR | Databricks | Google Cloud (Dataproc) | AWS Glue | Self-managed Spark / Kubernetes | |
|---|---|---|---|---|---|
| DataPelago | Not documentedAWS stated; EMR not named | Not documented | Not documentedGoogle Cloud stated; service not named | Not documented | Not documented |
| Photon | Not available | Only platform | Not available | Not available | Not available |
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 |
|---|---|---|---|---|---|
| Photon | Documented | Documented | Not stated | Not stated | Documented |
| DataPelago | Documented | Partial | 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 DataPelago if
- Low adoption effort on your current platform matters most
- You need to run on several platforms or standard instances
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, DataPelago or Databricks Photon?
Databricks Photon, with 3.46 against 3.14 out of 5 on our lead weights. DataPelago scores higher on adoption effort and platform and instance portability and Databricks Photon on stage coverage, tuning and operating burden, cost model transparency, published evidence and maturity and community. The score measures fit for speeding up existing Spark ETL, not raw speed.
Do DataPelago and Databricks Photon run on the same platforms?
They share no documented platform on the pages we reviewed. Databricks Photon is also documented for Databricks.
What do DataPelago and Databricks Photon cost?
DataPelago: Pay as you go through the Google Cloud and AWS marketplaces; the AWS Marketplace listing shows a vCPU-hour dimension and a $100,000 per month one-month contract entry (Commercial). 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.