Head to head · Figures checked September 2026
Databricks Photon vs Speedata
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 2.61 out of 5). Databricks Photon scores higher on stage coverage, tuning and operating burden, cost model transparency, published evidence and maturity and community, and Speedata does not score higher on any criterion. They tie on adoption effort and platform and instance portability. 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 Speedata ranks 9th of 9. Which one fits depends on the criteria below and on the platform you run.
Databricks Photon is a Databricks-native vectorized C++ query engine inside the Databricks Runtime. Speedata is a Custom analytics silicon: the C200 Analytics Processing Unit (APU) card plus Dash, a Spark plugin that hooks the Catalyst optimizer. Neither requires changes to Spark SQL or DataFrame code, according to its own documentation.
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
2.61 / 5
ETL fit score (editorial assessment, 0-5)ETL fit score (editorial assessment, 0-5)
Rank 9 of 9
Teams that can add dedicated analytics hardware on premises
How do Databricks Photon and Speedata score on each criterion?
| Criterion | Photon | Speedata | Higher score |
|---|---|---|---|
| Adoption effort 20% | 2.6 | 2.6 | Tie |
| Stage coverage 20% | 3.8 | 2.6 | Photon |
| Tuning and operating burden 20% | 4.6 | 3.0 | Photon |
| Platform and instance portability 10% | 1.6 | 1.6 | Tie |
| Cost model transparency 5% | 3.2 | 2.0 | Photon |
| Published evidence 15% | 3.2 | 3.0 | Photon |
| Maturity and community 10% | 4.6 | 2.6 | Photon |
| ETL fit score | 3.46 | 2.61 | Photon |
Why each score
- Adoption effort
- 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.
- Speedata (2.6): Speedata states Dash drops into existing Spark environments with zero code changes, but the job has to move onto servers fitted with Speedata APU cards, bought pre-configured or through OEM partners such as HPE.
- Stage coverage
- 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.
- Speedata (2.6): The product page says Dash offloads compute-intensive work and that non-accelerable elements such as UDFs fall back to CPU; no operator list and no shuffle, spill or write detail are published.
- Tuning and operating burden
- Photon (4.6): Managed by Databricks inside its runtime, so there is no plugin for your team to install or version.
- Speedata (3.0): A vendor product with a Workload Analyzer that reads your Spark logs; you run dedicated hardware in your own data center, and support terms are not published.
- Platform and instance portability
- Photon (1.6): Available only inside Databricks (on AWS, Azure and Google Cloud).
- Speedata (1.6): Documented for on-premises servers with C200 cards, plus a February 2026 sovereign-cloud partnership with Nebul; Amazon EMR, EKS, Databricks and Kubernetes are not mentioned on the product page.
- Cost model transparency
- 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.
- Speedata (2.0): No pricing page and no public price for the software or the APU servers; buyers need a demo and a quote.
- Published evidence
- 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.
- Speedata (3.0): Vendor figures include 62.7x on production Spark workloads and 37 servers consolidated to 3; the site also cites a SIGMOD 2026 Best Paper whose Microsoft CoddSpeed benchmark features the APU. Setup detail for the vendor figures is not published.
- Maturity and community
- Photon (4.6): The default engine on Databricks serverless and SQL warehouses, backed by Databricks.
- Speedata (2.6): A commercial hardware vendor with Israeli founders; TechCrunch reported a $44M Series B in June 2025 (secondary source). Founding year is not stated and there is no open-source community.
What do Databricks Photon and Speedata cost, as published?
| Photon | Speedata | |
|---|---|---|
| License | Commercial (part of Databricks) | Commercial |
| Published price | Databricks DBU pricing; Photon changes DBU consumption | Not published; book a demo |
| Instances | Supported Databricks instance types, including Graviton | Servers fitted with Speedata C200 APU cards |
| Runs on | Databricks only (AWS, Azure, Google Cloud) | Speedata APU servers in your data center (pre-configured 2U server or OEM servers such as HPE); Amazon EMR, EKS and Databricks not mentioned on the product page |
| Code changes | None on Databricks; platform move otherwise | None stated; Dash Spark plugin (spark.plugins) |
Prices and terms as published on the pages we reviewed, 27 September 2026. Neither vendor's figure is a quote.
Sources: Photon documentation, speedata.io, Speedata product page, Speedata news, Speedata company page · 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 |
|---|---|---|---|---|---|
| Photon | Documented | Documented | Not stated | Not stated | Documented |
| Speedata | Partial | 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 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
Choose Speedata if
- You want the least tuning and operating work for your team
- You want published, reproducible evidence
- You run on self-managed Spark or Kubernetes
Frequently asked questions
Which has the higher ETL fit score, Databricks Photon or Speedata?
Databricks Photon, with 3.46 against 2.61 out of 5 on our lead weights. Databricks Photon scores higher on stage coverage, tuning and operating burden, cost model transparency, published evidence and maturity and community, and Speedata does not score higher on any criterion. They tie on adoption effort and platform and instance portability. The score measures fit for speeding up existing Spark ETL, not raw speed.
Do Databricks Photon and Speedata run on the same platforms?
They share no documented platform on the pages we reviewed. Databricks Photon is also documented for Databricks. Speedata is also documented for self-managed Spark or Kubernetes.
What do Databricks Photon and Speedata cost?
Databricks Photon: Databricks DBU pricing; Photon changes DBU consumption (Commercial (part of Databricks)). Speedata: Not published; book a demo (Commercial). For a like-for-like comparison, work out cost per run on one of your own jobs: see Spark cost per job, explained.