Head to head · Figures checked September 2026
RAPIDS Accelerator vs Speedata
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 lead weights (3.65 against 2.61 out of 5). RAPIDS Accelerator scores higher on adoption effort, stage coverage, platform and instance portability, cost model transparency, published evidence and maturity and community, and Speedata does not score higher on any criterion. They tie on tuning and operating burden. 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 lead weights RAPIDS Accelerator ranks 4th of 9 and Speedata ranks 9th of 9. Which one fits depends on the criteria below and on the platform you run.
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. 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.
3.65 / 5
ETL fit score (editorial assessment, 0-5)ETL fit score (editorial assessment, 0-5)
Rank 4 of 9
Best for teams that already run GPU capacity
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 RAPIDS Accelerator and Speedata score on each criterion?
| Criterion | RAPIDS Accelerator | Speedata | Higher score |
|---|---|---|---|
| Adoption effort 20% | 3.4 | 2.6 | RAPIDS Accelerator |
| Stage coverage 20% | 4.2 | 2.6 | RAPIDS Accelerator |
| Tuning and operating burden 20% | 3.0 | 3.0 | Tie |
| Platform and instance portability 10% | 3.2 | 1.6 | RAPIDS Accelerator |
| Cost model transparency 5% | 4.2 | 2.0 | RAPIDS Accelerator |
| Published evidence 15% | 3.6 | 3.0 | RAPIDS Accelerator |
| Maturity and community 10% | 4.6 | 2.6 | RAPIDS Accelerator |
| ETL fit score | 3.65 | 2.61 | RAPIDS Accelerator |
Why each score
- Adoption effort
- RAPIDS Accelerator (3.4): No code changes, but the job has to move to NVIDIA GPU instances and the cluster needs GPU-specific configuration.
- 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
- 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.
- 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
- RAPIDS Accelerator (3.0): Qualification and Profiling tools help, but GPU sizing and plugin configuration are your team's job.
- 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
- 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.
- 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
- 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.
- 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
- RAPIDS Accelerator (3.6): The Qualification Tool estimates fit from your own event logs; headline benchmark figures were not reviewed for this edition.
- 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
- 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.
- 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 RAPIDS Accelerator and Speedata cost, as published?
| RAPIDS Accelerator | Speedata | |
|---|---|---|
| License | Apache License 2.0 | Commercial |
| Published price | No plugin fee; GPU instance pricing applies | Not published; book a demo |
| Instances | NVIDIA GPU instances (Volta or later) | Servers fitted with Speedata C200 APU cards |
| Runs on | Amazon EMR, Databricks, Dataproc, GKE, Azure Synapse, Kubernetes, on-premises, OCI | 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; plugin replaces internal physical plan parts | 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: spark-rapids overview, RAPIDS Accelerator user guide, RAPIDS Accelerator FAQ, NVIDIA/spark-rapids on GitHub, speedata.io, Speedata product page, Speedata news, Speedata company page · Fetched 27 Sep 2026
Where are they documented to run?
| EMR | Databricks | Google Cloud (Dataproc) | AWS Glue | Self-managed Spark / Kubernetes | |
|---|---|---|---|---|---|
| RAPIDS Accelerator | Documented | Documented | Documented | Not documented | Documented |
| Speedata | Not documented | Not documented | Not documented | Not documented | Documentedon Speedata APU servers, on premises |
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 |
| 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 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
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, RAPIDS Accelerator or Speedata?
RAPIDS Accelerator, with 3.65 against 2.61 out of 5 on our lead weights. RAPIDS Accelerator scores higher on adoption effort, stage coverage, platform and instance portability, cost model transparency, published evidence and maturity and community, and Speedata does not score higher on any criterion. They tie on tuning and operating burden. The score measures fit for speeding up existing Spark ETL, not raw speed.
Do RAPIDS Accelerator and Speedata 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).
What do RAPIDS Accelerator and Speedata cost?
RAPIDS Accelerator: No plugin fee; GPU instance pricing applies (Apache License 2.0). 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.