Head to head · Figures checked September 2026
Flarion vs Speedata
ETL Compare staff · Figures checked September 2026 · Sourced from vendor docs, project pages and public benchmarks · Published 29 September 2026
In brief
Flarion has the higher ETL fit score on our lead weights (3.15 against 2.61 out of 5). Flarion scores higher on adoption effort, stage coverage, tuning and operating burden and platform and instance portability; Speedata scores higher on published evidence and maturity and community. They tie on cost model transparency. 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 Flarion ranks 7th of 9 and Speedata ranks 9th of 9. 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. 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.
3.15 / 5
ETL fit score (editorial assessment, 0-5)ETL fit score (editorial assessment, 0-5)
Rank 7 of 9
Commercial plugin with a broad managed-platform list
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 Flarion and Speedata score on each criterion?
| Criterion | Flarion | Speedata | Higher score |
|---|---|---|---|
| Adoption effort 20% | 4.2 | 2.6 | Flarion |
| Stage coverage 20% | 2.8 | 2.6 | Flarion |
| Tuning and operating burden 20% | 3.8 | 3.0 | Flarion |
| Platform and instance portability 10% | 4.2 | 1.6 | Flarion |
| Cost model transparency 5% | 2.0 | 2.0 | Tie |
| Published evidence 15% | 1.8 | 3.0 | Speedata |
| Maturity and community 10% | 2.0 | 2.6 | Speedata |
| ETL fit score | 3.15 | 2.61 | Flarion |
Why each score
- Adoption effort
- Flarion (4.2): Flarion states "zero code and infrastructure changes" and a 6-minute setup by adding configuration parameters.
- 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
- 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.
- 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
- Flarion (3.8): A commercial product with performance monitoring and anomaly detection described; support terms are not published.
- 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
- Flarion (4.2): Lists Databricks, AWS EMR, GCP Dataproc, Azure HDInsight, Spark on Kubernetes and on-premises, on standard 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
- Flarion (2.0): Listed on AWS Marketplace; no price on the homepage.
- 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
- Flarion (1.8): Headline figures (3x performance, 60% cost reduction) without a published methodology on the pages we reviewed.
- 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
- Flarion (2.0): Company details such as founding date and funding are not published on the pages we reviewed.
- 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 Flarion and Speedata cost, as published?
| Flarion | Speedata | |
|---|---|---|
| License | Commercial | Commercial |
| Published price | Usage-based, per CPU-hour: $0.004 self-hosted cloud, $0.007 on managed Spark (EMR, GCP Managed Spark, Azure HDInsight), $0.008 on Databricks; on-premise $0.50 per CPU-month. | Not published; book a demo |
| Instances | Standard instances | Servers fitted with Speedata C200 APU cards |
| Runs on | Databricks, AWS EMR, GCP Dataproc, Azure HDInsight, Spark on Kubernetes, on-premises | 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 stated; configuration parameters | 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: flarion.io, Flarion pricing, 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 |
|---|---|---|---|---|---|
| Flarion | Partial | Documented | Not stated | Not stated | Not stated |
| 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 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 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, Flarion or Speedata?
Flarion, with 3.15 against 2.61 out of 5 on our lead weights. Flarion scores higher on adoption effort, stage coverage, tuning and operating burden and platform and instance portability and Speedata on published evidence and maturity and community. They tie on cost model transparency. The score measures fit for speeding up existing Spark ETL, not raw speed.
Do Flarion and Speedata 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).
What do Flarion and Speedata cost?
Flarion: Usage-based, per CPU-hour: $0.004 self-hosted cloud, $0.007 on managed Spark (EMR, GCP Managed Spark, Azure HDInsight), $0.008 on Databricks; on-premise $0.50 per CPU-month. (Commercial). 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.