Softwr

Technology · head to head

Apache Spark vs RAD Studio

Apache Spark logo

Apache Spark

Technology

A distributed engine for batch, SQL, streaming and machine learning workloads over data that does not fit on one machine.

From
Free
Free plan
Yes
Full reviewWebsite
RAD Studio logo

RAD Studio

Developer Tools

Cross-platform native app development with Delphi and C++Builder

From
$2000/perpetual
Free plan
No
Full reviewWebsite

The short version

  • Only Apache Spark has a free tier, so it costs nothing to try first.
  • Each has a real cost: Apache Spark running it well is JVM operations work: executor sizing, shuffle partition counts, off-heap memory and serialisation all have to be tuned, and the failures you actually get are out-of-memory errors and skewed shuffles rather than wrong answers, so you need somebody who can read the Spark UI or you will scale the cluster instead of fixing the query.; RAD Studio professional edition costs $2,000 for the perpetual license plus $599 for year one maintenance, totaling $2,599 before tax
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Apache Spark and RAD Studio actually diverge.

Attributes where Apache Spark and RAD Studio differ
AttributeApache SparkRAD Studio
Starting priceFree$2000/perpetual
Pricing modelopen-sourceone-time
Free tierYesNo
CategoryTechnologyDeveloper Tools

Identical on both: platforms (Web), user rating (Not yet rated).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Apache Spark

  • Unified engine
  • Catalyst optimiser
  • DataFrame and SQL APIs
  • Structured Streaming
  • Spark Connect
  • Kubernetes and YARN support
  • Table format integration
  • MLlib

Only in RAD Studio

Nothing recorded that Apache Spark does not also cover.

What people use each for

The jobs each tool is most often brought in to do.

Apache Spark

  • Nightly ETL over terabytes in object storage, where a single machine would take longer than the batch window allowsnot RAD Studio
  • Building and maintaining a lakehouse on Iceberg or Delta Lake, where Spark handles both the writes and the compactionnot RAD Studio
  • Feature engineering and model training across datasets too large to fit in pandas on one nodenot RAD Studio
  • Migrating legacy MapReduce or Hive workloads onto an engine that is still actively developed and widely supported by cloud vendorsnot RAD Studio

RAD Studio

  • Building Windows desktop applications with VCL componentsnot Apache Spark
  • Creating iOS and Android apps from shared Delphi codebasenot Apache Spark
  • Connecting to Oracle databases through FireDAC connectivitynot Apache Spark
  • Building REST web services that run on Linux serversnot Apache Spark

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Apache Spark

  • Running it well is JVM operations work: executor sizing, shuffle partition counts, off-heap memory and serialisation all have to be tuned, and the failures you actually get are out-of-memory errors and skewed shuffles rather than wrong answers, so you need somebody who can read the Spark UI or you will scale the cluster instead of fixing the query.
  • The fastest Spark is not open source. Databricks' Photon engine and comparable vendor accelerations are proprietary, so benchmark numbers quoted for Spark frequently describe a fork you can only rent, and moving off that vendor loses the performance you sized your pipelines around.
  • It is a distributed system with distributed overheads, and modern single-node tools such as DuckDB and Polars finish faster on datasets up to hundreds of gigabytes with no cluster to start, so a Spark job below that threshold is paying coordination cost for nothing.
  • Structured Streaming is micro-batch, which puts an end-to-end latency floor in the range of hundreds of milliseconds to seconds; workloads that need genuine per-event latency go to Flink instead, and discovering this after building on Spark means a rewrite.
  • Major upgrades deliberately break jobs: Spark 4.0 turns ANSI SQL mode on by default, so silent overflow and invalid casts that previously produced nulls now raise runtime errors, and a pipeline that worked for years can start failing purely on upgrade.
  • PySpark hides a process boundary, and Python UDFs serialise every row between the JVM and a Python worker; a direct translation of pandas code into PySpark UDFs can run an order of magnitude slower than the equivalent built-in expressions.

RAD Studio

  • Professional edition costs $2,000 for the perpetual license plus $599 for year one maintenance, totaling $2,599 before tax
  • Enterprise edition steps up to $3,400 license plus $1,199 maintenance, and Architect edition to $4,416 license plus $2,183 maintenance
  • The perpetual license only entitles the buyer to updates for one year; continued access to new updates and upgrades requires an annually renewable Update Subscription

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

RAD Studio

$2000/perpetual
  • Professional Edition$2000/perpetual
    • Perpetual License
    • First year maintenance $599
    • Total $2,599 + Tax
  • Enterprise Edition$3400/perpetual
    • Perpetual License
    • First year maintenance $1,199
    • Total $4,599 + Tax
  • Architect Edition$4416/perpetual
    • Perpetual License
    • First year maintenance $2,183
    • Total $6,599 + Tax

Which should you pick?

Choose Apache Spark if

  • You need unified engine.
  • You want to start without paying.
  • You also want catalyst optimiser.
Apache Spark review

Choose RAD Studio if

Nothing in the data separates RAD Studio from Apache Spark on the points above - pick on price and on how each one feels to use.

RAD Studio review

Questions people ask

Is Apache Spark or RAD Studio better?
Neither clearly leads. Apache Spark starts at Free and RAD Studio at $2000/perpetual, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or RAD Studio?
Apache Spark has a free tier; the other does not. Paid plans start at Free for Apache Spark and $2000/perpetual for RAD Studio.
Does Apache Spark or RAD Studio run on more platforms?
Both run on Web, so platform support will not decide this one for you.
Can I use Apache Spark for free?
Yes. Apache Spark has a free tier, so you can try it without paying. RAD Studio starts at $2000/perpetual.
What is Apache Spark best used for?
Apache Spark is most often used for nightly etl over terabytes in object storage, where a single machine would take longer than the batch window allows, building and maintaining a lakehouse on iceberg or delta lake, where spark handles both the writes and the compaction, feature engineering and model training across datasets too large to fit in pandas on one node, migrating legacy mapreduce or hive workloads onto an engine that is still actively developed and widely supported by cloud vendors. Of those, nightly etl over terabytes in object storage, where a single machine would take longer than the batch window allows and building and maintaining a lakehouse on iceberg or delta lake, where spark handles both the writes and the compaction are not what RAD Studio is typically brought in for.
What can Apache Spark do that RAD Studio cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming.

Answered from the vendors’ own pages

Apache Spark: When is Spark the wrong choice?

When your data fits comfortably on one machine. DuckDB or Polars will process hundreds of gigabytes on a single large node faster than a Spark cluster, without a scheduler, a driver or a shuffle. Spark earns its overhead when the data genuinely does not fit.

RAD Studio: How much does RAD Studio cost?

RAD Studio uses perpetual licensing: Professional Edition $2,000 perpetual license + $599 first year maintenance ($2,599 total + tax). Enterprise Edition $3,400 license + $1,199 maintenance ($4,599 total + tax). Architect Edition $4,416 license + $2,183 maintenance ($6,599 total + tax).

Source
Apache Spark: Is Spark the same on Databricks as the open source version?

No. Databricks runs its own runtime including the proprietary Photon engine and its own optimisations, so performance figures and some behaviours do not carry over to open source Spark on EMR, Dataproc or your own Kubernetes cluster.

RAD Studio: What is the difference between RAD Studio editions?

Professional Edition ($2,599 total + tax) targets individual developers. Enterprise Edition ($4,599 total + tax) adds multi-tier app capabilities. Architect Edition ($6,599 total + tax) includes advanced database and cloud features. All three include perpetual licenses.

Source
Apache Spark: Can I use Spark for real-time processing?

For near-real-time, yes, with Structured Streaming's micro-batch model, which lands in the sub-second to seconds range. For true per-event latency in the low milliseconds, Flink is the usual choice.

RAD Studio: Does RAD Studio offer Kai AI integration and discounts?

Yes, adding Kai AI to any edition provides discounts: 25% off Professional ($2,136 + tax), 30% off Enterprise ($3,394 + tax), and 40% off Architect ($4,109 + tax). This promotion is active through August 31, 2026.

Source
Apache Spark: Does upgrading between major versions break things?

Yes, by design in some cases. Spark 4.0 makes ANSI SQL mode the default, which converts previously silent overflow and cast failures into runtime errors. Upgrades need a testing pass over production pipelines rather than a version bump.

Apache Spark: Do I need to know Scala?

No. Python covers the vast majority of work and PySpark is the most common interface. Scala still helps when reading the source, writing custom data sources or diagnosing errors that surface as JVM stack traces.

Share

Related pages

More on Apache Spark

More on RAD Studio

Keep looking

Other head to heads