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Apache Spark vs ConfigCat

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
ConfigCat logo

ConfigCat

Developer Tools

Feature flag service priced per flag and environment with unlimited seats and unlimited monthly active users

From
Free
Free plan
Yes
Full reviewWebsite

The short version

  • 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.; ConfigCat there is no experimentation or statistical analysis engine, so teams wanting real A/B tests with significance testing must buy and integrate a separate analytics or experimentation product.
  • They diverge on capability: Apache Spark covers Unified engine, ConfigCat covers Feature flags and configuration.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which Apache Spark and ConfigCat actually diverge.

Attributes where Apache Spark and ConfigCat differ
AttributeApache SparkConfigCat
Pricing modelopen-sourcePer month by feature flag and environment count
PlatformsWebWeb, API, iOS, Android, Node.js, .NET, Java, Python, Go
CategoryTechnologyDeveloper Tools

Identical on both: starting price (Free), free tier (Yes), 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 ConfigCat

  • Feature flags and configuration
  • Percentage rollouts
  • Targeting rules and segments
  • Unlimited seats
  • Global CDN delivery
  • Audit log
  • Public API and webhooks
  • SDKs across languages

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 ConfigCat
  • Building and maintaining a lakehouse on Iceberg or Delta Lake, where Spark handles both the writes and the compactionnot ConfigCat
  • Feature engineering and model training across datasets too large to fit in pandas on one nodenot ConfigCat
  • Migrating legacy MapReduce or Hive workloads onto an engine that is still actively developed and widely supported by cloud vendorsnot ConfigCat

ConfigCat

  • A consumer or mobile app with millions of users where per-MAU flag pricing would cost more than the flags are worthnot Apache Spark
  • A team that wants every engineer, product manager and support agent to have a login without paying per seatnot Apache Spark
  • Decoupling deployment from release with percentage rollouts and a kill switch, without buying an experimentation platformnot Apache Spark
  • A small team that needs ten flags permanently and can run indefinitely on the free tiernot 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.

ConfigCat

  • There is no experimentation or statistical analysis engine, so teams wanting real A/B tests with significance testing must buy and integrate a separate analytics or experimentation product.
  • Pricing by flag count punishes exactly the hygiene you want: the Pro tier caps at 100 flags, so teams that do not aggressively retire stale flags are pushed to the 325 dollar Smart tier for housekeeping reasons rather than growth.
  • The free tier allows only 2 environments, which does not cover the common development, staging and production split, so almost any real deployment starts on a paid plan.
  • It is a smaller vendor than the category incumbents, so enterprise procurement teams will find less depth in compliance attestations, contractual terms and reference architecture than LaunchDarkly offers.
  • Flag evaluation relies on SDK-side caching of a CDN-delivered config, which is fast but means propagation of a change is eventually consistent rather than instant, something to understand before relying on it as an emergency kill switch.

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

ConfigCat

Free
  • Forever FreeFree
    • 10 feature flags
    • 2 environments and 2 products
    • 2 segments
  • Pro$110/month
    • 100 feature flags
    • 3 environments and 3 products
    • Unlimited seats and MAUs
  • Smart$325/month
    • Unlimited feature flags
    • Unlimited environments, products and segments
    • Unlimited seats and MAUs
  • Enterprise$900/month
    • Everything in Smart
    • Enterprise support and controls
    • Unlimited everything on flag counts

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 ConfigCat if

  • You need feature flags and configuration.
  • You want to start without paying.
  • You work on Web, API, iOS, Android, Node.js, .NET, Java, Python, Go.
  • You also want percentage rollouts.
ConfigCat review

Questions people ask

Is Apache Spark or ConfigCat better?
Neither clearly leads. Apache Spark starts at Free and ConfigCat at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or ConfigCat?
Apache Spark starts at Free and ConfigCat at Free.
Does Apache Spark or ConfigCat run on more platforms?
Apache Spark runs on Web. ConfigCat runs on Web, API, iOS, Android, Node.js, .NET, Java, Python, Go.
Can I use Apache Spark for free?
Both have a free tier, so you can try either at no cost before committing.
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 ConfigCat is typically brought in for.
What can Apache Spark do that ConfigCat cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. ConfigCat covers Feature flags and configuration, Percentage rollouts, Targeting rules and segments, Unlimited seats.

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.

ConfigCat: How is ConfigCat priced?

By feature flag, environment and product count. Seats, monthly active users and flag reads are unlimited on every plan.

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.

ConfigCat: Is the free tier usable?

For small projects yes, but at 10 flags and 2 environments it will not cover a dev, staging and production setup.

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.

ConfigCat: Can it run A/B tests?

It can split traffic, but there is no statistics engine. You need a separate analytics tool to decide a winner.

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.

ConfigCat: How fast does a flag change take effect?

SDKs poll and cache, so changes propagate on the polling interval rather than instantly.

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.

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