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Apache Spark vs Nixpacks

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
Nixpacks logo

Nixpacks

Developer Tools

Build system that turns a source directory into an OCI image, now in maintenance mode

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.; Nixpacks the project is in maintenance mode with no commits on the default branch since May 2026 and no release since October 2025, so support for new language versions arrives only if someone forks it.
  • They diverge on capability: Apache Spark covers Unified engine, Nixpacks covers Automatic language detection.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Apache Spark and Nixpacks differ
AttributeApache SparkNixpacks
Pricing modelopen-sourceOpen source, no licence fee
PlatformsWebLinux, macOS, CLI, Docker
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 Nixpacks

  • Automatic language detection
  • No Dockerfile required
  • Nix toolchains
  • Configuration escape hatches
  • Docker layer caching

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

Nixpacks

  • Understanding the build behaviour of a platform that adopted Nixpacks before it was frozennot Apache Spark
  • Producing container images from a polyglot monorepo where nobody wants to own several Dockerfilesnot Apache Spark
  • Reproducing an existing Railway build locally while planning a migration to Railpacknot Apache Spark
  • Comparing what a Nix based builder does differently from a buildpack before choosing a successornot 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.

Nixpacks

  • The project is in maintenance mode with no commits on the default branch since May 2026 and no release since October 2025, so support for new language versions arrives only if someone forks it.
  • Build determinism is softer than it appears, because providers pin a nixpkgs archive commit and bumping that archive silently moves compiler and runtime versions underneath a build that looks unchanged.
  • Caching is limited to Docker layers rather than a content addressed graph, so a small change early in the build invalidates everything after it, which is exactly the limitation Railpack was rewritten to fix.
  • Images carry Nix store layers and come out larger than an equivalent tuned Dockerfile or buildpack output, which costs registry storage and slows cold pulls on scale-out.
  • There is no specification, no vendor neutral governance and no third party provider ecosystem, so a single company was able to redirect strategy and leave every downstream user with a frozen build tool.

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Nixpacks

Free
  • NixpacksFree
    • MIT licensed, no usage limits
    • No commercial edition and no support contract
    • In maintenance mode, superseded by Railpack

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 Nixpacks if

  • You need automatic language detection.
  • You want to start without paying.
  • You work on Linux, macOS, CLI, Docker.
  • You also want no dockerfile required.
Nixpacks review

Questions people ask

Is Apache Spark or Nixpacks better?
Neither clearly leads. Apache Spark starts at Free and Nixpacks at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or Nixpacks?
Apache Spark starts at Free and Nixpacks at Free.
Does Apache Spark or Nixpacks run on more platforms?
Apache Spark runs on Web. Nixpacks runs on Linux, macOS, CLI, Docker.
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 Nixpacks is typically brought in for.
What can Apache Spark do that Nixpacks cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. Nixpacks covers Automatic language detection, No Dockerfile required, Nix toolchains, Configuration escape hatches.

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.

Nixpacks: Should I start a new project on Nixpacks?

No. The documentation and README both say it is in maintenance mode and recommend Railpack. Choose Railpack, Cloud Native Buildpacks or a Dockerfile instead.

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.

Nixpacks: Will my existing Nixpacks builds stop working?

Not immediately. Pinned nixpkgs archives keep resolving, so builds continue. What you lose is support for new runtime major versions and any fix for a provider bug.

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.

Nixpacks: What replaces it?

Railpack, also from Railway and also MIT licensed, rebuilt as a BuildKit custom frontend for finer grained caching and parallel steps.

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.

Nixpacks: Is it still open source?

Yes, MIT, and the repository is not archived. Being open source does not help much when nobody is merging provider updates.

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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