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Apache Spark vs Okteto

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
Okteto logo

Okteto

Developer Tools

Runs production-like development environments on your own Kubernetes cluster

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.; Okteto two of the three plans publish no price at all, with no currency and no billing period stated anywhere, so any budget above five seats requires a sales conversation before you can even compare it with an alternative.
  • They diverge on capability: Apache Spark covers Unified engine, Okteto covers Cluster development environments.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Apache Spark and Okteto differ
AttributeApache SparkOkteto
Pricing modelopen-sourcequote
PlatformsWebLinux, macOS, Windows, CLI, Kubernetes, Web
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 Okteto

  • Cluster development environments
  • File synchronisation
  • Development containers
  • Environment catalogue
  • Preview environments
  • Bring your own cloud

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

Okteto

  • A microservices estate where no developer can run the full dependency graph on a laptopnot Apache Spark
  • Giving each pull request a working environment reviewers can click rather than describenot Apache Spark
  • Letting coding agents build and test against real databases and queues instead of mocksnot Apache Spark
  • Removing a fleet of local Kubernetes installations that drift from production and produce false test resultsnot 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.

Okteto

  • Two of the three plans publish no price at all, with no currency and no billing period stated anywhere, so any budget above five seats requires a sales conversation before you can even compare it with an alternative.
  • The Apache-2.0 CLI is restricted to the development section of the manifest, so build services, user management and observability all require the commercial platform and the open licence provides very little continuity insurance.
  • The last disclosed funding round is a 15 million US dollar Series A from February 2022 with nothing announced since, and the contributor base is dominated by company employees, which is concentrated risk for something embedded in every developer daily loop.
  • You must bring and pay for the Kubernetes cluster, so node cost, capacity planning and version upgrades stay with your platform team on top of the licence fee.
  • The 2026 repositioning towards AI agents means the classic developer inner loop is no longer what the marketing leads with, so a buyer purchasing that use case should confirm roadmap commitment rather than assume it.

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Okteto

Free
  • StarterFree
    • 5 seats included
    • Self-hosted deployment into your own cluster
    • Community support
  • Scale$undefined/year
    • Contact sales, no price, currency or billing period published
    • More seats and environments than Starter
    • Self-hosted deployment
  • Enterprise$undefined/year
    • Contact sales, no price published
    • Bring your own cloud deployment
    • Available through Google Cloud and AWS marketplaces

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 Okteto if

  • You need cluster development environments.
  • You want to start without paying.
  • You work on Linux, macOS, Windows, CLI, Kubernetes, Web.
  • You also want file synchronisation.
Okteto review

Questions people ask

Is Apache Spark or Okteto better?
Neither clearly leads. Apache Spark starts at Free and Okteto at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or Okteto?
Apache Spark starts at Free and Okteto at Free.
Does Apache Spark or Okteto run on more platforms?
Apache Spark runs on Web. Okteto runs on Linux, macOS, Windows, CLI, Kubernetes, Web.
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 Okteto is typically brought in for.
What can Apache Spark do that Okteto cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. Okteto covers Cluster development environments, File synchronisation, Development containers, Environment catalogue.

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.

Okteto: Is Okteto open source?

The CLI is Apache-2.0, but the README states it supports only the development section of the manifest. Everything operationally interesting, including builds, user management and observability, is in the commercial platform.

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.

Okteto: What does it cost?

Only the free Starter tier with five seats is published. Scale and Enterprise are both contact sales, with no price, currency or billing period disclosed on the pricing page.

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.

Okteto: Do I need my own Kubernetes cluster?

Yes. Okteto installs into a cluster you provide and pay for, so capacity, upgrades and node costs remain your responsibility in addition to the licence.

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.

Okteto: How is this different from running Kubernetes locally?

Environments run against the same managed databases, queues and service mesh as production, so a class of failures that only appears in a real cluster is caught before merge rather than after deploy.

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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