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Apache Spark vs OpsLevel

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
OpsLevel logo

OpsLevel

Developer Tools

Internal developer portal that scores service ownership and production readiness

From
On request
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.; OpsLevel opsLevel does not publish prices at all, not even a starting figure, so you cannot size a budget or compare against Backstage's zero licence cost without entering a sales cycle.
  • They diverge on capability: Apache Spark covers Unified engine, OpsLevel covers Service catalogue.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Apache Spark and OpsLevel differ
AttributeApache SparkOpsLevel
Starting priceFreeOn request
Pricing modelopen-sourcequote
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 OpsLevel

  • Service catalogue
  • Scorecards and rubric
  • Checks
  • Campaigns
  • Self-service actions
  • Slack and Teams integration
  • Catalogue auto-enrichment
  • Deployment on request

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

OpsLevel

  • A platform team that needs to prove every production service has a named owner and an on-call rota before an auditnot Apache Spark
  • Driving a fleet-wide migration such as a runtime upgrade across 400 services with a deadline and visible progressnot Apache Spark
  • Giving developers a paved road to create a new service from a template without waiting on the platform teamnot Apache Spark
  • An organisation where an incident took an hour to route because nobody could identify the owning teamnot 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.

OpsLevel

  • OpsLevel does not publish prices at all, not even a starting figure, so you cannot size a budget or compare against Backstage's zero licence cost without entering a sales cycle.
  • The Standard plan caps at 50 users, which is below the size at which service ownership becomes a real problem, so most genuine buyers land on Enterprise and its unpublished pricing.
  • Scores are only as honest as the metadata teams maintain; where ownership records go stale the rubric produces confident numbers about a catalogue that no longer reflects reality, and leadership acts on them.
  • It reports on quality rather than producing it, so it can generate friction between platform teams who set the rubric and product teams who see it as a scoreboard imposed on them without extra headcount to fix the findings.
  • As a hosted product it needs read access to source control, cloud accounts and observability tooling, which is a meaningful vendor review in regulated environments; on-premises exists but only on the Enterprise tier.

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

OpsLevel

On request
  • Standard$undefined/year
    • Up to 50 users
    • Unlimited catalogued components
    • Scorecards, campaigns and the global rubric
  • Enterprise$undefined/year
    • Unlimited users
    • Custom integrations
    • Customisable dashboards

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 OpsLevel if

  • You need service catalogue.
  • You also want scorecards and rubric.
OpsLevel review

Questions people ask

Is Apache Spark or OpsLevel better?
Neither clearly leads. Apache Spark starts at Free and OpsLevel at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or OpsLevel?
Apache Spark has a free tier; the other does not. Paid plans start at Free for Apache Spark and On request for OpsLevel.
Does Apache Spark or OpsLevel 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. OpsLevel starts at On request.
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 OpsLevel is typically brought in for.
What can Apache Spark do that OpsLevel cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. OpsLevel covers Service catalogue, Scorecards and rubric, Checks, Campaigns.

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.

OpsLevel: How much does OpsLevel cost?

It does not publish pricing. Billing is per developer using the portal, with volume discounts, and a quote requires a sales conversation.

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.

OpsLevel: How does it compare with Backstage?

Backstage has no licence fee but you staff a team to build and run it. OpsLevel is hosted with checks, campaigns and scorecards out of the box, and you pay per developer instead.

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.

OpsLevel: Does it require writing catalogue YAML by hand?

No. It auto-discovers and enriches entries from connected systems, though teams still curate ownership and metadata.

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.

OpsLevel: Can it run on premises?

Yes, but only on the Enterprise plan.

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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