Softwr

Technology · head to head

Apache Spark vs Zabbix Cloud

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
Rated
-
Full reviewWebsite
Zabbix Cloud logo

Zabbix Cloud

Technology

Zabbix monitoring, fully managed

From
$50/month
Rated
-
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.; Zabbix Cloud billed by NVPS (new values per second) rather than by number of monitored hosts, so cost scales with metric volume: Nano starts at $50/month for 50 NVPS and 2xLarge reaches $5,000/month for 10,000 NVPS
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Apache Spark and Zabbix Cloud actually diverge.

Attributes where Apache Spark and Zabbix Cloud differ
AttributeApache SparkZabbix Cloud
Starting priceFree$50/month
Pricing modelopen-sourcesubscription
Free tierYesNo

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

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 Zabbix Cloud

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

Zabbix Cloud

  • Network performance monitoring and detectionnot Apache Spark
  • Server health and performance trackingnot Apache Spark
  • Container monitoring in Kubernetes environmentsnot Apache Spark
  • Database performance across vendor platformsnot Apache Spark
  • VMware and Windows infrastructure observationnot 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.

Zabbix Cloud

  • Billed by NVPS (new values per second) rather than by number of monitored hosts, so cost scales with metric volume: Nano starts at $50/month for 50 NVPS and 2xLarge reaches $5,000/month for 10,000 NVPS
  • The free trial lasts only 5 days before a paid subscription is required
  • Annual subscription is needed to get the 10% discount; the base prices listed are monthly rates

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Zabbix Cloud

$50/month
  • Nano$50/month
    • 50 NVPS
    • 10GB storage free
  • Micro$100/month
    • 100 NVPS
  • Small$250/month
    • 250 NVPS
  • Medium$750/month
    • 1000 NVPS

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 Zabbix Cloud if

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

Zabbix Cloud review

Questions people ask

Is Apache Spark or Zabbix Cloud better?
Neither clearly leads. Apache Spark starts at Free and Zabbix Cloud at $50/month, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or Zabbix Cloud?
Apache Spark has a free tier; the other does not. Paid plans start at Free for Apache Spark and $50/month for Zabbix Cloud.
Does Apache Spark or Zabbix Cloud 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. Zabbix Cloud starts at $50/month.
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 Zabbix Cloud is typically brought in for.
What can Apache Spark do that Zabbix Cloud 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.

Zabbix Cloud: What is the starting price for Zabbix Cloud?

Zabbix Cloud starts at $50 per month for the Nano tier, which includes 50 NVPS and 10GB of free storage. Pricing scales up to $5000 monthly for the 2xLarge tier with 10000 NVPS.

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.

Zabbix Cloud: Is there a free trial for Zabbix Cloud?

Yes, Zabbix Cloud offers a 5-day free trial with no credit card required. After the trial, you save 10% by converting to an annual subscription.

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.

Zabbix Cloud: How does annual billing work with Zabbix Cloud?

Annual subscribers receive a 10% discount on monthly pricing. A pricing calculator is available to estimate costs based on devices, metrics, update intervals, and storage requirements.

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 Zabbix Cloud

Keep looking

Other head to heads