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Amplitude vs Apache Spark

Amplitude logo

Amplitude

Technology

The digital analytics platform to understand your users

From
Free
Rated
-
Full reviewWebsite
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

The short version

  • Each has a real cost: Amplitude metered on event volume, so instrumenting more of a product raises the bill even if the audience does not grow; 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.
  • They diverge on capability: Amplitude covers Event tracking, Apache Spark covers Unified engine.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Amplitude and Apache Spark differ
AttributeAmplitudeApache Spark
Pricing modelUnknownopen-source
PlatformsWeb, Ios, Android, ApiWeb
Founded2012Unknown

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

  • Event tracking
  • User segmentation
  • Funnel analysis
  • Retention analysis
  • Cohort analysis
  • A/B testing
  • Revenue analytics
  • Predictive analytics

Only in Apache Spark

  • Unified engine
  • Catalyst optimiser
  • DataFrame and SQL APIs
  • Structured Streaming
  • Spark Connect
  • Kubernetes and YARN support
  • Table format integration
  • MLlib

What people use each for

The jobs each tool is most often brought in to do.

Amplitude

  • User behavior analysisnot Apache Spark
  • Feature adoption trackingnot Apache Spark
  • Conversion rate optimizationnot Apache Spark
  • Customer journey mappingnot Apache Spark
  • Retention improvementnot Apache Spark

Apache Spark

  • Nightly ETL over terabytes in object storage, where a single machine would take longer than the batch window allowsnot Amplitude
  • Building and maintaining a lakehouse on Iceberg or Delta Lake, where Spark handles both the writes and the compactionnot Amplitude
  • Feature engineering and model training across datasets too large to fit in pandas on one nodenot Amplitude
  • Migrating legacy MapReduce or Hive workloads onto an engine that is still actively developed and widely supported by cloud vendorsnot Amplitude

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Amplitude

  • Metered on event volume, so instrumenting more of a product raises the bill even if the audience does not grow
  • The free plan covers 2M events a month
  • The Plus plan scales to 70M events, above which pricing is custom
  • Growth and Enterprise pricing is not published

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.

Pricing, plan by plan

Amplitude

Free
  • StarterFree
    • 2 million events per month
  • Plus$49/month
    • $0.049 per MTU
    • Up to 300k MTUs
    • Advanced analytics
  • GrowthFree
    • Causal insights
    • Feature experimentation
    • Real-time streaming
  • EnterpriseFree
    • Cross-product analysis
    • Advanced permissions
    • Dedicated account manager

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Which should you pick?

Choose Amplitude if

  • You need event tracking.
  • You want to start without paying.
  • You work on Web, Ios, Android, Api.
  • You also want user segmentation.
Amplitude review

Choose Apache Spark if

  • You need unified engine.
  • You want to start without paying.
  • You also want catalyst optimiser.
Apache Spark review

Questions people ask

Is Amplitude or Apache Spark better?
Neither clearly leads. Amplitude starts at Free and Apache Spark at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Amplitude or Apache Spark?
Amplitude starts at Free and Apache Spark at Free.
Does Amplitude or Apache Spark run on more platforms?
Amplitude runs on Web, Ios, Android, Api. Apache Spark runs on Web.
Can I use Amplitude for free?
Both have a free tier, so you can try either at no cost before committing.
What is Amplitude best used for?
Amplitude is most often used for user behavior analysis, feature adoption tracking, conversion rate optimization, customer journey mapping. Of those, user behavior analysis and feature adoption tracking are not what Apache Spark is typically brought in for.
What can Amplitude do that Apache Spark cannot?
Amplitude covers Event tracking, User segmentation, Funnel analysis, Retention analysis. Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming.

Answered from the vendors’ own pages

Amplitude: Does Amplitude have a free plan?

Yes, Amplitude offers a free Starter plan with 2 million events per month and access to the entire platform including analytics, session replay, and experimentation features.

Source
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.

Amplitude: What is Amplitude's pricing based on?

Amplitude's pricing is based on the number of monthly tracked users (MTUs), data volume, and advanced features selected. The Plus plan starts at $49 per month with a rate of $0.049 per MTU.

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.

Amplitude: What analytics features does every Amplitude plan include?

Every plan includes access to the full platform: analytics, session replay, feature experimentation, web experimentation, guides and surveys, activation, and AI tools like AI Feedback and AI Assistant.

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.

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.

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