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Apache Spark vs PostHog

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
PostHog logo

PostHog

Technology

The single platform to analyze, test, observe, and deploy new features

From
Free
Rated
-
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.; PostHog the free tier covers 1M events, 5K web session recordings and 2.5K mobile recordings per month before usage-based billing starts
  • They diverge on capability: Apache Spark covers Unified engine, PostHog covers Product analytics.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Apache Spark and PostHog differ
AttributeApache SparkPostHog
Pricing modelopen-sourceusage-based
PlatformsWebWeb, Ios, Android, Api
FoundedUnknown2020

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 Apache Spark

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

Only in PostHog

  • Product analytics
  • Session recording
  • Feature flags
  • A/B testing
  • Heatmaps
  • SQL access
  • Data warehouse
  • Apps platform

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

PostHog

  • Product analyticsnot Apache Spark
  • Feature experimentationnot Apache Spark
  • User behavior trackingnot Apache Spark
  • A/B testingnot Apache Spark
  • Debug production issuesnot 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.

PostHog

  • The free tier covers 1M events, 5K web session recordings and 2.5K mobile recordings per month before usage-based billing starts
  • Accounts without a card on file are limited to 1 project; adding one raises it to 6
  • Data retention is 1 year until a card is added, which extends it to 7 years
  • Support is community-only until the account is on a paid plan
  • Error tracking is capped at 100K exceptions and surveys at 1500 responses per month on the free tier

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

PostHog

Free
  • FreeFree
    • 1M events/month
    • 5K sessions/month
    • Unlimited users
  • Paid$undefined/month
    • $0.00031/event
    • $0.005/session
    • Advanced permissions
  • Enterprise$undefined/month
    • SAML SSO
    • Advanced security
    • Dedicated support

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 PostHog if

  • You need product analytics.
  • You want to start without paying.
  • You work on Web, Ios, Android, Api.
  • You also want session recording.
PostHog review

Questions people ask

Is Apache Spark or PostHog better?
Neither clearly leads. Apache Spark starts at Free and PostHog at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or PostHog?
Apache Spark starts at Free and PostHog at Free.
Does Apache Spark or PostHog run on more platforms?
Apache Spark runs on Web. PostHog runs on Web, Ios, Android, Api.
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 PostHog is typically brought in for.
What can Apache Spark do that PostHog cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. PostHog covers Product analytics, Session recording, Feature flags, A/B testing.

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.

PostHog: What does PostHog's free tier include per month?

PostHog free tier includes: 1M analytics events, 5K session replays, 1M feature flag requests, 100K error tracking exceptions, 1,500 survey responses, 1M data warehouse rows, 10K data pipeline events, 100K AI observability events, 500 PostHog AI credits, 10K workflow messages, and 10GB log ingestion. Source: https://posthog.com/pricing

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.

PostHog: How much data retention does PostHog provide on paid plans?

PostHog free tier provides 1-year data retention. Pay-as-you-go plans offer 7-year data retention across all projects, enabling longer historical analysis. Source: https://posthog.com/pricing

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.

PostHog: What percentage of PostHog users stay on the free tier?

PostHog states that 97% of companies use PostHog for free, indicating extensive free tier adoption. However, specific per-unit pricing rates for overages on paid plans are not published. Source: https://posthog.com/pricing

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.

PostHog: When does PostHog provide priority support on paid plans?

PostHog provides email or Slack support for accounts exceeding $2,000/month on pay-as-you-go plans. Specific response times and support SLAs are not detailed on their pricing page. Source: https://posthog.com/pricing

Source
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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