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PostHog vs Apache Spark MLlib

PostHog logo

PostHog

Software

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

From
Free
Rated
-
Full reviewWebsite
Apache Spark MLlib logo

Apache Spark MLlib

Software

Scalable machine learning on Apache Spark

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: PostHog the free tier covers 1M events, 5K web session recordings and 2.5K mobile recordings per month before usage-based billing starts; Apache Spark MLlib apache Spark MLlib is Apache 2.0 licensed and free with no paid tier from the Apache project itself; SLA-backed support has to be sourced from a third party such as a managed Spark vendor rather than from Apache.
  • They diverge on capability: PostHog covers Product analytics, Apache Spark MLlib covers Classification.

Where they differ

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

Attributes where PostHog and Apache Spark MLlib differ
AttributePostHogApache Spark MLlib
Pricing modelusage-basedopen-source
PlatformsWeb, Ios, Android, ApiLinux, macOS, Windows
Founded20201999

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Unknown).

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 PostHog

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

Only in Apache Spark MLlib

  • Classification
  • Regression
  • Clustering
  • Collaborative filtering
  • Feature engineering
  • Apache Spark
  • Hadoop
  • Kafka

What people use each for

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

PostHog

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

Apache Spark MLlib

  • Large-scale distributed machine learning on Spark clustersnot PostHog
  • Classification and regression with decision trees, random forests, gradient-boosted treesnot PostHog
  • Clustering with K-means and Gaussian Mixture Modelsnot PostHog

Where each one falls short

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

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

Apache Spark MLlib

  • Apache Spark MLlib is Apache 2.0 licensed and free with no paid tier from the Apache project itself; SLA-backed support has to be sourced from a third party such as a managed Spark vendor rather than from Apache.

Pricing, plan by plan

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

Apache Spark MLlib

Free

No published plan breakdown. See the Apache Spark MLlib review.

Which should you pick?

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

Choose Apache Spark MLlib if

  • You need classification.
  • You want to start without paying.
  • You work on Linux, macOS, Windows.
  • You also want regression.
Apache Spark MLlib review

Questions people ask

Is PostHog or Apache Spark MLlib better?
Neither clearly leads. PostHog starts at Free and Apache Spark MLlib at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, PostHog or Apache Spark MLlib?
PostHog starts at Free and Apache Spark MLlib at Free.
Does PostHog or Apache Spark MLlib run on more platforms?
PostHog runs on Web, Ios, Android, Api. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use PostHog for free?
Both have a free tier, so you can try either at no cost before committing.
What is PostHog best used for?
PostHog is most often used for product analytics, feature experimentation, user behavior tracking, a/b testing. Of those, product analytics and feature experimentation are not what Apache Spark MLlib is typically brought in for.
What can PostHog do that Apache Spark MLlib cannot?
PostHog covers Product analytics, Session recording, Feature flags, A/B testing. Apache Spark MLlib covers Classification, Regression, Clustering, Collaborative filtering.
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