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PostHog vs Apache Spark MLlib

PostHog logo

PostHog

Technology

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

From
Free
Rated
-
Full reviewWebsite
Apache Spark MLlib logo

Apache Spark MLlib

Machine Learning

The machine learning library inside Apache Spark, for data that will not fit on one machine

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 the algorithm set has grown slowly and its gradient boosting does not match XGBoost or LightGBM in accuracy or speed, so teams routinely do feature engineering in Spark and then train elsewhere, which undoes the argument for using it at all.
  • They diverge on capability: PostHog covers Product analytics, Apache Spark MLlib covers DataFrame-based pipelines.
  • Prices and features above were last checked on 30 August 2026.

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
CategoryTechnologyMachine Learning
Founded20201999

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

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

Only in Apache Spark MLlib

  • DataFrame-based pipelines
  • Distributed algorithms
  • Alternating least squares
  • Feature transformers
  • Model selection
  • Pipeline persistence
  • Language bindings
  • Runs in existing Spark deployments

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

  • Training on a data set too large to hold on one machine, where sampling down would lose the rare events you care aboutnot PostHog
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot PostHog
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot PostHog
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot 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

  • The algorithm set has grown slowly and its gradient boosting does not match XGBoost or LightGBM in accuracy or speed, so teams routinely do feature engineering in Spark and then train elsewhere, which undoes the argument for using it at all.
  • There is no deep learning in MLlib; neural network work on Spark requires a separate integration, and the DataFrame-centred interface is an awkward fit for it.
  • Fitted models serialise into Spark's own format, so low-latency serving needs either a Spark session in the request path, which is far too slow, or a conversion through ONNX or MLeap, and this is where most Spark ML projects stall.
  • Debugging is JVM cluster debugging: executor out-of-memory, shuffle spill, skewed partitions and serialisation failures, so an engineer without Spark operations experience spends more time tuning the cluster than improving the model.
  • The cluster is the real cost and Spark holds executors for the duration of a job, so a badly partitioned training run pays for idle cores across the whole fleet while one straggler task finishes.

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 dataframe-based pipelines.
  • You want to start without paying.
  • You work on Linux, macOS, Windows.
  • You also want distributed algorithms.
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 DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

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 MLlib: What is the difference between spark.ml and spark.mllib?

spark.ml is the DataFrame-based interface and the one to use. spark.mllib is the older RDD-based package, kept for compatibility, in maintenance and receiving no new features.

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 MLlib: Do I need a cluster?

Spark runs in local mode on one machine, which is useful for development, but if you are running on one machine you would generally be better served by scikit-learn or XGBoost, which are faster and more capable at that scale.

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 MLlib: Can I use scikit-learn on Spark instead?

Yes, and it is often the better answer. You can distribute independent model fits across the cluster, or use pandas user-defined functions to run per-group models, keeping Spark for the data and a mature library for the modelling.

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 MLlib: How do I serve an MLlib model in real time?

Not directly. Either convert the pipeline to a portable format such as ONNX or MLeap, or reimplement the scoring path. Starting a Spark session per request adds seconds of overhead and is not a serving strategy.

Apache Spark MLlib: Is it free?

The library is Apache 2.0 and costs nothing. The cluster it runs on is billed by your cloud provider or by Databricks, and that is the actual expense.

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