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Machine Learning · head to head

LlamaIndex vs Apache Spark MLlib

LlamaIndex logo

LlamaIndex

Machine Learning

Data framework for LLM applications

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: LlamaIndex the free LlamaCloud plan includes 10K credits and has no pay as you go option, so work stops when credits run out; 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: LlamaIndex covers Data connectors, 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 LlamaIndex and Apache Spark MLlib actually diverge.

Attributes where LlamaIndex and Apache Spark MLlib differ
AttributeLlamaIndexApache Spark MLlib
Pricing modelusage-basedopen-source
PlatformsLinux, Mac, WindowsLinux, macOS, Windows
Founded20221999

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

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 LlamaIndex

  • Data connectors
  • Indexing
  • Query engine
  • RAG pipelines
  • Agents
  • OpenAI
  • Anthropic
  • Pinecone

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.

LlamaIndex

  • Parsing PDFs and complex documents into structured text for RAGnot Apache Spark MLlib
  • Building retrieval augmented generation pipelines over private datanot Apache Spark MLlib
  • Indexing and querying enterprise documents from an LLM applicationnot 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 LlamaIndex
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot LlamaIndex
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot LlamaIndex
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot LlamaIndex

Where each one falls short

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

LlamaIndex

  • The free LlamaCloud plan includes 10K credits and has no pay as you go option, so work stops when credits run out
  • Concurrent parse jobs are capped at 5 on Free and Starter, 20 on Pro and 100 on Enterprise
  • Pay as you go spend is capped at $500 per month on Starter and $5,000 per month on Pro
  • Enterprise SSO is Enterprise plan only
  • Volume discounts on credits and 5x higher rate limits are Enterprise only
  • SaaS or hybrid cloud deployment choice and a dedicated account manager are Enterprise only
  • Enterprise pricing is by quote with no published rate

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

LlamaIndex

Free
  • FreeFree
    • 10K monthly credits
    • Basic parsing
    • 5 concurrent jobs
  • Starter$50/month
    • 40K credits + pay-as-you-go
    • Up to 400K credits
    • 5 concurrent jobs
  • Pro$500/month
    • 400K credits + limited-time bonus
    • 20 concurrent jobs
    • Priority Slack support
  • Enterprise$null/custom
    • Custom volume discounts
    • 5x higher rate limits
    • SSO

Apache Spark MLlib

Free

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

Which should you pick?

Choose LlamaIndex if

  • You need data connectors.
  • You want to start without paying.
  • You work on Linux, Mac, Windows.
  • You also want indexing.
LlamaIndex 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 LlamaIndex or Apache Spark MLlib better?
Neither clearly leads. LlamaIndex 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, LlamaIndex or Apache Spark MLlib?
LlamaIndex starts at Free and Apache Spark MLlib at Free.
Does LlamaIndex or Apache Spark MLlib run on more platforms?
LlamaIndex runs on Linux, Mac, Windows. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use LlamaIndex for free?
Both have a free tier, so you can try either at no cost before committing.
What is LlamaIndex best used for?
LlamaIndex is most often used for parsing pdfs and complex documents into structured text for rag, building retrieval augmented generation pipelines over private data, indexing and querying enterprise documents from an llm application. Of those, parsing pdfs and complex documents into structured text for rag and building retrieval augmented generation pipelines over private data are not what Apache Spark MLlib is typically brought in for.
What can LlamaIndex do that Apache Spark MLlib cannot?
LlamaIndex covers Data connectors, Indexing, Query engine, RAG pipelines. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

LlamaIndex: How much does LlamaIndex (LlamaParse) cost?

LlamaIndex offers a Free plan with 10K monthly credits at $0/month. The Starter plan is $50/month for 40K credits plus pay-as-you-go overage up to 400K total. The Pro plan is $500/month for 400K credits. Credits are priced at 1,000 credits for $1.25.

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.

LlamaIndex: Is LlamaIndex free?

Yes, LlamaIndex offers a free plan with 10K monthly credits, basic parsing, 5 concurrent jobs, and support for up to 100 users with no upfront payment required.

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.

LlamaIndex: What are LlamaIndex's concurrent job limits?

The Free and Starter plans allow 5 concurrent jobs. The Pro plan increases this to 20 concurrent jobs. Enterprise plans offer custom configurations with 5x higher rate limits.

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.

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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