Softwr

AI · head to head

Amazon Q Developer vs Apache Spark MLlib

Amazon Q Developer logo

Amazon Q Developer

AI

Generative AI assistant for coding, testing, and AWS development

From
Free
Free plan
Yes
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
Free plan
Yes
Full reviewWebsite

The short version

  • Each has a real cost: Amazon Q Developer deepest value and AWS-specific expertise is tied to the AWS ecosystem, less compelling for non-AWS shops; 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: Amazon Q Developer covers Code generation, 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 Amazon Q Developer and Apache Spark MLlib actually diverge.

Attributes where Amazon Q Developer and Apache Spark MLlib differ
AttributeAmazon Q DeveloperApache Spark MLlib
Pricing modelfreemiumopen-source
Platformsweb, windows, mac, linux, apiLinux, macOS, Windows
CategoryAIMachine Learning
FoundedUnknown1999

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 Amazon Q Developer

  • Code generation
  • Agentic task automation
  • Security scanning
  • Application transformation
  • AWS expertise
  • Private code customization
  • Admin governance dashboard
  • IP indemnity protection

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.

Amazon Q Developer

  • AI-assisted code completion in an IDEnot Apache Spark MLlib
  • Automating Java and .NET application upgradesnot Apache Spark MLlib
  • Scanning codebases for security vulnerabilitiesnot Apache Spark MLlib
  • Getting AWS architecture guidance while codingnot Apache Spark MLlib
  • Automating feature implementation via agentsnot 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 Amazon Q Developer
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Amazon Q Developer
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Amazon Q Developer
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Amazon Q Developer

Where each one falls short

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

Amazon Q Developer

  • Deepest value and AWS-specific expertise is tied to the AWS ecosystem, less compelling for non-AWS shops
  • Free tier caps agentic requests at 50 per month, which can be exhausted quickly on larger tasks
  • Additional transformation usage beyond plan limits is billed per line of code, adding unpredictable costs
  • GitHub and Eclipse integrations are still in preview, indicating incomplete platform coverage

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

Amazon Q Developer

Free
  • FreeFree
    • 50 agentic requests per month
    • Access to latest Claude models
    • IDE and CLI use
  • Pro$19/month
    • Expanded agentic request limits
    • 4,000 lines of code per month per user for transformation, pooled at account level
    • Admin dashboard with user and policy management

Apache Spark MLlib

Free

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

Which should you pick?

Choose Amazon Q Developer if

  • You need code generation.
  • You want to start without paying.
  • You work on web, windows, mac, linux, api.
  • You also want agentic task automation.
Amazon Q Developer 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 Amazon Q Developer or Apache Spark MLlib better?
Neither clearly leads. Amazon Q Developer 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, Amazon Q Developer or Apache Spark MLlib?
Amazon Q Developer starts at Free and Apache Spark MLlib at Free.
Does Amazon Q Developer or Apache Spark MLlib run on more platforms?
Amazon Q Developer runs on web, windows, mac, linux, api. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Amazon Q Developer for free?
Both have a free tier, so you can try either at no cost before committing.
What is Amazon Q Developer best used for?
Amazon Q Developer is most often used for ai-assisted code completion in an ide, automating java and .net application upgrades, scanning codebases for security vulnerabilities, getting aws architecture guidance while coding. Of those, ai-assisted code completion in an ide and automating java and .net application upgrades are not what Apache Spark MLlib is typically brought in for.
What can Amazon Q Developer do that Apache Spark MLlib cannot?
Amazon Q Developer covers Code generation, Agentic task automation, Security scanning, Application transformation. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Amazon Q Developer: What does Amazon Q Developer cost?

There is a free tier with no cost, and a Pro tier priced at $19 per user per month that adds expanded agentic request limits, admin controls, and IP indemnity.

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.

Amazon Q Developer: What are the limits on the free plan?

The free tier includes 50 agentic requests per month, access to the latest Claude models in IDE and CLI, and 1,000 lines of code per month for Java transformation.

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.

Amazon Q Developer: How is usage metered beyond plan limits?

Java and .NET transformation usage beyond the plan's included lines of code is billed as overage at $0.003 per line of code submitted.

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.

Amazon Q Developer: Can I cancel my Amazon Q Developer Pro subscription anytime?

Subscriptions are billed monthly per user and prorated in the first month, but canceling before the end of a billing month still incurs the full month's subscription fee.

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.

Amazon Q Developer: What counts as an agentic request?

Agentic requests include both question-and-answer chat interactions and autonomous coding tasks performed by Q Developer's agents, all drawn from the same monthly quota.

Source
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.

Share

Related pages

More on Amazon Q Developer

More on Apache Spark MLlib

Keep looking

Other head to heads