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Sourcegraph Cody vs Apache Spark MLlib

Sourcegraph Cody logo

Sourcegraph Cody

AI

Enterprise AI coding assistant with whole-codebase context

From
$59/month
Free plan
No
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

  • Only Apache Spark MLlib has a free tier, so it costs nothing to try first.
  • Each has a real cost: Sourcegraph Cody no longer available to individual developers or small teams after free and Pro tiers were discontinued in mid-2025; 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: Sourcegraph Cody covers Multi-repository context, 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 Sourcegraph Cody and Apache Spark MLlib actually diverge.

Attributes where Sourcegraph Cody and Apache Spark MLlib differ
AttributeSourcegraph CodyApache Spark MLlib
Starting price$59/monthFree
Pricing modelquoteopen-source
Free tierNoYes
Platformsweb, windows, mac, linuxLinux, macOS, Windows
CategoryAIMachine Learning
Founded20131999

Identical on both: 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 Sourcegraph Cody

  • Multi-repository context
  • Large context window
  • Context filters
  • Model flexibility
  • IDE integration

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.

Sourcegraph Cody

  • Cross-repository code understanding for large engineering orgsnot Apache Spark MLlib
  • AI-assisted code review and refactoring at enterprise scalenot Apache Spark MLlib
  • Enforcing data-boundary controls when using third-party LLMsnot Apache Spark MLlib
  • Codebase-aware chat and autocomplete for developersnot 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 Sourcegraph Cody
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Sourcegraph Cody
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Sourcegraph Cody
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Sourcegraph Cody

Where each one falls short

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

Sourcegraph Cody

  • No longer available to individual developers or small teams after free and Pro tiers were discontinued in mid-2025
  • Priced substantially higher than many competing AI coding assistants, with Enterprise plans typically requiring annual contracts
  • Sourcegraph has shifted its individual-developer focus to a separate product, Amp, creating uncertainty about Cody's long-term product priority
  • Requires integration with Sourcegraph's broader code intelligence platform rather than working as a lightweight standalone extension

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

Sourcegraph Cody

$59/month
  • Enterprise$59/month
    • Whole-codebase AI context
    • Context filters for sensitive code
    • Multi-repository analysis

Apache Spark MLlib

Free

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

Which should you pick?

Choose Sourcegraph Cody if

  • You need multi-repository context.
  • You work on web, windows, mac, linux.
  • You also want large context window.
Sourcegraph Cody 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 Sourcegraph Cody or Apache Spark MLlib better?
Neither clearly leads. Sourcegraph Cody starts at $59/month and Apache Spark MLlib at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Sourcegraph Cody or Apache Spark MLlib?
Apache Spark MLlib has a free tier; the other does not. Paid plans start at $59/month for Sourcegraph Cody and Free for Apache Spark MLlib.
Does Sourcegraph Cody or Apache Spark MLlib run on more platforms?
Sourcegraph Cody runs on web, windows, mac, linux. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Apache Spark MLlib for free?
Yes. Apache Spark MLlib has a free tier, so you can try it without paying. Sourcegraph Cody starts at $59/month.
What is Sourcegraph Cody best used for?
Sourcegraph Cody is most often used for cross-repository code understanding for large engineering orgs, ai-assisted code review and refactoring at enterprise scale, enforcing data-boundary controls when using third-party llms, codebase-aware chat and autocomplete for developers. Of those, cross-repository code understanding for large engineering orgs and ai-assisted code review and refactoring at enterprise scale are not what Apache Spark MLlib is typically brought in for.
What can Sourcegraph Cody do that Apache Spark MLlib cannot?
Sourcegraph Cody covers Multi-repository context, Large context window, Context filters, Model flexibility. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Sourcegraph Cody: Is Sourcegraph Cody still available to individual developers?

No, as of mid-2025 Cody Free and Pro plans were discontinued; Cody is now sold only as part of Sourcegraph's Enterprise plan, which requires an annual contract.

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.

Sourcegraph Cody: How much does Sourcegraph Enterprise cost?

Enterprise plans start around $16,000 and scale with team size and seat count, including bundled AI credits; exact pricing requires a sales quote.

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.

Sourcegraph Cody: How does the AI credit system work on Enterprise plans?

Each plan includes a pooled allocation of AI credits shared across the organization; credits do not expire and roll over at renewal, with volume credit add-ons available.

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.

Sourcegraph Cody: What happens if my organization exceeds its credit allocation?

Sourcegraph's pricing page addresses overage and offers additional volume credit buckets for teams that need more than their committed allocation.

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.

Sourcegraph Cody: Is a free trial available for Sourcegraph Enterprise?

Sourcegraph's pricing page lists free trial availability as one of its published FAQ topics for prospective Enterprise customers.

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.

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