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Cybersecurity · head to head

Sigstore vs Apache Spark MLlib

Sigstore logo

Sigstore

Cybersecurity

Free public signing and transparency infrastructure for open source artifacts

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: Sigstore the security model depends on somebody watching the log. The documentation states that compromise of an identity provider or of Fulcio itself is detectable only if third parties monitor the transparency log, the monitoring tool is a community-tier rather than core project, and almost no consumer runs one.; 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: Sigstore covers Fulcio, Apache Spark MLlib covers DataFrame-based pipelines.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Sigstore and Apache Spark MLlib differ
AttributeSigstoreApache Spark MLlib
Pricing modelOpen source, public instance free to useopen-source
PlatformsWeb, Linux, macOS, Windows, Self-hostedLinux, macOS, Windows
CategoryCybersecurityMachine 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 Sigstore

  • Fulcio
  • Rekor
  • Keyless signing
  • Multi-language clients
  • Timestamp authority
  • Neutral governance

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.

Sigstore

  • Open source projects signing releases without running a certificate authoritynot Apache Spark MLlib
  • Organisations meeting a signed-artifact requirement without buying a signing productnot Apache Spark MLlib
  • Publishing provenance that a consumer can verify independently of younot Apache Spark MLlib
  • Self-hosting the same components where a public log is unacceptablenot 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 Sigstore
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Sigstore
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Sigstore
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Sigstore

Where each one falls short

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

Sigstore

  • The security model depends on somebody watching the log. The documentation states that compromise of an identity provider or of Fulcio itself is detectable only if third parties monitor the transparency log, the monitoring tool is a community-tier rather than core project, and almost no consumer runs one.
  • It is a 99.5 percent objective with no service level agreement, which permits several hours of downtime a month and offers no remedy. A pipeline that signs on every build has taken a hard dependency on a free service with no contract behind it.
  • Log scale is a live engineering problem rather than a theoretical one. The active shard holds billions of entries, the log has already been sharded twice, and sharding version 1 requires stopping traffic, which is why a replacement was built.
  • Ten-minute certificates make trust depend on log availability. Verifying an older signature relies on the log entry proving it was made inside that window, so a lost or unreachable entry can render a valid artifact unverifiable.
  • Migration debt is substantial and ongoing. Version 2 of the log is generally available but not the public default, the signing client has an announced breaking release ahead, some official clients lag the new log format, and a post-quantum migration is named as the next break after that.

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

Sigstore

Free
  • Public good instanceFree
    • Free to everyone with no contract
    • 99.5 percent availability objective, not an agreement
    • 100KB cap per attestation upload
  • Self-hostedFree
    • Apache-2.0
    • Run your own Fulcio and Rekor
    • Rekor v2 available for self-hosters

Apache Spark MLlib

Free

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

Which should you pick?

Choose Sigstore if

  • You need fulcio.
  • You want to start without paying.
  • You work on Web, Linux, macOS, Windows, Self-hosted.
  • You also want rekor.
Sigstore 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 Sigstore or Apache Spark MLlib better?
Neither clearly leads. Sigstore 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, Sigstore or Apache Spark MLlib?
Sigstore starts at Free and Apache Spark MLlib at Free.
Does Sigstore or Apache Spark MLlib run on more platforms?
Sigstore runs on Web, Linux, macOS, Windows, Self-hosted. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Sigstore for free?
Both have a free tier, so you can try either at no cost before committing.
What is Sigstore best used for?
Sigstore is most often used for open source projects signing releases without running a certificate authority, organisations meeting a signed-artifact requirement without buying a signing product, publishing provenance that a consumer can verify independently of you, self-hosting the same components where a public log is unacceptable. Of those, open source projects signing releases without running a certificate authority and organisations meeting a signed-artifact requirement without buying a signing product are not what Apache Spark MLlib is typically brought in for.
What can Sigstore do that Apache Spark MLlib cannot?
Sigstore covers Fulcio, Rekor, Keyless signing, Multi-language clients. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Sigstore: Is the public instance really free?

Yes, with no contract and no paid tier. That is also the weakness: a 99.5 percent objective with no agreement, no remedy and support through Slack.

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.

Sigstore: Has the public log moved to Rekor v2?

No. Version 2 reached general availability in October 2025 and self-hosters can use it, but the public instance still defaults to version 1 and the project has said it will for the foreseeable future.

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.

Sigstore: Does Sigstore make my dependencies safe?

No, and this is a category error worth avoiding. It tells you who published something. It has no knowledge of what the artifact contains or whether it is vulnerable.

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.

Sigstore: What are the rate limits?

Not published. Only the 100KB cap per attestation upload is documented, so do not design a high-volume pipeline around assumed throughput.

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.

Sigstore: Should we self-host it?

If a public record of every signature is unacceptable, or if a free service with no agreement cannot sit in your build path, then yes. Otherwise the public instance is what most projects use.

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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