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AI · head to head

Rev vs Apache Spark MLlib

Rev logo

Rev

AI

Human and AI transcription, captions and subtitles priced per minute

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: Rev human transcription at 1.99 US dollars a minute means roughly 120 dollars an hour of audio, so a long-form interview series costs more in transcription than most teams assume before they run the arithmetic.; 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: Rev covers Human transcription, 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 Rev and Apache Spark MLlib actually diverge.

Attributes where Rev and Apache Spark MLlib differ
AttributeRevApache Spark MLlib
Pricing modelPer minute for human work, per seat per month for AIopen-source
PlatformsWeb, iOS, Android, 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 Rev

  • Human transcription
  • AI transcription
  • Captions and subtitles
  • Legal transcripts
  • File analysis
  • Clio integration
  • Rev API
  • Speaker identification

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.

Rev

  • A law firm that needs a deposition transcript in a court-acceptable page format with a fixed turnaround and a published per-page price it can bill to a matter.not Apache Spark MLlib
  • A documentary or podcast producer costing a project in advance, because human transcription at a published per-minute rate makes the transcription line item a known number.not Apache Spark MLlib
  • A broadcaster needing compliant human captions rather than machine captions, where accuracy is a regulatory requirement and not a convenience.not Apache Spark MLlib
  • A team with high AI volume and occasional high-stakes files, using the subscription for the bulk and paying the discounted human rate only where accuracy matters.not 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 Rev
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Rev
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Rev
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Rev

Where each one falls short

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

Rev

  • Human transcription at 1.99 US dollars a minute means roughly 120 dollars an hour of audio, so a long-form interview series costs more in transcription than most teams assume before they run the arithmetic.
  • AI minutes are allocated per seat per month and do not roll over, so bursty workloads, which is how most media production actually behaves, waste allowance in quiet months and run out in busy ones.
  • The product direction has moved towards legal and investigative use, so a newsroom or media buyer is choosing a tool whose roadmap priority sits elsewhere.
  • Foreign-language subtitles are priced from 6.49 to 15.99 US dollars per minute, an order of magnitude above transcription, which makes multi-language localisation of any volume of video expensive enough to change the plan.
  • Human turnaround is stated in hours rather than guaranteed contractually on the published plans, so a hard broadcast or filing deadline needs an agreement beyond the standard terms.

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

Rev

Free
  • FreeFree
    • 45 AI transcription and caption minutes per month
    • Up to 5 files analysed
    • No human transcription included
  • Essentials$25.49/month
    • 5,000 AI minutes per seat per month
    • Up to 3 seats
    • 10 per cent discount on human transcription, captions and subtitles
  • Pro$47.99/month
    • 10,000 AI minutes per seat per month
    • Up to 5 seats
    • 15 per cent discount on human work
  • Unlimited$undefined/month
    • Unlimited AI minutes
    • Up to 500 files analysed at once
    • Dedicated account specialist

Apache Spark MLlib

Free

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

Which should you pick?

Choose Rev if

  • You need human transcription.
  • You want to start without paying.
  • You work on Web, iOS, Android, API.
  • You also want ai transcription.
Rev 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 Rev or Apache Spark MLlib better?
Neither clearly leads. Rev 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, Rev or Apache Spark MLlib?
Rev starts at Free and Apache Spark MLlib at Free.
Does Rev or Apache Spark MLlib run on more platforms?
Rev runs on Web, iOS, Android, API. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Rev for free?
Both have a free tier, so you can try either at no cost before committing.
What is Rev best used for?
Rev is most often used for a law firm that needs a deposition transcript in a court-acceptable page format with a fixed turnaround and a published per-page price it can bill to a matter., a documentary or podcast producer costing a project in advance, because human transcription at a published per-minute rate makes the transcription line item a known number., a broadcaster needing compliant human captions rather than machine captions, where accuracy is a regulatory requirement and not a convenience., a team with high ai volume and occasional high-stakes files, using the subscription for the bulk and paying the discounted human rate only where accuracy matters.. Of those, a law firm that needs a deposition transcript in a court-acceptable page format with a fixed turnaround and a published per-page price it can bill to a matter. and a documentary or podcast producer costing a project in advance, because human transcription at a published per-minute rate makes the transcription line item a known number. are not what Apache Spark MLlib is typically brought in for.
What can Rev do that Apache Spark MLlib cannot?
Rev covers Human transcription, AI transcription, Captions and subtitles, Legal transcripts. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Rev: How much is human transcription?

1.99 US dollars per minute with a stated accuracy target above 99 per cent, typically returned within 12 hours. Subscribers get 10 to 15 per cent off.

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.

Rev: Is there a free tier?

Yes, 45 AI minutes per month and up to 5 files analysed, with no human transcription included.

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.

Rev: Do unused AI minutes roll over?

No. The allowance is per seat per month and resets, which penalises irregular workloads.

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.

Rev: Can it do subtitles in other languages?

Yes, across seventeen languages, priced from 6.49 to 15.99 US dollars per minute, considerably more than transcription.

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.

Rev: Is Rev still a good choice for newsrooms?

It works, but the subscription tiers are now positioned around legal and investigative workflows, so a newsroom should compare it against a tool built specifically for journalism.

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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