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Machine Learning · head to head

Amazon Redshift ML vs Verbit

Amazon Redshift ML logo

Amazon Redshift ML

Machine Learning

SQL statements in Redshift that train models on SageMaker and return them as functions

From
Free
Free plan
Yes
Full reviewWebsite
Verbit logo

Verbit

AI

Transcription and captioning combining automatic speech recognition with professional human correction

From
$24/month
Free plan
No
Full reviewWebsite

The short version

  • Only Amazon Redshift ML has a free tier, so it costs nothing to try first.
  • Each has a real cost: Amazon Redshift ML training is billed by SageMaker separately from Redshift, so a feature that looks like a free SQL statement produces a second line item on a different part of the bill that the analyst who ran it usually cannot see.; Verbit only a small self-service tier has a published price; the institutional plans that most education buyers actually need are quoted, so budgeting requires a sales cycle and comparison against competitors requires several.
  • They diverge on capability: Amazon Redshift ML covers CREATE MODEL in SQL, Verbit covers ASR with human correction.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which Amazon Redshift ML and Verbit actually diverge.

Attributes where Amazon Redshift ML and Verbit differ
AttributeAmazon Redshift MLVerbit
Starting priceFree$24/month
Pricing modelusage-basedPer month by transcription hours
Free tierYesNo
PlatformsWebWeb, API
CategoryMachine LearningAI
Founded2006Unknown

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 Amazon Redshift ML

  • CREATE MODEL in SQL
  • Automatic model selection
  • Local inference
  • Bring your own model
  • Algorithm selection
  • Cost ceiling controls
  • Existing warehouse security
  • Batch and interactive scoring

Only in Verbit

  • ASR with human correction
  • Live realtime captioning
  • Domain-tuned models
  • Translation and subtitling
  • Audio description
  • LMS and video platform integrations
  • Court-admissible transcripts
  • Turnaround tiers

What people use each for

The jobs each tool is most often brought in to do.

Amazon Redshift ML

  • Adding a churn or propensity score to an existing dashboard where the data is already in Redshift and nobody needs a bespoke modelnot Verbit
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Verbit
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Verbit
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Verbit

Verbit

  • A university that must caption recorded teaching to meet accessibility law and wants machine output for general content with human correction only where a student accommodation appliesnot Amazon Redshift ML
  • A broadcaster needing live realtime captioning to regulatory standard, which the VITAC operation supplies rather than a pure software vendornot Amazon Redshift ML
  • A law firm or court reporting operation needing certified transcripts of depositions with a defensible chain of accuracynot Amazon Redshift ML
  • A market research team transcribing focus groups where speaker separation and verbatim fidelity matter more than turnaround speednot Amazon Redshift ML

Where each one falls short

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

Amazon Redshift ML

  • Training is billed by SageMaker separately from Redshift, so a feature that looks like a free SQL statement produces a second line item on a different part of the bill that the analyst who ran it usually cannot see.
  • Autopilot searches many candidate models by default and the duration and cost of CREATE MODEL scale with the data size and the MAX_CELLS setting, so an unconstrained statement against a large table is an expensive accident rather than an experiment.
  • Local inference runs on the Redshift cluster itself, so scoring millions of rows competes for the resources the warehouse exists to provide, and the remote inference alternative adds a per-batch network call plus an hourly SageMaker endpoint charge that persists whether or not anyone queries it.
  • The supported problem types are limited to what the exposed algorithms cover, so anything involving text, images, sequences, a custom loss function or a bespoke evaluation metric is out of scope and has to be built conventionally.
  • There is no retraining schedule, drift detection or model registry, so a model created by a statement stays exactly as trained until somebody remembers to recreate it, and nothing in the warehouse will report that its accuracy has decayed.

Verbit

  • Only a small self-service tier has a published price; the institutional plans that most education buyers actually need are quoted, so budgeting requires a sales cycle and comparison against competitors requires several.
  • Accuracy claims are benchmarked on clean audio with standard accents, and real lecture theatre recordings with technical vocabulary and non-native speakers fall well short, so the contracted level and the audio assumptions behind it must be written into the agreement.
  • The human correction layer is what delivers the accuracy institutions buy, and it introduces turnaround times measured in hours or days, which does not suit anyone expecting transcripts immediately after a session.
  • Cost scales with hours of content, so an institution that recorded everything during remote teaching and never reduced the volume can find transcription costing more than the lecture capture platform generating the recordings.
  • Verbit has grown by acquisition and consolidated several transcription brands, and customers of acquired services have been migrated onto Verbit contracts and pricing on the acquirer's timetable rather than their own.

Pricing, plan by plan

Amazon Redshift ML

Free
  • Free TrialFree
    • 2-month trial
    • 750 DC2.Large hours
  • On-Demand$0.25/hour
    • Per-node pricing
    • SageMaker training

Verbit

$24/month
  • Standard$24/month
    • From $24 per month
    • 100 hours per month split as 50 live and 50 post-production
    • Self-service sign-up
  • Campus Complete$undefined/year
    • Unlimited hours for an institution
    • Built around media accessibility obligations
    • LMS and lecture capture integrations
  • Legal Capture and Legal Visor$undefined/year
    • Unlimited legal transcription with court-admissible transcripts on request
    • Live AI transcription and case insights
    • Quoted
  • Custom$undefined/year
    • Enterprise deployment across the product suite
    • Negotiated accuracy levels and turnaround
    • Quoted

Which should you pick?

Choose Amazon Redshift ML if

  • You need create model in sql.
  • You want to start without paying.
  • You also want automatic model selection.
Amazon Redshift ML review

Choose Verbit if

  • You need asr with human correction.
  • You work on Web, API.
  • You also want live realtime captioning.
Verbit review

Questions people ask

Is Amazon Redshift ML or Verbit better?
Neither clearly leads. Amazon Redshift ML starts at Free and Verbit at $24/month, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Amazon Redshift ML or Verbit?
Amazon Redshift ML has a free tier; the other does not. Paid plans start at Free for Amazon Redshift ML and $24/month for Verbit.
Does Amazon Redshift ML or Verbit run on more platforms?
Amazon Redshift ML runs on Web. Verbit runs on Web, API.
Can I use Amazon Redshift ML for free?
Yes. Amazon Redshift ML has a free tier, so you can try it without paying. Verbit starts at $24/month.
What is Amazon Redshift ML best used for?
Amazon Redshift ML is most often used for adding a churn or propensity score to an existing dashboard where the data is already in redshift and nobody needs a bespoke model, letting an analytics team test whether a predictive column has any business value before asking for data science headcount, scoring rows inside a sql pipeline where moving data out to a separate service would add fragility for little benefit, organisations committed to aws whose main constraint is a data science backlog rather than modelling sophistication. Of those, adding a churn or propensity score to an existing dashboard where the data is already in redshift and nobody needs a bespoke model and letting an analytics team test whether a predictive column has any business value before asking for data science headcount are not what Verbit is typically brought in for.
What can Amazon Redshift ML do that Verbit cannot?
Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model. Verbit covers ASR with human correction, Live realtime captioning, Domain-tuned models, Translation and subtitling.

Answered from the vendors’ own pages

Amazon Redshift ML: Does it require SageMaker?

Yes. Redshift ML is an interface; the training happens in SageMaker and needs an IAM role and an S3 bucket for the intermediate data.

Verbit: Is there published pricing?

Only for the entry Standard tier at around $24 a month for 100 hours split between live and post-production. Campus Complete and the legal and media unlimited plans are quoted.

Amazon Redshift ML: Is there an extra charge?

The SQL interface is part of Redshift, but the training runs as a SageMaker job charged at SageMaker rates, and a remote inference endpoint is billed for as long as it exists.

Verbit: What accuracy does Verbit deliver?

Machine-only output is materially less accurate than the corrected tiers. Accuracy is a contracted service level on the human-corrected plans, and it assumes reasonable audio quality. Negotiate the figure and the remedy, not the marketing number.

Amazon Redshift ML: What kinds of model can it build?

Regression, binary and multiclass classification through the automatic path, plus direct use of XGBoost, linear learner, multilayer perceptron and K-means. Anything beyond structured tabular prediction is out of scope.

Verbit: What is VITAC?

A large North American broadcast captioning business Verbit acquired in 2021. It is why Verbit can supply live realtime captioning at broadcast standard rather than only post-production transcripts.

Amazon Redshift ML: Can I use a model I trained myself?

Yes, through the bring-your-own-model path, either compiled into the cluster for local inference or called as a remote SageMaker endpoint.

Verbit: Can I mix machine and human transcription?

Yes, and it is the main reason education buyers choose Verbit: route routine content through machine processing and accommodation-covered content through human correction on the same account.

Amazon Redshift ML: Does it retrain automatically?

No. Retraining means running CREATE MODEL again, on a schedule you build yourself, and nothing in the product monitors whether it is needed.

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