Machine Learning · head to head
Amazon Redshift ML vs Rev
Amazon Redshift ML
Machine Learning
SQL statements in Redshift that train models on SageMaker and return them as functions
- From
- Free
- Free plan
- Yes
Rev
AI
Human and AI transcription, captions and subtitles priced per minute
- From
- Free
- Free plan
- Yes
The short version
- 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.; 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.
- They diverge on capability: Amazon Redshift ML covers CREATE MODEL in SQL, Rev covers Human transcription.
- Prices and features above were last checked on 31 August 2026.
Where they differ
Only the attributes on which Amazon Redshift ML and Rev actually diverge.
| Attribute | Amazon Redshift ML | Rev |
|---|---|---|
| Pricing model | usage-based | Per minute for human work, per seat per month for AI |
| Platforms | Web | Web, iOS, Android, API |
| Category | Machine Learning | AI |
| Founded | 2006 | Unknown |
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 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 Rev
- Human transcription
- AI transcription
- Captions and subtitles
- Legal transcripts
- File analysis
- Clio integration
- Rev API
- Speaker identification
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 Rev
- Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Rev
- Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Rev
- Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Rev
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 Amazon Redshift ML
- 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 Amazon Redshift ML
- A broadcaster needing compliant human captions rather than machine captions, where accuracy is a regulatory requirement and not a convenience.not Amazon Redshift ML
- 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 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.
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.
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
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
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.
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.
Questions people ask
- Is Amazon Redshift ML or Rev better?
- Neither clearly leads. Amazon Redshift ML starts at Free and Rev at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Amazon Redshift ML or Rev?
- Amazon Redshift ML starts at Free and Rev at Free.
- Does Amazon Redshift ML or Rev run on more platforms?
- Amazon Redshift ML runs on Web. Rev runs on Web, iOS, Android, API.
- Can I use Amazon Redshift ML for free?
- Both have a free tier, so you can try either at no cost before committing.
- 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 Rev is typically brought in for.
- What can Amazon Redshift ML do that Rev cannot?
- Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model. Rev covers Human transcription, AI transcription, Captions and subtitles, Legal transcripts.
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.
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.
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.
Rev: Is there a free tier?
Yes, 45 AI minutes per month and up to 5 files analysed, with no human transcription included.
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.
Rev: Do unused AI minutes roll over?
No. The allowance is per seat per month and resets, which penalises irregular workloads.
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.
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.
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.
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.
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