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Machine Learning · head to head

Amazon Redshift ML vs Veritone

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
Veritone logo

Veritone

AI

AI orchestration platform and applications for media archives, public safety evidence and advertising

From
On request
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.; Veritone nothing is publicly priced, and because consumption of the underlying AI engines is metered, the total cost of a large archive ingest is difficult to forecast before you have run one.
  • They diverge on capability: Amazon Redshift ML covers CREATE MODEL in SQL, Veritone covers aiWARE orchestration.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Amazon Redshift ML and Veritone differ
AttributeAmazon Redshift MLVeritone
Starting priceFreeOn request
Pricing modelusage-basedquote
Free tierYesNo
PlatformsWebWeb, API, Self-hosted
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 Veritone

  • aiWARE orchestration
  • iDEMS
  • Automated redaction
  • Media archive indexing
  • FedRAMP authorisation
  • Advertising and content intelligence
  • Voice and synthetic media
  • Self-hosted deployment

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 Veritone
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Veritone
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Veritone
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Veritone

Veritone

  • A police department buried in body camera footage that must be redacted before disclosure, where automated face and audio redaction converts weeks of manual work into review timenot Amazon Redshift ML
  • A broadcaster or sports rights holder that cannot find footage in its own archive and loses licensing revenue because searching it costs more than the clip earnsnot Amazon Redshift ML
  • A federal agency that needs AI processing inside an authorised boundary, where FedRAMP status decides the shortlist before capability doesnot Amazon Redshift ML
  • A media buyer wanting attribution on broadcast and podcast advertising that digital analytics tools cannot seenot 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.

Veritone

  • Nothing is publicly priced, and because consumption of the underlying AI engines is metered, the total cost of a large archive ingest is difficult to forecast before you have run one.
  • The company is mid-restructure after divesting its media agency, with revenue guidance well below earlier expectations, so a buyer signing a multi-year evidence contract is taking corporate risk they can read in the public filings but cannot control.
  • The orchestration proposition means Veritone sits between you and the model vendors, so when a specific engine underperforms on your content the remedy is a support case rather than switching provider yourself.
  • The product portfolio spans law enforcement, broadcast, advertising and synthetic voice, which is a wide surface for one engineering organisation and means attention to any single application depends on where the revenue growth currently is.
  • Automated redaction is a review aid, not a guarantee: an agency remains legally responsible for what is disclosed, so the staff time saved is smaller than the demonstration implies once verification is included.

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

Veritone

On request
  • aiWARE platform$undefined/year
    • Consumption-based use of the AI engine catalogue
    • Enterprise licence, quoted
    • Self-hosted and FedRAMP deployment options
  • iDEMS$undefined/year
    • Digital evidence management for law enforcement
    • Automated redaction and disclosure workflow
    • Quoted per agency, commonly on government contract vehicles
  • Media and entertainment applications$undefined/year
    • Archive indexing, monetisation and advertising intelligence
    • 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 Veritone if

  • You need aiware orchestration.
  • You work on Web, API, Self-hosted.
  • You also want idems.
Veritone review

Questions people ask

Is Amazon Redshift ML or Veritone better?
Neither clearly leads. Amazon Redshift ML starts at Free and Veritone at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Amazon Redshift ML or Veritone?
Amazon Redshift ML has a free tier; the other does not. Paid plans start at Free for Amazon Redshift ML and On request for Veritone.
Does Amazon Redshift ML or Veritone run on more platforms?
Amazon Redshift ML runs on Web. Veritone runs on Web, API, Self-hosted.
Can I use Amazon Redshift ML for free?
Yes. Amazon Redshift ML has a free tier, so you can try it without paying. Veritone starts at On request.
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 Veritone is typically brought in for.
What can Amazon Redshift ML do that Veritone cannot?
Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model. Veritone covers aiWARE orchestration, iDEMS, Automated redaction, Media archive indexing.

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.

Veritone: What do you actually buy from Veritone?

An application, most often iDEMS for digital evidence or a media archive product. aiWARE is the platform underneath, and it is rarely purchased on its own outside enterprise and government deals.

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.

Veritone: Is pricing published?

No. Everything is quoted, and platform use is metered by consumption of the underlying AI engines, which makes forecasting a large ingest difficult.

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.

Veritone: Is it approved for US government use?

aiWARE holds FedRAMP authorisation and can be deployed into self-hosted government tenants, which is frequently the reason it reaches a public sector shortlist.

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.

Veritone: Did Veritone sell its advertising agency?

Yes. Veritone One was divested in 2024 for up to $104 million, and the remaining company is focused on enterprise AI software and licensing.

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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