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Cybersecurity · head to head

Feedzai vs Amazon Redshift ML

Feedzai logo

Feedzai

Cybersecurity

Real-time transaction fraud and financial crime detection for banks and payment processors

From
On request
Rated
-
Full reviewWebsite
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
Rated
-
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: Feedzai pricing is per transaction with an annual minimum, so a bank with seasonal or growing volume commits to a floor it may not use and pays overage above the band.; 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.
  • They diverge on capability: Feedzai covers Real-time scoring, Amazon Redshift ML covers CREATE MODEL in SQL.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

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

Attributes where Feedzai and Amazon Redshift ML differ
AttributeFeedzaiAmazon Redshift ML
Starting priceOn requestFree
Pricing modelquoteusage-based
Free tierNoYes
PlatformsWeb, LinuxWeb
CategoryCybersecurityMachine Learning
FoundedUnknown2006

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 Feedzai

  • Real-time scoring
  • Rule and model hybrid
  • Case manager
  • Behavioural biometrics
  • Model explainability
  • Deployment options

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

What people use each for

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

Feedzai

  • A bank joining an instant payments scheme where transfers are irrevocable and post-hoc recovery is impossiblenot Amazon Redshift ML
  • A card issuer whose existing rules engine cannot be changed without a release, so fraud waves run for daysnot Amazon Redshift ML
  • An acquirer needing per-merchant risk models rather than one portfolio-wide modelnot Amazon Redshift ML
  • A bank required by its regulator to explain automated declines to customers, which rules out opaque scoringnot Amazon Redshift ML

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

Where each one falls short

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

Feedzai

  • Pricing is per transaction with an annual minimum, so a bank with seasonal or growing volume commits to a floor it may not use and pays overage above the band.
  • It sits in the authorisation path, which makes every upgrade a change-controlled event with rollback plans, and the operational burden falls on the bank rather than the vendor.
  • Out of the box models need months of the customer own labelled fraud history before they beat the rules they replace, so the value case starts late.
  • AML and fraud are licensed as separate modules, so institutions expecting one platform fee find the transaction monitoring capability is a second line item.
  • The buyer profile is large institutions, so smaller banks and fintechs face minimums that make per-transaction economics unattractive below significant scale.

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.

Pricing, plan by plan

Feedzai

On request
  • Feedzai Financial Crime Platform$undefined/year
    • Priced by transaction volume with annual minimum commitment
    • Modules for fraud, AML and account opening licensed separately
    • Cloud, private cloud and on-premises deployment

Amazon Redshift ML

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

Which should you pick?

Choose Feedzai if

  • You need real-time scoring.
  • You work on Web, Linux.
  • You also want rule and model hybrid.
Feedzai review

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

Questions people ask

Is Feedzai or Amazon Redshift ML better?
Neither clearly leads. Feedzai starts at On request and Amazon Redshift ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Feedzai or Amazon Redshift ML?
Amazon Redshift ML has a free tier; the other does not. Paid plans start at On request for Feedzai and Free for Amazon Redshift ML.
Does Feedzai or Amazon Redshift ML run on more platforms?
Feedzai runs on Web, Linux. Amazon Redshift ML runs on Web.
Can I use Amazon Redshift ML for free?
Yes. Amazon Redshift ML has a free tier, so you can try it without paying. Feedzai starts at On request.
What is Feedzai best used for?
Feedzai is most often used for a bank joining an instant payments scheme where transfers are irrevocable and post-hoc recovery is impossible, a card issuer whose existing rules engine cannot be changed without a release, so fraud waves run for days, an acquirer needing per-merchant risk models rather than one portfolio-wide model, a bank required by its regulator to explain automated declines to customers, which rules out opaque scoring. Of those, a bank joining an instant payments scheme where transfers are irrevocable and post-hoc recovery is impossible and a card issuer whose existing rules engine cannot be changed without a release, so fraud waves run for days are not what Amazon Redshift ML is typically brought in for.
What can Feedzai do that Amazon Redshift ML cannot?
Feedzai covers Real-time scoring, Rule and model hybrid, Case manager, Behavioural biometrics. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Feedzai: Can Feedzai run on-premises?

Yes. On-premises and private cloud deployments are supported, which is why it appears in markets where transaction data cannot legally leave the country.

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.

Feedzai: Does it cover AML as well as fraud?

It does, but transaction monitoring is a separately licensed module. Assume two line items if you want both.

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.

Feedzai: How fast are decisions?

Designed for the authorisation window, typically tens of milliseconds. This is the constraint that rules out batch scoring architectures.

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.

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.

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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