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Spreadsheets · head to head

Quadratic vs Amazon Redshift ML

Quadratic logo

Quadratic

Spreadsheets

Infinite-canvas spreadsheet that runs Python, SQL and formulas in the same grid

From
Free
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

  • Each has a real cost: Quadratic aI usage is metered as a dollar allowance rather than a flat entitlement, so a team that adopts the agent enthusiastically will exhaust 20 USD per user per month quickly and the true cost per seat becomes unpredictable.; 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: Quadratic covers Multi-language cells, Amazon Redshift ML covers CREATE MODEL in SQL.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Quadratic and Amazon Redshift ML differ
AttributeQuadraticAmazon Redshift ML
Pricing modelPer user per monthusage-based
PlatformsWeb, macOS, Windows, LinuxWeb
CategorySpreadsheetsMachine Learning
FoundedUnknown2006

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 Quadratic

  • Multi-language cells
  • Infinite canvas
  • Direct database connections
  • AI agent in the sheet
  • WebAssembly engine
  • Python package support

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.

Quadratic

  • An analyst who wants a SQL result, a Python transformation and a set of manual assumptions visible in one file instead of three toolsnot Amazon Redshift ML
  • Ad hoc modelling where part of the logic is genuinely code and part is a judgement call typed into a cellnot Amazon Redshift ML
  • Sharing a reproducible analysis with a colleague who will only ever open a spreadsheet, not a notebooknot Amazon Redshift ML
  • Prototyping a data pull against a warehouse before committing it to a scheduled pipelinenot 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 Quadratic
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Quadratic
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Quadratic
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Quadratic

Where each one falls short

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

Quadratic

  • AI usage is metered as a dollar allowance rather than a flat entitlement, so a team that adopts the agent enthusiastically will exhaust 20 USD per user per month quickly and the true cost per seat becomes unpredictable.
  • Self-hosting is available only on the Enterprise plan, so an organisation that cannot send data to the vendor cloud has to enter a sales negotiation rather than run a container.
  • Excel and Google Sheets compatibility is partial, so files with heavy conditional formatting, pivot tables or macros do not survive a round trip and have to be rebuilt.
  • The product is young and the connector list is short, covering the main relational databases and two warehouses, so anything else has to be reached through Python code you write and maintain yourself.
  • There is no real mobile editing experience, which matters more than it sounds for a spreadsheet because approvals and quick checks routinely happen on a phone.

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

Quadratic

Free
  • PersonalFree
    • Limited AI usage
    • Limited files and connections
    • Limited sharing
  • Pro$18/month
    • Billed annually
    • 20 USD of AI credits per month
    • Unlimited files
  • Business$36/month
    • Billed annually
    • 40 USD of AI credits per month
    • Advanced permissions
  • Enterprise$undefined/year
    • Quoted
    • Self-hosting option
    • Custom AI usage

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 Quadratic if

  • You need multi-language cells.
  • You want to start without paying.
  • You work on Web, macOS, Windows, Linux.
  • You also want infinite canvas.
Quadratic 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 Quadratic or Amazon Redshift ML better?
Neither clearly leads. Quadratic starts at Free and Amazon Redshift ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Quadratic or Amazon Redshift ML?
Quadratic starts at Free and Amazon Redshift ML at Free.
Does Quadratic or Amazon Redshift ML run on more platforms?
Quadratic runs on Web, macOS, Windows, Linux. Amazon Redshift ML runs on Web.
Can I use Quadratic for free?
Both have a free tier, so you can try either at no cost before committing.
What is Quadratic best used for?
Quadratic is most often used for an analyst who wants a sql result, a python transformation and a set of manual assumptions visible in one file instead of three tools, ad hoc modelling where part of the logic is genuinely code and part is a judgement call typed into a cell, sharing a reproducible analysis with a colleague who will only ever open a spreadsheet, not a notebook, prototyping a data pull against a warehouse before committing it to a scheduled pipeline. Of those, an analyst who wants a sql result, a python transformation and a set of manual assumptions visible in one file instead of three tools and ad hoc modelling where part of the logic is genuinely code and part is a judgement call typed into a cell are not what Amazon Redshift ML is typically brought in for.
What can Quadratic do that Amazon Redshift ML cannot?
Quadratic covers Multi-language cells, Infinite canvas, Direct database connections, AI agent in the sheet. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Quadratic: Do I need to know Python to use it?

No, formulas work on their own, but the reason to choose Quadratic over Google Sheets is the code cells, so a team with no Python will not get the value.

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.

Quadratic: What happens when the AI credits run out?

Prompting stops until the next monthly reset or until you move up a plan; the spreadsheet itself keeps working normally.

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.

Quadratic: Can it replace a BI tool?

Not for scheduled distribution or governed metrics. It is for exploratory work by one person or a small team, not for dashboards a hundred people read.

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.

Quadratic: Is my database query sent to the vendor?

Connections run through the vendor service on all plans except Enterprise self-hosting, which is the option to take if that is unacceptable.

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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