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Spreadsheets · head to head

Equals vs Amazon Redshift ML

Equals logo

Equals

Spreadsheets

A real spreadsheet with SQL result sets landing in its cells, now sold only on annual contracts starting at 24,000 USD

From
$24000/year
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: Equals the price list now starts at 24,000 USD a year with no monthly billing and no self-serve signup, having offered a free plan in 2023 and 39 USD per seat in 2024, so any team that evaluated it previously is looking at a different product commercially.; 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: Equals covers Connected sheets, Amazon Redshift ML covers CREATE MODEL in SQL.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Equals and Amazon Redshift ML differ
AttributeEqualsAmazon Redshift ML
Starting price$24000/yearFree
Pricing modelPer year by connector count and refresh frequencyusage-based
Free tierNoYes
PlatformsWeb, APIWeb
CategorySpreadsheetsMachine 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 Equals

  • Connected sheets
  • Real spreadsheet mechanics
  • Cell references into SQL
  • Equals Warehouse
  • Views
  • Writeback

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.

Equals

  • A revenue operations team that wants software metrics computed in a spreadsheet rather than defined in a BI semantic layernot Amazon Redshift ML
  • Analysts who need a SQL result and manual assumptions in the same file, with the assumptions visible as cellsnot Amazon Redshift ML
  • Joining Stripe, Salesforce and a production database without building a warehouse and pipeline firstnot Amazon Redshift ML
  • Distributing a recurring numbers pack to Slack, email or a wall display without rebuilding it as a dashboardnot 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 Equals
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Equals
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Equals
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Equals

Where each one falls short

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

Equals

  • The price list now starts at 24,000 USD a year with no monthly billing and no self-serve signup, having offered a free plan in 2023 and 39 USD per seat in 2024, so any team that evaluated it previously is looking at a different product commercially.
  • Your data is copied out of your infrastructure by design, into a Snowflake warehouse that Equals operates, with Fivetran holding non-SQL source data in transit, and no self-hosted or bring-your-own-warehouse option is documented.
  • The financial function category contains only IRR, NPV and PMT, with no XIRR, XNPV, FV, PV, RATE or NPER, and there is no LAMBDA, LET, OFFSET or INDIRECT, so a substantial class of existing spreadsheet models cannot be ported without rework.
  • No documentation of importing or exporting Excel files exists anywhere in the docs index; the supported file sources are CSV and Google Sheets and the only documented export is dashboards as PDF, which is a notable gap for a product positioning itself as a successor to Excel.
  • Data freshness is a pricing lever rather than a setting, at six hours on Essential, one hour on Business and fifteen minutes on Enterprise, and the feature that removes row limits is itself gated above the entry tier, so the Essential row ceiling is neither published nor unlimited.

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

Equals

$24000/year
  • Essential$24000/year
    • Unlimited viewers and unlimited editors
    • 3 connectors
    • Data sync every 6 hours
  • Business$36000/year
    • Unlimited viewers and unlimited editors
    • 5 connectors or a warehouse connection
    • Data sync every hour
  • Enterprise$60000/year
    • Unlimited viewers and unlimited editors
    • Unlimited connectors or a warehouse connection
    • Data sync every 15 minutes
  • Connector add-ons$199/month
    • 199 USD per month for each additional database or import script connector
    • 699 USD per month for each additional connector of any other type
    • Available on Business and Enterprise

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 Equals if

  • You need connected sheets.
  • You work on Web, API.
  • You also want real spreadsheet mechanics.
Equals 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 Equals or Amazon Redshift ML better?
Neither clearly leads. Equals starts at $24000/year and Amazon Redshift ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Equals or Amazon Redshift ML?
Amazon Redshift ML has a free tier; the other does not. Paid plans start at $24000/year for Equals and Free for Amazon Redshift ML.
Does Equals or Amazon Redshift ML run on more platforms?
Equals runs on Web, API. 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. Equals starts at $24000/year.
What is Equals best used for?
Equals is most often used for a revenue operations team that wants software metrics computed in a spreadsheet rather than defined in a bi semantic layer, analysts who need a sql result and manual assumptions in the same file, with the assumptions visible as cells, joining stripe, salesforce and a production database without building a warehouse and pipeline first, distributing a recurring numbers pack to slack, email or a wall display without rebuilding it as a dashboard. Of those, a revenue operations team that wants software metrics computed in a spreadsheet rather than defined in a bi semantic layer and analysts who need a sql result and manual assumptions in the same file, with the assumptions visible as cells are not what Amazon Redshift ML is typically brought in for.
What can Equals do that Amazon Redshift ML cannot?
Equals covers Connected sheets, Real spreadsheet mechanics, Cell references into SQL, Equals Warehouse. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Equals: Is it a real spreadsheet or a modelling tool with a grid?

A real spreadsheet. A1 references, absolute reference locking, pivot tables and spilling array formulas all work, and SQL results land in ranges that ordinary cells reference.

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.

Equals: What does it actually cost?

24,000 USD a year for Essential, 36,000 for Business and 60,000 for Enterprise, billed annually. There is no monthly option and no self-serve signup; the trial is negotiated per customer.

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.

Equals: Are seats charged?

No. Viewers and editors are unlimited on every plan. The meters are connector count, refresh frequency and AI spend, with additional connectors at 199 or 699 USD a month.

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.

Equals: Does my data stay in my own warehouse?

No. Connecting more than one source syncs data into a Snowflake warehouse that Equals runs, and non-SQL sources pass through Fivetran. There is no documented self-hosted option.

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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