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Business Intelligence · head to head

Mode vs Amazon Redshift ML

Mode logo

Mode

Business Intelligence

Collaborative analytics for data teams

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: Mode free tier limited to 4GB RAM and 1 CPU for SQL notebooks, insufficient for large datasets; 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: Mode covers SQL Editor, 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 Mode and Amazon Redshift ML actually diverge.

Attributes where Mode and Amazon Redshift ML differ
AttributeModeAmazon Redshift ML
Pricing modelsubscriptionusage-based
CategoryBusiness IntelligenceMachine Learning
Founded20132006

Identical on both: starting price (Free), free tier (Yes), platforms (Web), 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 Mode

  • SQL Editor
  • Python/R Notebooks
  • Interactive Reports
  • Version Control
  • Scheduling
  • Snowflake
  • Redshift
  • BigQuery

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.

Mode

  • Self-service analyticsnot Amazon Redshift ML
  • Data explorationnot Amazon Redshift ML
  • Ad-hoc reportingnot Amazon Redshift ML
  • Collaborative analysisnot Amazon Redshift ML
  • Embedded analyticsnot 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 Mode
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Mode
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Mode
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Mode

Where each one falls short

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

Mode

  • Free tier limited to 4GB RAM and 1 CPU for SQL notebooks, insufficient for large datasets
  • Requires SQL knowledge for most analysis tasks, creating dependency on technical resources
  • Paid plan pricing not publicly listed; requires sales consultation
  • Recently acquired by ThoughtSpot in 2026, creating product direction uncertainty
  • Limited customization options for visual aspects and embedded analytics

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

Mode

Free
  • FreeFree
    • SQL Editor
    • Python/R Notebooks
    • Basic Charts
  • Business$65/month
    • Advanced Visualizations
    • Collaboration
    • Integrations

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 Mode if

  • You need sql editor.
  • You want to start without paying.
  • You also want python/r notebooks.
Mode 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 Mode or Amazon Redshift ML better?
Neither clearly leads. Mode 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, Mode or Amazon Redshift ML?
Mode starts at Free and Amazon Redshift ML at Free.
Does Mode or Amazon Redshift ML run on more platforms?
Both run on Web, so platform support will not decide this one for you.
Can I use Mode for free?
Both have a free tier, so you can try either at no cost before committing.
What is Mode best used for?
Mode is most often used for self-service analytics, data exploration, ad-hoc reporting, collaborative analysis. Of those, self-service analytics and data exploration are not what Amazon Redshift ML is typically brought in for.
What can Mode do that Amazon Redshift ML cannot?
Mode covers SQL Editor, Python/R Notebooks, Interactive Reports, Version Control. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Mode: What languages does Mode support for analysis?

Mode notebooks support SQL, Python (3.11 with pandas, NumPy, scikit-learn, matplotlib), and R (4.2.0 with ggplot2, dplyr, tidyr). Both Python and R allow additional library installation at runtime.

Source
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.

Mode: Can I integrate Mode notebook results into reports?

Yes. Mode allows adding notebook cell results directly to reports, with synchronized scheduling so reports re-run to keep data current.

Source
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.

Mode: Does Mode support collaborative analysis?

Yes. Mode notebooks provide moveable code blocks and markdown cells enabling exploratory analysis and team collaboration on data queries and visualizations.

Source
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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