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Business Intelligence · head to head

Fabi vs Amazon Redshift ML

Fabi logo

Fabi

Business Intelligence

AI notebooks combining SQL, Python and no-code for small 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: Fabi fabi is an early-stage company with a small team, so the durability risk is real and there is no obvious migration path for Smartbooks if it stops trading.; 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: Fabi covers Smartbooks, 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 Fabi and Amazon Redshift ML actually diverge.

Attributes where Fabi and Amazon Redshift ML differ
AttributeFabiAmazon Redshift ML
Pricing modelPer user per monthusage-based
CategoryBusiness IntelligenceMachine Learning
FoundedUnknown2006

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 Fabi

  • Smartbooks
  • Smart Reports
  • AI analysis
  • Scheduled runs
  • Database connectors
  • Viewer seats

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.

Fabi

  • A single analyst at a startup fielding ad hoc questions faster than dashboards can be built for themnot Amazon Redshift ML
  • An operations team that needs Python for a one-off analysis but has to hand the result to non-technical colleaguesnot Amazon Redshift ML
  • Replacing a set of scheduled Jupyter notebooks that nobody outside the data team can read or rerunnot Amazon Redshift ML
  • Exploratory work against a warehouse where building a semantic model first would cost more than the answer is worthnot 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 Fabi
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Fabi
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Fabi
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Fabi

Where each one falls short

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

Fabi

  • Fabi is an early-stage company with a small team, so the durability risk is real and there is no obvious migration path for Smartbooks if it stops trading.
  • There is no meaningful governance layer: no data catalogue, no certified metric definitions and limited lineage, so it does not scale to an organisation that needs a single agreed number.
  • The free and Builder tiers cap AI requests, and the daily cap on Starter is reached quickly during genuine exploratory work.
  • Connector counts are limited by tier, so the Builder plan at 39 dollars connects to exactly one data source, which is rarely enough in practice.
  • It overlaps heavily with what Snowflake, Databricks and Hex now ship natively, so a company already paying for one of those is buying a fourth notebook interface.

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

Fabi

Free
  • StarterFree
    • One builder seat
    • Ten dashboard viewers
    • Five Smartbooks
  • Builder$39/month
    • Twenty-five Smart Report viewers
    • Ten Smartbooks with workflows
    • One data connector
  • Team$199/month
    • Four builder seats, extra seats at 39 USD
    • Fifty viewers
    • Premium connector
  • Enterprise$undefined/year
    • Unlimited builder seats
    • Full connector access
    • Custom security review

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 Fabi if

  • You need smartbooks.
  • You want to start without paying.
  • You also want smart reports.
Fabi 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 Fabi or Amazon Redshift ML better?
Neither clearly leads. Fabi 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, Fabi or Amazon Redshift ML?
Fabi starts at Free and Amazon Redshift ML at Free.
Does Fabi 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 Fabi for free?
Both have a free tier, so you can try either at no cost before committing.
What is Fabi best used for?
Fabi is most often used for a single analyst at a startup fielding ad hoc questions faster than dashboards can be built for them, an operations team that needs python for a one-off analysis but has to hand the result to non-technical colleagues, replacing a set of scheduled jupyter notebooks that nobody outside the data team can read or rerun, exploratory work against a warehouse where building a semantic model first would cost more than the answer is worth. Of those, a single analyst at a startup fielding ad hoc questions faster than dashboards can be built for them and an operations team that needs python for a one-off analysis but has to hand the result to non-technical colleagues are not what Amazon Redshift ML is typically brought in for.
What can Fabi do that Amazon Redshift ML cannot?
Fabi covers Smartbooks, Smart Reports, AI analysis, Scheduled runs. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Fabi: Is there a free plan?

Yes, a Starter tier with one builder, ten viewers, five Smartbooks and ten AI requests a day.

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.

Fabi: Do viewers cost money?

Viewers are bundled by tier rather than charged individually; builders are the priced seat.

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.

Fabi: Can I run arbitrary Python?

Yes, Smartbooks include Python cells alongside SQL and no-code steps.

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.

Fabi: How many data connectors does the entry paid plan include?

One. Additional and premium connectors come with the Team tier.

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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