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AI · head to head

Banana vs Amazon Redshift ML

Banana logo

Banana

AI

ML inference at scale

From
$1200/month
Free plan
No
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
Free plan
Yes
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: Banana banana shut down its serverless GPU infrastructure on 31 March 2024 at noon PST and told customers to migrate to another provider by that time; 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: Banana covers GPU inference, 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 Banana and Amazon Redshift ML actually diverge.

Attributes where Banana and Amazon Redshift ML differ
AttributeBananaAmazon Redshift ML
Starting price$1200/monthFree
Pricing modelsubscriptionusage-based
Free tierNoYes
PlatformsCloud, ApiWeb
CategoryAIMachine Learning
Founded20212006

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 Banana

  • GPU inference
  • Auto-scaling
  • Docker deployment
  • Low latency
  • REST API
  • Python SDK
  • Cloud support
  • Api 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.

Banana

  • Historically, serverless GPU inference for machine learning modelsnot Amazon Redshift ML
  • Migration reference for teams that ran models on Banana before the 2024 shutdownnot 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 Banana
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Banana
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Banana
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Banana

Where each one falls short

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

Banana

  • Banana shut down its serverless GPU infrastructure on 31 March 2024 at noon PST and told customers to migrate to another provider by that time
  • The vendor's own sunset notice names limited runway, retention problems and GPU supply constraints as the reasons for closing
  • The banana.dev site still displays pricing tiers, but every tier links to the sunset notice rather than to a purchase

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

Banana

$1200/month
  • Team$1200/month
    • 10 team members
    • 5 projects
    • 50 max parallel GPUs
  • Enterprise$null/custom
    • Custom seat limit
    • Custom GPU configuration

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 Banana if

  • You need gpu inference.
  • You work on Cloud, Api.
  • You also want auto-scaling.
Banana 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 Banana or Amazon Redshift ML better?
Neither clearly leads. Banana starts at $1200/month and Amazon Redshift ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Banana or Amazon Redshift ML?
Amazon Redshift ML has a free tier; the other does not. Paid plans start at $1200/month for Banana and Free for Amazon Redshift ML.
Does Banana or Amazon Redshift ML run on more platforms?
Banana runs on Cloud, 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. Banana starts at $1200/month.
What is Banana best used for?
Banana is most often used for historically, serverless gpu inference for machine learning models, migration reference for teams that ran models on banana before the 2024 shutdown. Of those, historically, serverless gpu inference for machine learning models and migration reference for teams that ran models on banana before the 2024 shutdown are not what Amazon Redshift ML is typically brought in for.
What can Banana do that Amazon Redshift ML cannot?
Banana covers GPU inference, Auto-scaling, Docker deployment, Low latency. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Banana: How much does Banana's Team plan cost?

The Team plan costs $1,200 per month plus the cost of compute resources at cost with zero markup applied.

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.

Banana: What is the maximum team size on Banana's Team plan?

The Team plan includes 10 team members and supports a maximum of 5 projects with up to 50 parallel GPUs.

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.

Banana: Does Banana offer an Enterprise plan with custom pricing?

Banana offers an Enterprise plan with custom pricing plus at-cost compute, including SAML SSO, automation API, and dedicated support.

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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