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Machine Learning · head to head

Amazon Redshift ML vs Vellum

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
Visit Amazon Redshift MLFull review
Vellum logo

Vellum

AI

Personalized AI assistant with persistent memory and autonomous action-taking

From
Free
Free plan
Yes
Visit VellumFull review

The short version

  • Vellum is a personal AI assistant.
  • Free plans on both; paid from $0.25/mo and $30/mo.
  • Only Amazon Redshift ML lists CREATE MODEL in SQL; only Vellum lists persistent AI identity.
  • Where Vellum falls short: pricing varies significantly by computational tier, making budget planning difficult.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

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

Attributes where Amazon Redshift ML and Vellum differ
AttributeAmazon Redshift MLVellum
Pricing modelUsage-basedFreemium with paid plans and pay-as-you-go credits
PlatformsWebWeb, macOS, iOS, Slack, Telegram, Terminal
CategoryMachine LearningAI

Identical on both: starting price (Free), free tier (Yes).

Not yet recorded: founded for Vellum.

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 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

Only in Vellum

  • Persistent AI identity
  • Long-term memory
  • Email automation
  • Calendar management
  • Autonomous task execution
  • Multi-device synchronization
  • Custom plugins
  • Open-source deployment

What people use each for

The jobs each tool is most often brought in to do.

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 Vellum
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Vellum
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Vellum
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Vellum

Vellum

  • Personal productivity automation across email and calendarnot Amazon Redshift ML
  • CRM updates and pipeline managementnot Amazon Redshift ML
  • Expense report generation and managementnot Amazon Redshift ML
  • Document drafting and content creationnot Amazon Redshift ML
  • Meeting scheduling and calendar coordinationnot Amazon Redshift ML

Where each one falls short

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

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

Vellum

  • Pricing varies significantly by computational tier, making budget planning difficult
  • Requires explicit permission for sensitive actions, limiting autonomy
  • Self-hosting option may intimidate non-technical users
  • Learning curve to develop customizations with plugins

Pricing, plan by plan

Amazon Redshift ML

Free
  • Free TrialFree
    • 2-month trial
    • 750 DC2.Large hours
  • On-Demand$0.25/hour
    • Per-node pricing
    • SageMaker training

Vellum

Free
  • FreeFree
    • Starter credits included
    • Basic features
  • Mighty$30/mo
    • 1 vCPU, 2 GiB RAM
    • 10 GB storage
    • Included usage allowance
  • Super$100/mo
    • 2.5 vCPU, 5 GiB RAM
    • 30 GB storage
    • Assistant email and subdomain
  • Ultra$200/mo
    • 4 vCPU, 8 GiB RAM
    • 60 GB storage
    • Assistant email and subdomain

Which should you pick?

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

Choose Vellum if

  • You need persistent ai identity.
  • You want to start without paying.
  • You work on Web, macOS, iOS, Slack, Telegram, Terminal.
  • You also want long-term memory.
Vellum review

Questions people ask

Is Amazon Redshift ML or Vellum better?
Neither clearly leads. Amazon Redshift ML starts at Free and Vellum at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Amazon Redshift ML or Vellum?
Amazon Redshift ML starts at Free and Vellum at Free.
Does Amazon Redshift ML or Vellum run on more platforms?
Amazon Redshift ML runs on Web. Vellum runs on Web, macOS, iOS, Slack, Telegram, Terminal.
Can I use Amazon Redshift ML for free?
Both have a free tier, so you can try either at no cost before committing.
What is Amazon Redshift ML best used for?
Amazon Redshift ML is most often used for adding a churn or propensity score to an existing dashboard where the data is already in redshift and nobody needs a bespoke model, letting an analytics team test whether a predictive column has any business value before asking for data science headcount, scoring rows inside a sql pipeline where moving data out to a separate service would add fragility for little benefit, organisations committed to aws whose main constraint is a data science backlog rather than modelling sophistication. Of those, adding a churn or propensity score to an existing dashboard where the data is already in redshift and nobody needs a bespoke model and letting an analytics team test whether a predictive column has any business value before asking for data science headcount are not what Vellum is typically brought in for.
What can Amazon Redshift ML do that Vellum cannot?
Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model. Vellum covers Persistent AI identity, Long-term memory, Email automation, Calendar management.

Answered from the vendors’ own pages

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.

Vellum: How does Vellum protect my data privacy?

Vellum uses permission-based controls requiring explicit approval for sensitive actions. Data is encrypted and the option for self-hosting provides complete data control. Open-source deployment option allows hosting on your own infrastructure.

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.

Vellum: Can I use Vellum for business team collaboration?

Vellum is primarily designed as a personal assistant with persistent identity. For team collaboration, platforms like Lindy offer better multi-user features with shared credit pools and Slack workspace integration.

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.

Vellum: What does the free tier include?

Vellum offers free starter credits to begin using the platform. Beyond that, it operates on a pay-as-you-go credit model with paid tiers starting at $30/month for increased computational resources and bundled credits.

Source
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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