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AI · head to head

Lindy vs Amazon Redshift ML

Lindy logo

Lindy

AI

AI teammate that automates work across your entire software stack

From
Free
Free plan
Yes
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

  • Each has a real cost: Lindy requires per-user subscription, which scales costs with team size; 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: Lindy covers Slack integration, 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 Lindy and Amazon Redshift ML actually diverge.

Attributes where Lindy and Amazon Redshift ML differ
AttributeLindyAmazon Redshift ML
Pricing modelPer-user subscription with shared credit poolusage-based
PlatformsWeb, Slack, Email, MobileWeb
CategoryAIMachine Learning
FoundedUnknown2006

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

  • Slack integration
  • Email automation
  • Meeting transcription
  • Scheduled routines
  • 1,000+ app integrations
  • Skill library
  • Editable memory
  • MCP 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.

Lindy

  • Automating email and calendar management across teamsnot Amazon Redshift ML
  • Transcribing and summarizing meetings automaticallynot Amazon Redshift ML
  • Updating CRM systems with meeting notes and leadsnot Amazon Redshift ML
  • Generating daily reports and weekly briefsnot Amazon Redshift ML
  • Processing structured data from multiple applicationsnot 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 Lindy
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Lindy
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Lindy
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Lindy

Where each one falls short

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

Lindy

  • Requires per-user subscription, which scales costs with team size
  • Limited free trial period of 7 days may not allow full evaluation
  • Credit system complexity could be confusing for new users
  • Not designed for technical teams as primary tool

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

Lindy

Free
  • Free TrialFree
    • 7-day free trial for new team members joining via Slack
  • Plus$29.99/month
    • 3,000 credits per month
    • Slack integration
    • Email automation
  • Pro$99.99/month
    • 15,000 credits per month
    • All Plus features
    • Advanced automation
  • Max$199.99/month
    • 35,000 credits per month
    • All Pro features
    • Priority support

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 Lindy if

  • You need slack integration.
  • You want to start without paying.
  • You work on Web, Slack, Email, Mobile.
  • You also want email automation.
Lindy 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 Lindy or Amazon Redshift ML better?
Neither clearly leads. Lindy 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, Lindy or Amazon Redshift ML?
Lindy starts at Free and Amazon Redshift ML at Free.
Does Lindy or Amazon Redshift ML run on more platforms?
Lindy runs on Web, Slack, Email, Mobile. Amazon Redshift ML runs on Web.
Can I use Lindy for free?
Both have a free tier, so you can try either at no cost before committing.
What is Lindy best used for?
Lindy is most often used for automating email and calendar management across teams, transcribing and summarizing meetings automatically, updating crm systems with meeting notes and leads, generating daily reports and weekly briefs. Of those, automating email and calendar management across teams and transcribing and summarizing meetings automatically are not what Amazon Redshift ML is typically brought in for.
What can Lindy do that Amazon Redshift ML cannot?
Lindy covers Slack integration, Email automation, Meeting transcription, Scheduled routines. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Lindy: How does Lindy maintain data privacy and security?

Lindy is SOC 2 Type II, GDPR, HIPAA, and PIPEDA compliant. Data is encrypted in transit and at rest. The platform never sells your data or uses it to train models. Enterprise plans include signed BAAs and audit logs.

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.

Lindy: Can we start with a free trial?

New team members joining through Slack receive a 7-day free trial before being billed. Direct signups are charged immediately. There is no permanent free tier after the trial.

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.

Lindy: How many applications can Lindy integrate with?

Lindy connects to over 1,000 applications including Gmail, Notion, HubSpot, GitHub, Stripe, and others, with support for Model Context Protocol servers for custom integrations.

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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