Softwr

AI · head to head

Gumloop vs Amazon Redshift ML

Gumloop logo

Gumloop

AI

AI infrastructure platform for building and deploying autonomous agents

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: Gumloop pro plan at $37/month may be limiting for enterprises considering spending caps; 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: Gumloop covers No-code agent builder, 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 Gumloop and Amazon Redshift ML actually diverge.

Attributes where Gumloop and Amazon Redshift ML differ
AttributeGumloopAmazon Redshift ML
Pricing modelTiered subscription with usage creditsusage-based
PlatformsWeb, Slack, Microsoft Teams, GmailWeb
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 Gumloop

  • No-code agent builder
  • 300+ app integrations
  • Company Brain
  • Self-improving agents
  • Multi-channel deployment
  • Role-based access
  • Budget controls
  • Audit logging

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.

Gumloop

  • CRM management and sales pipeline analysisnot Amazon Redshift ML
  • Lead qualification and sales outreach automationnot Amazon Redshift ML
  • Meeting preparation and call analysisnot Amazon Redshift ML
  • Data analysis and automated reportingnot Amazon Redshift ML
  • Content creation and knowledge base updatesnot 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 Gumloop
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Gumloop
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Gumloop
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Gumloop

Where each one falls short

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

Gumloop

  • Pro plan at $37/month may be limiting for enterprises considering spending caps
  • Requires enterprise plan for advanced security controls needed by large organizations
  • No permanent free tier beyond 14-day trial
  • Company Brain integration depends on having all tools connected

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

Gumloop

Free
  • Free TrialFree
    • 14-day free trial of Pro plan
  • Enterprise$undefined/custom
    • Custom credit allocation
    • 35+ models plus custom proxy
    • Org-wide security controls

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 Gumloop if

  • You need no-code agent builder.
  • You want to start without paying.
  • You work on Web, Slack, Microsoft Teams, Gmail.
  • You also want 300+ app integrations.
Gumloop 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 Gumloop or Amazon Redshift ML better?
Neither clearly leads. Gumloop 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, Gumloop or Amazon Redshift ML?
Gumloop starts at Free and Amazon Redshift ML at Free.
Does Gumloop or Amazon Redshift ML run on more platforms?
Gumloop runs on Web, Slack, Microsoft Teams, Gmail. Amazon Redshift ML runs on Web.
Can I use Gumloop for free?
Both have a free tier, so you can try either at no cost before committing.
What is Gumloop best used for?
Gumloop is most often used for crm management and sales pipeline analysis, lead qualification and sales outreach automation, meeting preparation and call analysis, data analysis and automated reporting. Of those, crm management and sales pipeline analysis and lead qualification and sales outreach automation are not what Amazon Redshift ML is typically brought in for.
What can Gumloop do that Amazon Redshift ML cannot?
Gumloop covers No-code agent builder, 300+ app integrations, Company Brain, Self-improving agents. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Gumloop: Can non-technical users build agents with Gumloop?

Yes, Gumloop is designed for domain experts without programming skills. The no-code interface enables anyone who understands a task to automate it without coding.

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.

Gumloop: How many applications can Gumloop integrate with?

Gumloop integrates with 300+ business tools including Salesforce, HubSpot, Slack, Gmail, GitHub, Jira, and others. The Company Brain feature unifies data from all connected applications.

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.

Gumloop: What security features does Enterprise include?

Enterprise plans offer SCIM and SAML support, SOC 2 Type II compliance, custom MCP server hosting, advanced admin features, audit logging, and VPC deployments for data isolation.

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.

Share

Related pages

More on Gumloop

More on Amazon Redshift ML

Keep looking

Other head to heads