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Databases · head to head

Knack vs Amazon Redshift ML

Knack logo

Knack

Databases

Hosted no-code database and web app builder priced by stored records and storage, with unlimited user accounts on every plan.

From
$59/month
Free plan
No
Visit KnackFull review
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

The short version

  • Amazon Redshift ML has a free plan, then $0.25/mo; Knack starts at $59/mo.
  • Only Knack lists visual database builder; only Amazon Redshift ML lists CREATE MODEL in SQL.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

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

Attributes where Knack and Amazon Redshift ML differ
AttributeKnackAmazon Redshift ML
Starting price$59/monthFree
Pricing modelSubscriptionUsage-based
Free tierNoYes
CategoryDatabasesMachine Learning

Identical on both: platforms (Web).

Not yet recorded: founded for Knack.

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 Knack

  • Visual database builder
  • User logins and roles
  • Page builder
  • Unlimited users
  • E-commerce
  • Automation flows
  • Scheduled tasks
  • REST API and webhooks

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.

Knack

  • A member or client portal where thousands of external people need their own login and see only their own recordsnot Amazon Redshift ML
  • Replacing a shared spreadsheet used by a team that has outgrown it but cannot justify a developer to build a bespoke internal toolnot Amazon Redshift ML
  • Inventory, asset or case tracking for a small operations team that needs roles, forms and a record of who changed whatnot Amazon Redshift ML
  • A registration or booking application for an association, school or non-profit where the user count is large and the record count stays modestnot 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 Knack
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Knack
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Knack
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Knack

Where each one falls short

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

Knack

  • Plans are capped by stored records, so an application that logs events or keeps history rather than current state climbs tiers on data volume alone, and archiving old records becomes a permanent maintenance chore rather than a one-off
  • API access is capped per day, at 1,000 calls on the entry plan and 10,000 at the Corporate tier, so a scheduled sync or a busy automation can exhaust the day's quota before the working day ends and integrations then fail silently
  • The application itself cannot be exported or self-hosted; records can be downloaded but the pages, roles, flows and business logic exist only inside Knack, so outgrowing the platform means rebuilding the app rather than moving it
  • Customisation beyond the builder is limited to injected CSS and JavaScript that Knack's support does not cover, so any non-standard interface behaviour becomes yours to maintain against a DOM the vendor can change without warning
  • Compliance capabilities that regulated buyers assume are baseline, HIPAA and GovCloud options, advanced SSO, IP restrictions and private environments, are gated to the Corporate and Enterprise tiers, so a small regulated pilot cannot legitimately start on an entry plan

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

Knack

$59/month
  • Starter$59/mo
    • 20,000 database records
    • Unlimited users, tables, fields
    • 25 AI credits
  • Pro$130/mo
    • 50,000 database records
    • Remove Knack branding
    • E-commerce payments
  • Corporate$300/mo
    • 125,000-2.5M database records
    • App restore functionality
    • Advanced SSO
  • EnterpriseOn request
    • Dedicated account manager
    • HIPAA/GovCloud-based options
    • Custom pricing

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 Knack if

  • You need visual database builder.
  • You also want user logins and roles.
Knack 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 Knack or Amazon Redshift ML better?
Neither clearly leads. Knack starts at $59/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, Knack or Amazon Redshift ML?
Amazon Redshift ML has a free tier; the other does not. Paid plans start at $59/month for Knack and Free for Amazon Redshift ML.
Does Knack 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 Amazon Redshift ML for free?
Yes. Amazon Redshift ML has a free tier, so you can try it without paying. Knack starts at $59/month.
What is Knack best used for?
Knack is most often used for a member or client portal where thousands of external people need their own login and see only their own records, replacing a shared spreadsheet used by a team that has outgrown it but cannot justify a developer to build a bespoke internal tool, inventory, asset or case tracking for a small operations team that needs roles, forms and a record of who changed what, a registration or booking application for an association, school or non-profit where the user count is large and the record count stays modest. Of those, a member or client portal where thousands of external people need their own login and see only their own records and replacing a shared spreadsheet used by a team that has outgrown it but cannot justify a developer to build a bespoke internal tool are not what Amazon Redshift ML is typically brought in for.
What can Knack do that Amazon Redshift ML cannot?
Knack covers Visual database builder, User logins and roles, Page builder, Unlimited users. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Knack: Is Knack charged per user?

No. User accounts are unlimited on all plans. Plans are bounded by stored record count, file storage, API calls per day and automation transactions.

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.

Knack: What happens when I exceed the record limit?

You move to a higher tier or reduce stored records. Because the limit is on data rather than people, apps that accumulate history hit it while apps that track a fixed set of entities generally do not.

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.

Knack: Can I export my application or self-host it?

You can export record data. The application itself, its pages, roles, permissions and flows, is not exportable and cannot be self-hosted, so leaving means rebuilding elsewhere.

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.

Knack: Is it suitable for public-facing portals?

That is its strongest case, because logins are unlimited and role-based permissions are built in. The constraint is record volume and API throughput, not audience size.

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.

Knack: Can I use it for regulated data?

HIPAA-aligned and government cloud options exist but only on the Enterprise tier, along with private server environments. Lower tiers should not be used for regulated workloads.

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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