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Energy · head to head

Novity vs Amazon Redshift ML

Novity logo

Novity

Energy

Hybrid physics and machine learning prognostics that estimate remaining useful life for process equipment

From
On request
Rated
-
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
Rated
-
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: Novity novity is a small venture-backed company with a strategic investor rather than a profitable business, so continuity risk is real and the Tokyo Gas investment signals a likely eventual acquisition that would reset the roadmap.; 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: Novity covers TruPrognostics engine, Amazon Redshift ML covers CREATE MODEL in SQL.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

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

Attributes where Novity and Amazon Redshift ML differ
AttributeNovityAmazon Redshift ML
Starting priceOn requestFree
Pricing modelquoteusage-based
Free tierNoYes
PlatformsWeb, CloudWeb
CategoryEnergyMachine Learning
FoundedUnknown2006

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 Novity

  • TruPrognostics engine
  • Cold-start modelling
  • Fault mode diagnosis
  • Remaining useful life
  • Existing sensor reuse
  • Recommended actions
  • Historian connectors
  • Asset class libraries

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.

Novity

  • A gas processing plant that needs a defensible time-to-failure number before deferring a turnaroundnot Amazon Redshift ML
  • An LNG terminal with critical compressors and no run-to-failure history to train a conventional modelnot Amazon Redshift ML
  • A wastewater operator whose existing vibration alarms are ignored because they carry no severity or horizonnot Amazon Redshift ML
  • A generator operator supplying data centre load where an unplanned trip carries contractual penaltiesnot 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 Novity
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Novity
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Novity
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Novity

Where each one falls short

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

Novity

  • Novity is a small venture-backed company with a strategic investor rather than a profitable business, so continuity risk is real and the Tokyo Gas investment signals a likely eventual acquisition that would reset the roadmap.
  • Physics-based models must be configured per equipment class, so each new asset type is an engineering engagement rather than a configuration screen, and rollout speed is limited by Novitys own capacity.
  • Prognostics depend on the quality and sampling rate of your historian data; plants recording ten-minute averages will not get useful remaining-useful-life estimates without new instrumentation.
  • Nothing about pricing is published and there is no self-service entry point, so evaluation always starts with a sales-led pilot on a handful of assets.
  • The deployment footprint is concentrated in oil and gas, LNG and water, so reference customers and pre-built asset models outside those industries are limited.

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

Novity

On request
  • TruPrognostics$undefined/year
    • Quoted per asset class and monitored equipment count
    • Model configuration and commissioning quoted as a project
    • Typically an annual subscription tied to a pilot then a rollout

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 Novity if

  • You need truprognostics engine.
  • You work on Web, Cloud.
  • You also want cold-start modelling.
Novity 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 Novity or Amazon Redshift ML better?
Neither clearly leads. Novity starts at On request and Amazon Redshift ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Novity or Amazon Redshift ML?
Amazon Redshift ML has a free tier; the other does not. Paid plans start at On request for Novity and Free for Amazon Redshift ML.
Does Novity or Amazon Redshift ML run on more platforms?
Novity runs on Web, Cloud. 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. Novity starts at On request.
What is Novity best used for?
Novity is most often used for a gas processing plant that needs a defensible time-to-failure number before deferring a turnaround, an lng terminal with critical compressors and no run-to-failure history to train a conventional model, a wastewater operator whose existing vibration alarms are ignored because they carry no severity or horizon, a generator operator supplying data centre load where an unplanned trip carries contractual penalties. Of those, a gas processing plant that needs a defensible time-to-failure number before deferring a turnaround and an lng terminal with critical compressors and no run-to-failure history to train a conventional model are not what Amazon Redshift ML is typically brought in for.
What can Novity do that Amazon Redshift ML cannot?
Novity covers TruPrognostics engine, Cold-start modelling, Fault mode diagnosis, Remaining useful life. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Novity: What does Novity actually output?

A named failure mode and an estimated remaining useful life with a confidence band, not just an anomaly alert.

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.

Novity: Do we need failure history to train it?

No. The physics component is what lets it produce useful prognostics on equipment with little or no run-to-failure data.

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.

Novity: Do we need new sensors?

Often not. It reads from your existing historian, but low sampling rates or missing measurements can require additional instrumentation.

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.

Novity: Who backs the company?

It was spun out of Xerox PARC and took a strategic investment from Acario Innovation, the venture arm of Tokyo Gas, in 2026.

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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