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AI · head to head

Avathon vs Amazon Redshift ML

Avathon logo

Avathon

AI

Industrial AI for asset performance and renewables, formerly SparkCognition

From
On request
Free plan
No
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

  • Only Amazon Redshift ML has a free tier, so it costs nothing to try first.
  • Each has a real cost: Avathon the company rebranded from SparkCognition to Avathon and narrowed its focus after repeated reported layoff rounds, so reference checks and roadmap commitments made under the old name need reconfirming.; 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: Avathon covers Renewable Suite, 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 Avathon and Amazon Redshift ML actually diverge.

Attributes where Avathon and Amazon Redshift ML differ
AttributeAvathonAmazon Redshift ML
Starting priceOn requestFree
Pricing modelquoteusage-based
Free tierNoYes
PlatformsWeb, CloudWeb
CategoryAIMachine 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 Avathon

  • Renewable Suite
  • Loss attribution
  • Predictive maintenance models
  • Computer vision
  • Digital twin models
  • Work order integration
  • Fleet benchmarking
  • Document intelligence

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.

Avathon

  • A wind fleet owner wanting underperforming turbines ranked by lost megawatt hours rather than by alarm countnot Amazon Redshift ML
  • A solar independent power producer detecting inverter and string underperformance that falls below the SCADA alarm thresholdnot Amazon Redshift ML
  • A manufacturer with years of historian data and failure records who wants failure prediction models trained on their own historynot Amazon Redshift ML
  • A site using cameras to detect missing personal protective equipment or unauthorised entry into restricted zonesnot 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 Avathon
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Avathon
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Avathon
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Avathon

Where each one falls short

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

Avathon

  • The company rebranded from SparkCognition to Avathon and narrowed its focus after repeated reported layoff rounds, so reference checks and roadmap commitments made under the old name need reconfirming.
  • Headcount is in the low hundreds against a product portfolio spanning renewables, vision, predictive maintenance and document AI, which is thin coverage per product line for enterprise support expectations.
  • Model accuracy depends on having enough labelled historical failures, and sites with short data history or poor maintenance record-keeping get generic anomaly detection rather than useful failure prediction.
  • Nothing is published about pricing or asset-count thresholds, so there is no way to judge whether a small fleet is even a viable customer without entering a sales cycle.
  • It sits on top of your SCADA and CMMS rather than replacing them, so the integration and asset-hierarchy mapping work is a real project cost that the software subscription does not cover.

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

Avathon

On request
  • Avathon$undefined/year
    • Annual subscription sized by asset count or installed megawatts for renewables
    • Separate pricing for vision, predictive maintenance and document products
    • Model development and data onboarding quoted as a professional services project

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 Avathon if

  • You need renewable suite.
  • You work on Web, Cloud.
  • You also want loss attribution.
Avathon 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 Avathon or Amazon Redshift ML better?
Neither clearly leads. Avathon 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, Avathon or Amazon Redshift ML?
Amazon Redshift ML has a free tier; the other does not. Paid plans start at On request for Avathon and Free for Amazon Redshift ML.
Does Avathon or Amazon Redshift ML run on more platforms?
Avathon 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. Avathon starts at On request.
What is Avathon best used for?
Avathon is most often used for a wind fleet owner wanting underperforming turbines ranked by lost megawatt hours rather than by alarm count, a solar independent power producer detecting inverter and string underperformance that falls below the scada alarm threshold, a manufacturer with years of historian data and failure records who wants failure prediction models trained on their own history, a site using cameras to detect missing personal protective equipment or unauthorised entry into restricted zones. Of those, a wind fleet owner wanting underperforming turbines ranked by lost megawatt hours rather than by alarm count and a solar independent power producer detecting inverter and string underperformance that falls below the scada alarm threshold are not what Amazon Redshift ML is typically brought in for.
What can Avathon do that Amazon Redshift ML cannot?
Avathon covers Renewable Suite, Loss attribution, Predictive maintenance models, Computer vision. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Avathon: Is this the same company as SparkCognition?

Yes. SparkCognition rebranded to Avathon and refocused on industrial and government asset AI.

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.

Avathon: Which product is most mature?

The renewable energy suite for wind and solar underperformance, which has the longest production deployment history.

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.

Avathon: Does it need my historian data in the cloud?

Generally yes for model training and ongoing scoring, which is a security review item for utility and defence customers.

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.

Avathon: Can it work without historical failure records?

It will still do anomaly detection, but failure prediction models need labelled historical failures to be worth the deployment cost.

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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