Softwr

Machine Learning · head to head

Neptune.ai vs Amazon Redshift ML

Neptune.ai logo

Neptune.ai

Machine Learning

Metadata store for MLOps

From
Free
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

  • Each has a real cost: Neptune.ai free tier limited to 100 hours per month, exhausted quickly with serious ML work; 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: Neptune.ai covers Experiment tracking, 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 Neptune.ai and Amazon Redshift ML actually diverge.

Attributes where Neptune.ai and Amazon Redshift ML differ
AttributeNeptune.aiAmazon Redshift ML
Pricing modelUnknownusage-based
PlatformsWeb, Self-hostedWeb
Founded20172006

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning).

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 Neptune.ai

  • Experiment tracking
  • Model registry
  • Metadata logging
  • Comparison views
  • Custom dashboards
  • PyTorch
  • TensorFlow
  • Keras

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.

Neptune.ai

  • Machine learningnot Amazon Redshift ML
  • Data analysisnot Amazon Redshift ML
  • Model trainingnot Amazon Redshift ML
  • Predictive analyticsnot 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 Neptune.ai
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Neptune.ai
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Neptune.ai
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Neptune.ai

Where each one falls short

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

Neptune.ai

  • Free tier limited to 100 hours per month, exhausted quickly with serious ML work
  • Lacks hyperparameter sweeps compared to Weights and Biases
  • No pipeline orchestration or broader MLOps lifecycle management
  • Dashboard visualization limitations - automatic resizing affects visualization order and size
  • Cloud-based SaaS only (as of last available service) requires internet connectivity

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

Neptune.ai

Free

No published plan breakdown. See the Neptune.ai review.

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 Neptune.ai if

  • You need experiment tracking.
  • You want to start without paying.
  • You work on Web, Self-hosted.
  • You also want model registry.
Neptune.ai 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 Neptune.ai or Amazon Redshift ML better?
Neither clearly leads. Neptune.ai 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, Neptune.ai or Amazon Redshift ML?
Neptune.ai starts at Free and Amazon Redshift ML at Free.
Does Neptune.ai or Amazon Redshift ML run on more platforms?
Neptune.ai runs on Web, Self-hosted. Amazon Redshift ML runs on Web.
Can I use Neptune.ai for free?
Both have a free tier, so you can try either at no cost before committing.
What is Neptune.ai best used for?
Neptune.ai is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Amazon Redshift ML is typically brought in for.
What can Neptune.ai do that Amazon Redshift ML cannot?
Neptune.ai covers Experiment tracking, Model registry, Metadata logging, Comparison views. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Neptune.ai: Does Neptune.ai support self-hosting?

Yes. Neptune can be self-hosted on a Kubernetes cluster with ClickHouse, MySQL, and Redis dependencies, allowing organizations to maintain full data control.

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.

Neptune.ai: What machine learning frameworks does Neptune integrate with?

Neptune integrates with PyTorch, TensorFlow, Keras, scikit-learn, XGBoost, LightGBM, Hugging Face Transformers, and Optuna for hyperparameter optimization.

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.

Neptune.ai: What is the cost for a team of 10 data scientists?

Neptune's Team plan costs $49 per user per month, resulting in $490/month for 10 users, comparable to Weights and Biases at $50/user.

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.

Neptune.ai: When is Neptune.ai shutting down?

Neptune.ai is shutting down its external SaaS service on March 5, 2026, following its acquisition by OpenAI in December 2025. Customers must export and migrate data before that date.

Source
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 Neptune.ai

More on Amazon Redshift ML

Keep looking

Other head to heads