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Machine Learning · head to head

Pachyderm vs Amazon Redshift ML

P

Pachyderm

Machine Learning

Data versioning and container pipelines that run on your Kubernetes cluster

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: Pachyderm it runs only on Kubernetes, so operating it means someone who can debug pods, storage classes and node pressure, and on a team without that person a cluster problem and an ML outage are the same event.; 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: Pachyderm covers Versioned file system, 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 Pachyderm and Amazon Redshift ML actually diverge.

Attributes where Pachyderm and Amazon Redshift ML differ
AttributePachydermAmazon Redshift ML
Pricing modelfreemiumusage-based
PlatformsLinuxWeb
Founded20142006

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 Pachyderm

  • Versioned file system
  • Datum-based incremental processing
  • Container pipelines
  • Automatic provenance
  • Parallel execution
  • S3 gateway
  • Enterprise authentication
  • Object storage backends

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.

Pachyderm

  • Reprocessing a growing archive of images or documents where a full pass every night would be wasteful and only the new files matternot Amazon Redshift ML
  • Regulated pipelines where an auditor will ask which exact input files and which code version produced a given resultnot Amazon Redshift ML
  • Genomics and scientific workflows built from existing command line tools that are easier to containerise than to rewritenot Amazon Redshift ML
  • Teams that already run Kubernetes and want data lineage without adopting a full commercial ML platformnot 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 Pachyderm
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Pachyderm
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Pachyderm
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Pachyderm

Where each one falls short

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

Pachyderm

  • It runs only on Kubernetes, so operating it means someone who can debug pods, storage classes and node pressure, and on a team without that person a cluster problem and an ML outage are the same event.
  • Data is held in Pachyderm's content-addressed repositories rather than as plain files in a bucket, so every other tool reaches it through the client or the S3 gateway and migrating away is a full export rather than a redirect.
  • The glob pattern that decides the unit of parallel work is the most consequential line in a pipeline specification, and getting it wrong produces either one enormous serial job or millions of tiny ones whose container start-up dominates the runtime.
  • Compute is billed by your cloud provider, not by Pachyderm, so a platform that looks inexpensive on the licence line runs on a cluster that has to be sized for peak pipeline load and, for training work, carries GPU nodes.
  • The project's direction now sits inside a large hardware vendor's portfolio following the 2023 acquisition, and a team adopting the community edition has no contractual claim on its continued development.

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

Pachyderm

Free
  • CommunityFree
    • Core features
    • Community support
  • EnterpriseFree
    • Advanced security
    • Premium support
    • SLAs

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 Pachyderm if

  • You need versioned file system.
  • You want to start without paying.
  • You work on Linux.
  • You also want datum-based incremental processing.
Pachyderm 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 Pachyderm or Amazon Redshift ML better?
Neither clearly leads. Pachyderm 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, Pachyderm or Amazon Redshift ML?
Pachyderm starts at Free and Amazon Redshift ML at Free.
Does Pachyderm or Amazon Redshift ML run on more platforms?
Pachyderm runs on Linux. Amazon Redshift ML runs on Web.
Can I use Pachyderm for free?
Both have a free tier, so you can try either at no cost before committing.
What is Pachyderm best used for?
Pachyderm is most often used for reprocessing a growing archive of images or documents where a full pass every night would be wasteful and only the new files matter, regulated pipelines where an auditor will ask which exact input files and which code version produced a given result, genomics and scientific workflows built from existing command line tools that are easier to containerise than to rewrite, teams that already run kubernetes and want data lineage without adopting a full commercial ml platform. Of those, reprocessing a growing archive of images or documents where a full pass every night would be wasteful and only the new files matter and regulated pipelines where an auditor will ask which exact input files and which code version produced a given result are not what Amazon Redshift ML is typically brought in for.
What can Pachyderm do that Amazon Redshift ML cannot?
Pachyderm covers Versioned file system, Datum-based incremental processing, Container pipelines, Automatic provenance. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Pachyderm: Is Pachyderm open source?

The community edition is, under Apache 2.0. Authentication, role-based access control, the console and multi-tenancy sit behind an enterprise licence key, which is the set of features most organisations need once more than one team uses it.

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.

Pachyderm: Do I need Kubernetes to run it?

Yes. There is no non-Kubernetes deployment. A local single-node install exists for evaluation, but anything real is a cluster with object storage behind it.

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.

Pachyderm: How is it different from DVC?

DVC is a command line tool a person runs alongside Git, with no server. Pachyderm is a server that owns the data and schedules the work centrally. DVC records what you did; Pachyderm does it and records it.

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.

Pachyderm: What does it actually cost to run?

The licence is separate from the infrastructure. You pay your cloud provider for the Kubernetes nodes that run every pipeline pod and for the object storage holding every version of every data set, and that bill grows with history as well as with 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.

Pachyderm: Can I serve models with it?

No. It is a batch data and training pipeline system. Serving is a separate tool and a separate deployment.

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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