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Machine Learning · head to head

Databricks vs Pachyderm

Databricks logo

Databricks

Machine Learning

Unified analytics platform for data engineering and data science

From
Free
Rated
-
Full reviewWebsite
P

Pachyderm

Machine Learning

Data versioning and container pipelines that run on your Kubernetes cluster

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Databricks cloud compute is billed separately by the cloud provider on top of Databricks DBU charges; 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.
  • They diverge on capability: Databricks covers Delta Lake, Pachyderm covers Versioned file system.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Databricks and Pachyderm actually diverge.

Attributes where Databricks and Pachyderm differ
AttributeDatabricksPachyderm
Pricing modelusage-basedfreemium
PlatformsWeb, Aws, Azure, GcpLinux
Founded20132014

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 Databricks

  • Delta Lake
  • Apache Spark
  • MLflow
  • Unity Catalog
  • Photon Engine
  • Collaborative Notebooks
  • Auto-scaling
  • AWS

Only in Pachyderm

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

What people use each for

The jobs each tool is most often brought in to do.

Databricks

  • Running Spark data engineering pipelines on managed clustersnot Pachyderm
  • Building a lakehouse over data in cloud object storagenot Pachyderm
  • Training and serving machine learning models alongside the datanot Pachyderm

Pachyderm

  • Reprocessing a growing archive of images or documents where a full pass every night would be wasteful and only the new files matternot Databricks
  • Regulated pipelines where an auditor will ask which exact input files and which code version produced a given resultnot Databricks
  • Genomics and scientific workflows built from existing command line tools that are easier to containerise than to rewritenot Databricks
  • Teams that already run Kubernetes and want data lineage without adopting a full commercial ML platformnot Databricks

Where each one falls short

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

Databricks

  • Cloud compute is billed separately by the cloud provider on top of Databricks DBU charges
  • The free trial lasts 14 days
  • Discounts require a Committed Use Contract, with larger commitments needed for larger discounts
  • Azure Databricks pricing is set by Microsoft rather than by Databricks
  • Security and compliance capabilities are sold as separate platform add ons rather than included in the base rate

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.

Pricing, plan by plan

Databricks

Free
  • Community EditionFree
    • Limited cluster
    • Notebook environment
    • Community support
  • Standard$0.07/DBU
    • Jobs compute
    • SQL compute
    • Standard support

Pachyderm

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

Which should you pick?

Choose Databricks if

  • You need delta lake.
  • You want to start without paying.
  • You work on Web, Aws, Azure, Gcp.
  • You also want apache spark.
Databricks review

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

Questions people ask

Is Databricks or Pachyderm better?
Neither clearly leads. Databricks starts at Free and Pachyderm at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Databricks or Pachyderm?
Databricks starts at Free and Pachyderm at Free.
Does Databricks or Pachyderm run on more platforms?
Databricks runs on Web, Aws, Azure, Gcp. Pachyderm runs on Linux.
Can I use Databricks for free?
Both have a free tier, so you can try either at no cost before committing.
What is Databricks best used for?
Databricks is most often used for running spark data engineering pipelines on managed clusters, building a lakehouse over data in cloud object storage, training and serving machine learning models alongside the data. Of those, running spark data engineering pipelines on managed clusters and building a lakehouse over data in cloud object storage are not what Pachyderm is typically brought in for.
What can Databricks do that Pachyderm cannot?
Databricks covers Delta Lake, Apache Spark, MLflow, Unity Catalog. Pachyderm covers Versioned file system, Datum-based incremental processing, Container pipelines, Automatic provenance.

Answered from the vendors’ own pages

Databricks: How is Databricks priced?

Databricks bills pay as you go with no up front cost, charging per second for the products used. Consumption is measured in Databricks Units, a normalised unit of processing power on the platform.

Source
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.

Databricks: Does Databricks publish a per DBU price?

Not on its main pricing page. Rates vary by product and instance type, and Databricks directs buyers to individual product pricing pages and a calculator rather than listing a single figure.

Source
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.

Databricks: Does the Databricks price include cloud costs?

No. Databricks states that if you configure it to work with your own cloud account, your cloud provider still charges you separately for the underlying resources.

Source
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.

Databricks: Can I get a discount on Databricks?

Databricks offers Committed Use Contracts, where larger usage commitments earn greater benefits, including options to use commitments flexibly across multiple clouds.

Source
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.

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.

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