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Machine Learning · head to head

Pachyderm vs Teradata

P

Pachyderm

Machine Learning

Data versioning and container pipelines that run on your Kubernetes cluster

From
Free
Free plan
Yes
Visit PachydermFull review
Teradata logo

Teradata

Databases

Long-established enterprise MPP data warehouse, rebranded in 2026 as the Autonomous Knowledge Platform, sold for cloud, on-premises and hybrid.

From
On request
Free plan
No
Visit TeradataFull review

The short version

  • Pachyderm is free; Teradata prices on request.
  • Only Pachyderm lists versioned file system; only Teradata lists workload management.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

Only the attributes on which Pachyderm and Teradata actually diverge.

Attributes where Pachyderm and Teradata differ
AttributePachydermTeradata
Starting priceFreeOn request
Pricing modelFreemiumQuote
Free tierYesNo
PlatformsLinuxWeb
CategoryMachine LearningDatabases

Not yet recorded: founded for Teradata.

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 Teradata

  • Massively parallel architecture
  • Workload management
  • Mature cost-based optimiser
  • Cloud, on-premises and hybrid
  • Bulk load utilities
  • BTEQ scripting
  • In-database analytics
  • Enterprise Vector Store

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 Teradata
  • Regulated pipelines where an auditor will ask which exact input files and which code version produced a given resultnot Teradata
  • Genomics and scientific workflows built from existing command line tools that are easier to containerise than to rewritenot Teradata
  • Teams that already run Kubernetes and want data lineage without adopting a full commercial ML platformnot Teradata

Teradata

  • A large existing Teradata estate where the practical question is which workloads to migrate first rather than whether to adoptnot Pachyderm
  • High-concurrency mixed workloads where hundreds of analysts and scheduled jobs contend and predictable prioritisation matters more than peak single-query speednot Pachyderm
  • Regulated reporting where the same query must produce the same answer for years and the audit trail of the existing implementation has valuenot Pachyderm
  • Hybrid deployments where regulatory or data-residency rules keep a portion of the warehouse on-premises while the rest moves to cloudnot 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

Teradata

  • Licensing is negotiated rather than published, so there is no way to compare total cost against a consumption-priced warehouse without entering a sales cycle, and the comparison is only ever as good as the workload profile you gave them
  • The SQL dialect and the loading utilities are Teradata-specific, so every stored procedure, macro and BTEQ script written against the platform is migration debt that grows with each release you ship
  • Primary index choice determines data distribution, and a poorly chosen index concentrates rows on a few processing units, which surfaces as one slow query rather than an error and needs a specialist to diagnose
  • The skills market is contracting, so DBA and workload-management expertise is expensive to hire, hard to replace when someone retires, and increasingly hard to buy from consultancies whose own bench has moved to cloud warehouses
  • The 2026 renaming of Vantage, VantageCloud, ClearScape and QueryGrid split documentation, runbooks and vendor material across two naming systems, so searching for an error or a configuration now returns results for a product that is described under a different name

Pricing, plan by plan

Pachyderm

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

Teradata

On request

No published plan breakdown. See the Teradata review.

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 Teradata if

  • You need massively parallel architecture.
  • You also want workload management.
Teradata review

Questions people ask

Is Pachyderm or Teradata better?
Neither clearly leads. Pachyderm starts at Free and Teradata at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Pachyderm or Teradata?
Pachyderm has a free tier; the other does not. Paid plans start at Free for Pachyderm and On request for Teradata.
Does Pachyderm or Teradata run on more platforms?
Pachyderm runs on Linux. Teradata runs on Web.
Can I use Pachyderm for free?
Yes. Pachyderm has a free tier, so you can try it without paying. Teradata starts at On request.
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 Teradata is typically brought in for.
What can Pachyderm do that Teradata cannot?
Pachyderm covers Versioned file system, Datum-based incremental processing, Container pipelines, Automatic provenance. Teradata covers Massively parallel architecture, Workload management, Mature cost-based optimiser, Cloud, on-premises and hybrid.

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.

Teradata: Is Teradata only on-premises?

No. It is sold for cloud, on-premises and hybrid deployment, and the cloud offering is now branded Teradata Cloud. A large part of the installed base is still on-premises or hybrid.

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.

Teradata: How does it compare to Snowflake or BigQuery?

On raw elasticity and cost transparency the cloud warehouses win. On mixed-workload concurrency management against a large existing query estate Teradata is still hard to replace, which is why migrations off it take years rather than quarters.

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.

Teradata: Why do organisations stay on it?

Because the cost of leaving is the estate, not the data. Thousands of procedures, scripts and extracts written in a proprietary dialect have to be rewritten and revalidated, and in regulated reporting that revalidation is the expensive part.

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.

Teradata: What changed in the 2026 rebrand?

Vantage became the Autonomous Knowledge Platform, VantageCloud became Teradata Cloud, ClearScape Analytics became AI Studio and QueryGrid became Fabric. The underlying products are continuous with what came before.

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.

Teradata: Can it handle AI and vector workloads?

It has added an Enterprise Vector Store and in-database analytics branded AI Studio. Whether that is preferable to moving the data into a purpose-built vector store depends on how much of your data already lives in the warehouse.

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