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Machine Learning · head to head

Dataiku vs Teradata

Dataiku logo

Dataiku

Machine Learning

Browser-based platform where visual data preparation and written code share one pipeline

From
Free
Free plan
Yes
Visit DataikuFull 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

  • Dataiku is free; Teradata prices on request.
  • Only Dataiku lists visual Flow; only Teradata lists massively parallel architecture.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

Only the attributes on which Dataiku and Teradata actually diverge.

Attributes where Dataiku and Teradata differ
AttributeDataikuTeradata
Starting priceFreeOn request
Pricing modelFreemiumQuote
Free tierYesNo
PlatformsLinux, macOS, Windows, WebWeb
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 Dataiku

  • Visual Flow
  • Visual recipes
  • Code recipes and notebooks
  • Computation pushdown
  • Automated machine learning
  • Scenarios
  • Node topology
  • Governance features

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.

Dataiku

  • Organisations where analysts and data scientists must collaborate on the same pipeline rather than exchanging extractsnot Teradata
  • Regulated model risk environments needing documented lineage, sign-off and a record of how a production model was producednot Teradata
  • Pushing heavy transformations down into a cloud warehouse while keeping the pipeline definition in one reviewable placenot Teradata
  • Large enterprises replacing a sprawl of spreadsheets and unmanaged scripts with something a governance function will acceptnot Teradata

Teradata

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

Where each one falls short

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

Dataiku

  • Visual recipes are stored as Dataiku's own configuration and do not export as runnable SQL or Python, so a Flow with hundreds of visual steps has to be rebuilt from scratch if the organisation ever leaves, and that cost rises with every project added
  • Production requires separate automation and API nodes, each installed and licensed, so the figure quoted for building models is not the figure for running them
  • Licensing is per user across tiers, and the lower tiers are constrained enough that occasional contributors frequently end up needing a full seat, which makes a wide rollout cost more than the initial estimate suggested
  • A self-hosted installation needs a dedicated administrator for upgrades, connection management, permissions and node topology, so the licence is a fraction of the real cost of ownership
  • Computation pushes down to the warehouse or Spark cluster where it is billed by that provider, so a platform sold on making analysts self-sufficient can generate a large warehouse bill that nobody attributes back to it

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

Dataiku

Free
  • Free EditionFree
    • Single user
    • Core features
  • EnterpriseFree
    • Full platform
    • Collaboration
    • MLOps

Teradata

On request

No published plan breakdown. See the Teradata review.

Which should you pick?

Choose Dataiku if

  • You need visual flow.
  • You want to start without paying.
  • You work on Linux, macOS, Windows, Web.
  • You also want visual recipes.
Dataiku review

Choose Teradata if

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

Questions people ask

Is Dataiku or Teradata better?
Neither clearly leads. Dataiku 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, Dataiku or Teradata?
Dataiku has a free tier; the other does not. Paid plans start at Free for Dataiku and On request for Teradata.
Does Dataiku or Teradata run on more platforms?
Dataiku runs on Linux, macOS, Windows, Web. Teradata runs on Web.
Can I use Dataiku for free?
Yes. Dataiku has a free tier, so you can try it without paying. Teradata starts at On request.
What is Dataiku best used for?
Dataiku is most often used for organisations where analysts and data scientists must collaborate on the same pipeline rather than exchanging extracts, regulated model risk environments needing documented lineage, sign-off and a record of how a production model was produced, pushing heavy transformations down into a cloud warehouse while keeping the pipeline definition in one reviewable place, large enterprises replacing a sprawl of spreadsheets and unmanaged scripts with something a governance function will accept. Of those, organisations where analysts and data scientists must collaborate on the same pipeline rather than exchanging extracts and regulated model risk environments needing documented lineage, sign-off and a record of how a production model was produced are not what Teradata is typically brought in for.
What can Dataiku do that Teradata cannot?
Dataiku covers Visual Flow, Visual recipes, Code recipes and notebooks, Computation pushdown. Teradata covers Massively parallel architecture, Workload management, Mature cost-based optimiser, Cloud, on-premises and hybrid.

Answered from the vendors’ own pages

Dataiku: Is there a free version?

There is a free edition with limits on users and features, adequate for evaluation and personal work. Anything a team runs in production is a negotiated commercial agreement.

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.

Dataiku: Do I have to write code to use it?

No. That is the premise. An analyst can build a complete pipeline through visual recipes, and a data scientist can write Python next to it in the same Flow.

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.

Dataiku: Where does the computation actually run?

Wherever you connect it. Transformations are pushed down into the warehouse, database or Spark cluster where the data lives, which is efficient and also means the compute cost appears on that provider's bill rather than Dataiku's.

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.

Dataiku: Can I export my work if we leave?

Code recipes are your code and leave with you. Visual recipes do not export as equivalent code, so the visual portion of a Flow has to be reimplemented, and that portion tends to be the majority in the projects where the platform succeeded best.

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.

Dataiku: Self-hosted or cloud?

Both are offered. Self-hosting gives control over data residency and networking and requires an administrator; the managed cloud removes that work and moves the constraint to what the vendor's environment supports.

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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