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Machine Learning · head to head

Dataiku vs Neptune.ai

Dataiku logo

Dataiku

Machine Learning

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

From
Free
Rated
-
Full reviewWebsite
Neptune.ai logo

Neptune.ai

Machine Learning

Metadata store for MLOps

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: 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.; Neptune.ai free tier limited to 100 hours per month, exhausted quickly with serious ML work
  • They diverge on capability: Dataiku covers Visual Flow, Neptune.ai covers Experiment tracking.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Dataiku and Neptune.ai actually diverge.

Attributes where Dataiku and Neptune.ai differ
AttributeDataikuNeptune.ai
Pricing modelfreemiumUnknown
PlatformsLinux, Mac, Windows, WebWeb, Self-hosted
Founded20132017

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 Dataiku

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

Only in Neptune.ai

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

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 Neptune.ai
  • Regulated model risk environments needing documented lineage, sign-off and a record of how a production model was producednot Neptune.ai
  • Pushing heavy transformations down into a cloud warehouse while keeping the pipeline definition in one reviewable placenot Neptune.ai
  • Large enterprises replacing a sprawl of spreadsheets and unmanaged scripts with something a governance function will acceptnot Neptune.ai

Neptune.ai

  • Machine learningnot Dataiku
  • Data analysisnot Dataiku
  • Model trainingnot Dataiku
  • Predictive analyticsnot 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.

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

Pricing, plan by plan

Dataiku

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

Neptune.ai

Free

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

Which should you pick?

Choose Dataiku if

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

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

Questions people ask

Is Dataiku or Neptune.ai better?
Neither clearly leads. Dataiku starts at Free and Neptune.ai at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Dataiku or Neptune.ai?
Dataiku starts at Free and Neptune.ai at Free.
Does Dataiku or Neptune.ai run on more platforms?
Dataiku runs on Linux, Mac, Windows, Web. Neptune.ai runs on Web, Self-hosted.
Can I use Dataiku for free?
Both have a free tier, so you can try either at no cost before committing.
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 Neptune.ai is typically brought in for.
What can Dataiku do that Neptune.ai cannot?
Dataiku covers Visual Flow, Visual recipes, Code recipes and notebooks, Computation pushdown. Neptune.ai covers Experiment tracking, Model registry, Metadata logging, Comparison views.

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.

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
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.

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
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.

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
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.

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
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.

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