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Machine Learning · head to head

Dataiku vs Lindy

Dataiku logo

Dataiku

Machine Learning

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

From
Free
Rated
-
Full reviewWebsite
Lindy logo

Lindy

AI

AI teammate that automates work across your entire software stack

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.; Lindy requires per-user subscription, which scales costs with team size
  • They diverge on capability: Dataiku covers Visual Flow, Lindy covers Slack integration.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Dataiku and Lindy actually diverge.

Attributes where Dataiku and Lindy differ
AttributeDataikuLindy
Pricing modelfreemiumPer-user subscription with shared credit pool
PlatformsLinux, Mac, Windows, WebWeb, Slack, Email, Mobile
CategoryMachine LearningAI
Founded2013Unknown

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

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 Lindy

  • Slack integration
  • Email automation
  • Meeting transcription
  • Scheduled routines
  • 1,000+ app integrations
  • Skill library
  • Editable memory
  • MCP support

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

Lindy

  • Automating email and calendar management across teamsnot Dataiku
  • Transcribing and summarizing meetings automaticallynot Dataiku
  • Updating CRM systems with meeting notes and leadsnot Dataiku
  • Generating daily reports and weekly briefsnot Dataiku
  • Processing structured data from multiple applicationsnot 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.

Lindy

  • Requires per-user subscription, which scales costs with team size
  • Limited free trial period of 7 days may not allow full evaluation
  • Credit system complexity could be confusing for new users
  • Not designed for technical teams as primary tool

Pricing, plan by plan

Dataiku

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

Lindy

Free
  • Free TrialFree
    • 7-day free trial for new team members joining via Slack
  • Plus$29.99/month
    • 3,000 credits per month
    • Slack integration
    • Email automation
  • Pro$99.99/month
    • 15,000 credits per month
    • All Plus features
    • Advanced automation
  • Max$199.99/month
    • 35,000 credits per month
    • All Pro features
    • Priority support

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 Lindy if

  • You need slack integration.
  • You want to start without paying.
  • You work on Web, Slack, Email, Mobile.
  • You also want email automation.
Lindy review

Questions people ask

Is Dataiku or Lindy better?
Neither clearly leads. Dataiku starts at Free and Lindy at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Dataiku or Lindy?
Dataiku starts at Free and Lindy at Free.
Does Dataiku or Lindy run on more platforms?
Dataiku runs on Linux, Mac, Windows, Web. Lindy runs on Web, Slack, Email, Mobile.
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 Lindy is typically brought in for.
What can Dataiku do that Lindy cannot?
Dataiku covers Visual Flow, Visual recipes, Code recipes and notebooks, Computation pushdown. Lindy covers Slack integration, Email automation, Meeting transcription, Scheduled routines.

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.

Lindy: How does Lindy maintain data privacy and security?

Lindy is SOC 2 Type II, GDPR, HIPAA, and PIPEDA compliant. Data is encrypted in transit and at rest. The platform never sells your data or uses it to train models. Enterprise plans include signed BAAs and audit logs.

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.

Lindy: Can we start with a free trial?

New team members joining through Slack receive a 7-day free trial before being billed. Direct signups are charged immediately. There is no permanent free tier after the trial.

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.

Lindy: How many applications can Lindy integrate with?

Lindy connects to over 1,000 applications including Gmail, Notion, HubSpot, GitHub, Stripe, and others, with support for Model Context Protocol servers for custom integrations.

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.

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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