Machine Learning · head to head
Dataiku vs Height
Dataiku
Machine Learning
Browser-based platform where visual data preparation and written code share one pipeline
- From
- Free
- Rated
- -
Height
Technology
A project management tool from a small independent vendor that uses AI agents to handle routine ticket maintenance.
- From
- Free
- Rated
- -
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.; Height it is one product from one small venture-funded company with no second line of business underwriting it, so adopting it as your system of record is a bet on that company's funding, and the tool holds work history you would have to reconstruct elsewhere if the bet fails.
- They diverge on capability: Dataiku covers Visual Flow, Height covers Autonomous triage.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Dataiku and Height actually diverge.
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 Height
- Autonomous triage
- Duplicate detection
- Attribute maintenance
- Per-task chat
- Multiple views
- Developer integrations
- Custom fields and filters
- Public API
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 Height
- Regulated model risk environments needing documented lineage, sign-off and a record of how a production model was producednot Height
- Pushing heavy transformations down into a cloud warehouse while keeping the pipeline definition in one reviewable placenot Height
- Large enterprises replacing a sprawl of spreadsheets and unmanaged scripts with something a governance function will acceptnot Height
Height
- A product or engineering team with no dedicated project manager, where backlog upkeep currently falls on whoever has timenot Dataiku
- Teams leaving Jira because its configuration and administration cost more attention than the tracking is worthnot Dataiku
- A support or intake queue where incoming requests need categorising and deduplicating before anyone can plan themnot Dataiku
- Startups that want tasks, chat and progress tracking in one tool rather than stitching a tracker to a chat appnot 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.
Height
- It is one product from one small venture-funded company with no second line of business underwriting it, so adopting it as your system of record is a bet on that company's funding, and the tool holds work history you would have to reconstruct elsewhere if the bet fails.
- The 2024 relaunch as Height 2.0 reoriented the product around AI agents and changed workflows customers had already built on, which is the clearest available evidence of how much the product may be re-shaped again under you.
- The ecosystem is small next to Jira, Linear and Asana: fewer third-party integrations, no consultancy market, and far less written material to search when something behaves unexpectedly, so support questions go to the vendor and wait.
- The automation only pays off if tasks contain enough substance for a model to work with; on a team whose tickets are two-word titles, the agents have nothing to triage or deduplicate and the product reduces to an ordinary tracker at a premium.
- Task content is processed by hosted large language models, so a security review becomes a question about subprocessors and data handling, and there is no self-hosted or on-premises deployment to fall back on if the answer is unacceptable.
- There is no widely used two-way synchronisation with Jira, so an organisation where one team adopts Height and the rest stay on Jira ends up with two systems of record and manual reconciliation between them.
Pricing, plan by plan
Dataiku
Free- Free EditionFree
- Single user
- Core features
- EnterpriseFree
- Full platform
- Collaboration
- MLOps
Height
FreeNo published plan breakdown. See the Height 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.
Choose Height if
- You need autonomous triage.
- You want to start without paying.
- You also want duplicate detection.
Questions people ask
- Is Dataiku or Height better?
- Neither clearly leads. Dataiku starts at Free and Height at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Dataiku or Height?
- Dataiku starts at Free and Height at Free.
- Does Dataiku or Height run on more platforms?
- Dataiku runs on Linux, Mac, Windows, Web. Height runs on Web.
- 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 Height is typically brought in for.
- What can Dataiku do that Height cannot?
- Dataiku covers Visual Flow, Visual recipes, Code recipes and notebooks, Computation pushdown. Height covers Autonomous triage, Duplicate detection, Attribute maintenance, Per-task chat.
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.
Height: How is this different from Jira automation?
Jira automation is rule-based: you define a trigger and an action. Height's agents read the content of tasks and act on judgement, such as recognising that two tickets describe the same bug, which no rule can express.
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.
Height: Can we self-host it?
No. It is software as a service only, with no on-premises or private-cloud deployment. If your requirements rule out a hosted tracker, this is not a candidate.
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.
Height: What happens to our data if the company fails?
You would need an export and a migration to another tool. This is the standard risk with a single-product startup, and it is worth confirming the export path covers task history, comments and custom fields before committing to it.
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.
Height: Does it work for non-engineering teams?
Yes, the views and custom fields are generic enough for marketing, operations or support queues. Its integrations, though, are aimed at software teams, so a non-engineering team gets less of the surrounding value.
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.
Height: Do the AI features need our tickets to be well written?
In practice, yes. Triage, deduplication and attribute maintenance work from what is written in the task, so the return is much higher on a team that already writes descriptive tickets than on one that does not.
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- Height vs AWS SageMaker
- Height vs Anaconda
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- Height vs RapidMiner
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- Height vs KNIME
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- Height vs Neptune.ai
- Height vs ClearML
- Height vs Python
- Height vs Groq
- Height vs Haystack
- Height vs IBM SPSS
- Height vs JMP
- Height vs Minitab
- Height vs Mistral AI
- Height vs Asana
- Height vs ClickUp
- Height vs Linear
- Height vs Monday.com
- Height vs Shortcut
- Height vs Coda
- Height vs Intercom
- Height vs Attio
- Height vs Jenkins
- Height vs Kubernetes
- Height vs Notion
- Height vs Figma
- Height vs Site24x7
- Height vs StatusCake
- Height vs Storybook
- Height vs WebStorm
- Height vs Zabbix Cloud