Machine Learning · head to head
Dataiku vs Postgres
Dataiku
Machine Learning
Browser-based platform where visual data preparation and written code share one pipeline
- 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.; Postgres each major version is supported for only 5 years after its initial release, after which it is end-of-life
- They diverge on capability: Dataiku covers Visual Flow, Postgres covers ACID compliance.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Dataiku and Postgres 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 Postgres
- ACID compliance
- Complex queries
- Foreign keys
- Triggers
- Views
- Stored procedures
- JSON/JSONB support
- Full-text search
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 Postgres
- Regulated model risk environments needing documented lineage, sign-off and a record of how a production model was producednot Postgres
- Pushing heavy transformations down into a cloud warehouse while keeping the pipeline definition in one reviewable placenot Postgres
- Large enterprises replacing a sprawl of spreadsheets and unmanaged scripts with something a governance function will acceptnot Postgres
Postgres
- Running a general purpose relational database for applicationsnot Dataiku
- Self-hosting an open source SQL database with no licence feenot Dataiku
- Workloads needing extensions, JSON and full text search in one enginenot 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.
Postgres
- Each major version is supported for only 5 years after its initial release, after which it is end-of-life
- Major version upgrades break on-disk compatibility and require a full dump and reload or the pg_upgrade tool
- New major versions ship about once a year, so staying supported means a disruptive upgrade cycle
- Minor releases contain only frequently-encountered bug fixes, low-risk fixes, security issues and data corruption fixes, so feature gaps are not addressed within a major version
- There is no vendor SLA; commercial support must be bought separately from third party professional services listed by the project
Pricing, plan by plan
Dataiku
Free- Free EditionFree
- Single user
- Core features
- EnterpriseFree
- Full platform
- Collaboration
- MLOps
Postgres
Free- Community EditionFree
- Full database features
- No limitations
- Community 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.
Choose Postgres if
- You need acid compliance.
- You want to start without paying.
- You work on Linux, Windows, Macos, Docker.
- You also want complex queries.
Questions people ask
- Is Dataiku or Postgres better?
- Neither clearly leads. Dataiku starts at Free and Postgres at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Dataiku or Postgres?
- Dataiku starts at Free and Postgres at Free.
- Does Dataiku or Postgres run on more platforms?
- Dataiku runs on Linux, Mac, Windows, Web. Postgres runs on Linux, Windows, Macos, Docker.
- 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 Postgres is typically brought in for.
- What can Dataiku do that Postgres cannot?
- Dataiku covers Visual Flow, Visual recipes, Code recipes and notebooks, Computation pushdown. Postgres covers ACID compliance, Complex queries, Foreign keys, Triggers.
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.
Postgres: How much does PostgreSQL cost to use?
PostgreSQL is completely free to download, install, and use. No licensing fees, subscription costs, or per-seat charges apply. The database is open source under the PostgreSQL License. Source: https://www.postgresql.org
SourceDataiku: 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.
Postgres: Are there commercial PostgreSQL support options available?
Official PostgreSQL (the project) is free. Commercial PostgreSQL services such as hosting, professional support, training, and managed database services are offered by third-party vendors, not the PostgreSQL project itself. Source: https://www.postgresql.org
SourceDataiku: 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.
Postgres: Do I need a license to use PostgreSQL commercially?
No. PostgreSQL is open source under the PostgreSQL License, which permits free commercial use without royalties, licensing fees, or support obligations. Source: https://www.postgresql.org
SourceDataiku: 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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