Dataiku Data Science Platform REST API
Dataiku Data Science Platform REST API for enterprises to build, deploy, and govern data science and ML projects — enabling data analysts, data scientists, and ML engineers to collaborate on data pipelines, model development, model deployment, and LLM-powered applications through Dataiku's unified AI platform. Enables AI agents to manage project management for data science project lifecycle automation, handle dataset management for data preparation and feature engineering automation, access flow management for visual ML pipeline automation, retrieve model deployment for MLflow-compatible model serving automation, manage scenario automation for scheduled pipeline and retraining automation, handle LLM integration for prompt engineering and LLM application automation, access API node for model REST endpoint management automation, retrieve monitoring for model drift and data quality monitoring automation, manage collaboration for team workspace and sharing automation, and integrate Dataiku with cloud platforms, data warehouses, and MLflow for enterprise ML operations automation.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Enterprise data science. SOC2, GDPR, HIPAA. API key. US/EU. ML model and training data.
⚡ Reliability
Best When
An enterprise data science team wanting AI agents to automate project management, pipeline orchestration, model deployment, and governance across the full ML lifecycle through Dataiku's collaborative data science platform.
Avoid When
ENTERPRISE LICENSE REQUIRED: Dataiku requires enterprise agreement; automated free-developer assumption creates license_required; Dataiku pricing starts $50K+/year; automated must have Dataiku license. DSS INSTANCE IS SELF-MANAGED OR CLOUD: Dataiku deploys as on-premises DSS or Dataiku Cloud; automated shared-cloud assumption creates endpoint_mismatch for API calls to wrong instance; automated must configure correct DSS instance URL per deployment. FLOW IS RECIPE-BASED: Dataiku pipelines use visual recipes (Prepare, Python, SQL, Join); automated code-only assumption creates flow_not_executing for pipelines requiring recipe-specific execution; automated must understand Dataiku recipe types. API NODES REQUIRE SEPARATE DEPLOYMENT: Model serving via API nodes requires deploying to API node infrastructure; automated built-in-serving assumption creates api_node_not_deployed for models without API node setup; automated must configure and deploy API nodes for model serving.
Use Cases
- • Automating data science workflows from data prep to model deployment for ML operations agents
- • Managing model governance and compliance documentation for enterprise ML agents
- • Building and deploying LLM-powered applications on enterprise data for generative AI agents
- • Orchestrating scheduled ML pipeline retraining and monitoring for MLOps automation agents
Not For
- • Individual data scientists without team collaboration needs (Dataiku is collaborative platform, single-user tools are simpler)
- • Real-time streaming ML (Dataiku is primarily batch ML platform, not sub-second inference engine)
- • Large-scale distributed ML research (Dataiku is production ML, not academic research framework)
Interface
Authentication
Dataiku uses API key for DSS REST API. REST API with JSON. New York, NY HQ (US) and Paris, France (EU). Founded 2013 by Florian Douetteau, Marc Batty, Clément Stenac, Thomas Cabrol. Products: Dataiku DSS (Data Science Studio), Dataiku Cloud, Dataiku LLM Mesh, Dataiku Govern. $400M+ raised, $3.7B valuation (2023). 500+ enterprise customers. Industries: financial services, pharma, retail, manufacturing. Competes with Palantir, Databricks, and SAS for enterprise ML platform.
Pricing
New York NY / Paris FR. $3.7B valuation. Free tier (local only). Enterprise starts $50K+. 500+ enterprise customers.
Agent Metadata
Known Gotchas
- ⚠ PROJECT NAMES ARE CASE-SENSITIVE: Dataiku project identifiers are case-sensitive; automated normalized-id assumption creates project_not_found for project IDs with incorrect casing; automated must use exact project ID as returned by project list API
- ⚠ SCENARIOS ARE ASYNC: Dataiku scenario runs execute asynchronously; automated synchronous assumption creates incomplete_output for checking results before scenario run completes; automated must poll scenario run status until complete
- ⚠ RECIPE OUTPUTS REQUIRE COMPUTE: Running a recipe requires compute resources; automated instant-output assumption creates job_pending for recipes requiring compute allocation; automated must account for job queuing and execution time
- ⚠ MANAGED FOLDERS VS DATASETS DIFFER: Dataiku distinguishes managed folders (files) from datasets (tabular); automated unified-storage assumption creates wrong_api_endpoint for operations mixing folders and datasets; automated must use correct API for each storage type
- ⚠ API KEY PERMISSIONS ARE USER-SCOPED: Dataiku API keys inherit permissions of the user who created them; automated admin-access assumption creates permission_denied for API keys with limited user permissions; automated must use API keys with appropriate user role
Alternatives
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Scores are editorial opinions as of 2026-03-07.