Agentic Index
Datafold vs Dataiku (2026)
Datafold last verified ·Dataiku last verified
Datafold and Dataiku both sell to data teams, for different jobs. That verdict is the Agentic Index coverage score, graded from each vendor's own published materials.
Datafold automates data engineering. Its Migration Agent translates pipelines and validates them to full parity against the source, and its Data Knowledge Graph and MCP tools give coding agents reliable context on the customer's data. Migrations are priced as a fixed, outcome based fee scoped by the number of legacy objects and their complexity, with the timeline and data parity guaranteed by contract. Dataiku is a data science and machine learning platform with an agentic layer, where agents are built and governed beside the team's pipelines and models, with a Free Edition and a 14 day cloud trial. Choose Datafold to move a legacy data estate with parity guaranteed, and Dataiku to build and govern agents across your data work.
On the Agentic Index MCP platform ranking, Datafold and Dataiku both clear the bar: each documents all three interoperability capabilities in full. 142 of the 554 platforms it grades clear it. See the MCP platform ranking
This comparison is published by Agentic Index, an independent agentic AI vendor research platform. Datafold and Dataiku are each graded against the same 14 capability Agentic Index taxonomy, from the vendor's own public materials under the Agentic Index verification standard, alongside 1,084 researched vendors. No vendor pays for placement and no vendor has reviewed this page. How this evidence is graded
Choose Datafold if
- You are migrating a legacy data estate and want parity with the source proven.
- A fixed price with the timeline and data parity guaranteed by contract is how you want to buy.
- Coding agents on your team need reliable context about your data through MCP.
Choose Dataiku if
- Agents should be built and governed beside your pipelines and models.
- Agents need human approval steps, memory and orchestration.
- A Free Edition and a trial let you start before a contract.
| Feature | D Datafold |
D Dataiku |
|---|---|---|
| Action & orchestration | ||
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Integrations & Tool Calling Ability to connect agents to real systems through native integrations, OAuth-authenticated actions, custom tools, APIs, webhooks, or MCP-compatible tools. |
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DatafoldIntegrations & Tool Calling Datafold's AI Code Reviews post a summary comment and inline suggested changes on pull requests through its GitHub, GitLab and Azure DevOps apps, and the Migration Agent works across legacy and modern warehouses, ETL tools and the customer's code repository to translate pipelines. Sourcedocs.datafold.com/deployment-testing/ai-code-reviews.mdread 2026-09-28 |
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DataikuIntegrations & Tool Calling Agents call managed tools, API endpoints, OpenAPI specs, local and remote MCP servers, Knowledge Bank search, LLM Mesh queries, web search, inline code and plugin tools, over connections secured per group. Sourcedoc.dataiku.com/dss/latest/agents/index.htmlread 2026-09-27 |
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Workflow Orchestration Ability to sequence, branch, retry, route, and combine deterministic workflow nodes with autonomous agent steps. |
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DatafoldWorkflow Orchestration The Migration Agent runs a fixed loop of translating, compiling, diffing and self correcting until parity, and code reviews pass through a review supervisor. The LLM provider settings refer to migration goals and per role model overrides, but the roles are not described, and customers cannot compose their own branching or flows. Sourcedocs.datafold.com/data-migration-automation/datafold-migration-agent.mdread 2026-09-28 |
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DataikuWorkflow Orchestration Structured visual agents define sequences with routing, delegation to other agents, parallel and for each branches, agentic loops and reflection blocks, alongside code agents and external agents from Snowflake Cortex, Databricks, AWS Bedrock and Google Vertex AI; Agent Hub can orchestrate a query across several agents. Sourcedoc.dataiku.com/dss/latest/agents/structured-visual-agents/index.htmlread 2026-09-27 |
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Triggers & Channel Coverage How agents wake up and where they work: schedules, webhooks, message events, CRM events, inbox events, chat, email, voice, and collaboration tools. |
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DatafoldTriggers & Channel Coverage AI Code Reviews run automatically whenever a pull request is created or updated, monitors run on schedules and alert on data diffs, metrics, tests and schema changes, and people reach the assistant by mentioning it in Slack or through MCP clients. Sourcedocs.datafold.com/deployment-testing/ai-code-reviews.mdread 2026-09-28 |
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DataikuTriggers & Channel Coverage Agents run inside automation scenarios, which start on time based, dataset change, SQL query change, scenario completion or Python triggers without a person, and users reach agents in Slack (DM or mention), Microsoft Teams, Agent Hub, Agent Chat and through APIs. Sourcedoc.dataiku.com/dss/latest/scenarios/index.htmlread 2026-09-27 |
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| Knowledge & context | ||
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Knowledge Grounding & RAG Ability to ground agent behavior in company data through document ingestion, retrieval, external knowledge APIs, semantic search, or RAG layers. |
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DatafoldKnowledge Grounding & RAG The Data Knowledge Graph serves the customer's lineage, business logic, usage, ontology and organizational knowledge to Datafold's agents and, over MCP, to the customer's own coding agents. Sourcedatafold.comread 2026-09-28 |
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DataikuKnowledge Grounding & RAG Knowledge Banks embed datasets and documents into vector stores that agents search through a Knowledge Bank Search tool with static, dynamic and agent inferred filters and document level security, with GraphRAG and automated RAG optimization also documented. Sourcedoc.dataiku.com/dss/latest/agents/tools/knowledge-bank-search.htmlread 2026-09-27 |
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Memory & State Persistence Ability to persist context across a run, conversation, workflow, user, team, or longer-term memory layer. |
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DatafoldMemory & State Persistence The Slack assistant rereads the latest messages of a thread on every turn, about fifteen messages, so context lasts within a thread. The agents keep no memory across sessions with a set scope and lifetime, and the Data Knowledge Graph holds the customer's knowledge, not the agents' own memory. Sourcedocs.datafold.com/integrations/agents/slack-bot.mdread 2026-09-28 |
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DataikuMemory & State Persistence Long term memory blocks store facts, such as preferences, and episodes from past conversations in memory banks, kept separately per user, shared by agents in a project through a default bank or split into named banks, and retrieved by LLM filtering and semantic search in later conversations. No retention period or delete path is stated. Sourcedoc.dataiku.com/dss/latest/agents/structured-visual-agents/blocks/long-term-memory.htmlread 2026-09-27 |
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| Control & trust | ||
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Human Oversight & Guardrails Approval steps, consent checkpoints, escalation rules, structured guardrails, policy constraints, and pause/resume controls. |
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DatafoldHuman Oversight & Guardrails Admins limit which MCP tools an agent's service account can call through group permissions, hiding the rest, and code review suggestions wait for people to merge them in their own Git host. Datafold's engineers oversee migrations as part of its service, which the customer does not operate, and there is no approval step before an agent action commits. Sourcedocs.datafold.com/security/mcp-tool-permissions.mdread 2026-09-28 |
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DataikuHuman Oversight & Guardrails Human approval set on a tool makes a visual agent pause before calling it and ask the user to confirm, optionally letting the person reviewing edit the tool inputs (not inside For Each, Parallel or Reflection sub sequences), and guardrails cover PII, prompt injection, toxicity, topic boundaries and bias. Sourcedoc.dataiku.com/dss/latest/agents/tools/human-approval.htmlread 2026-09-27 |
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Security, Identity & Governance RBAC, SSO, auditability, encryption, least-privilege tool access, compliance posture, and data handling policy. |
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DatafoldSecurity, Identity & Governance The SafeBase trust center lists SOC 2, HIPAA and GDPR with reports available on request, and Datafold supports SSO through Okta OIDC, Google OAuth, SAML and Microsoft Entra ID with group provisioning, user roles and permissions, custom groups and service accounts. Sourcesecurity.datafold.comread 2026-09-28 |
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DataikuSecurity, Identity & Governance Dataiku offers SSO through SAML, OIDC, LDAP and Kerberos, MFA, user profiles, per project group and user permissions, connection security, user secrets and an audit trail. Its trust page lists ISO 27001:2022, ISO 27701, ISO 42001 and ISO 9001 certification, SOC 1 and SOC 2 Type II for Dataiku Cloud, and a HIPAA report. Sourcedataiku.com/legal/trustread 2026-09-27 |
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Observability & Auditability Traces, logs, execution histories, metrics, audit events, and debugging detail for production agent behavior. |
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DatafoldObservability & Auditability An audit log API returns each action with the user, IP, request, object type and ID, status and payload, and agents act through service accounts whose MCP tool calls run under named permissions. Datafold does not say whether the log marks an action as an agent's. Sourcedocs.datafold.com/api-reference/audit-logs/get-audit-logs.mdread 2026-09-28 |
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DataikuObservability & Auditability Every LLM Mesh call returns a nested trace of the agent's LLM calls, guardrail steps, inputs, outputs, tokens and cost, viewed in the Trace Explorer (tree, timeline and explorer views), and the evaluation trajectory explorer replays tool calls in order; an audit trail records user actions. Sourcedoc.dataiku.com/dss/latest/agents/tracing.htmlread 2026-09-27 |
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Deployment & Data Residency Deployment modes and options, including SaaS, dedicated cloud, VPC, on-prem, hybrid, local runtime, and self-hosting. |
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DatafoldDeployment & Data Residency Multi tenant SaaS runs in US West (Oregon) or Europe (Ireland) at the customer's choice, and Dedicated Cloud deploys a single tenant instance in a VPC on AWS, GCP or Azure, hosted by Datafold or in the customer's own account and managed by either party. Sourcedocs.datafold.com/datafold-deployment/datafold-deployment-options.mdread 2026-09-28 |
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DataikuDeployment & Data Residency Dataiku runs as fully managed Dataiku Cloud or is installed on the customer's own infrastructure (Linux, macOS, experimental Windows) with hybrid options through sales, with installation, cloud and container deployment guides; locally running Hugging Face models keep inference in house. Sourcedataiku.com/product/get-startedread 2026-09-27 |
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| Solution readiness | ||
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Prebuilt Agents, Templates & Packs Ready-made workflows, packaged employees, templates, blueprints, industry solutions, and role-specific agents that reduce time-to-value. |
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DatafoldPrebuilt Agents, Templates & Packs Datafold ships specialized agents that each do their own job. The Migration Agent translates and validates pipelines, AI Code Reviews review pull requests, and a Slack assistant answers questions about data sources, lineage, monitors and diffs. An optimization agent is also named, without a description. Sourcedatafold.comread 2026-09-28 |
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DataikuPrebuilt Agents, Templates & Packs Dataiku Solutions include named GenAI and agent solutions for separate jobs, Dynamic Selling Assistant, Supplier Management Assistant, Clinical Trial Intelligence Assistant, Medical Entity Extraction Assistant and Agentic Insights; Agent Hub distributes an organization's own governed agents and prompt library. Sourceknowledge.dataiku.com/latest/solutions/index.htmlread 2026-09-27 |
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| Platform extensibility | ||
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Model Flexibility & Routing Ability to work across multiple foundation models, route tasks to different models, or let buyers bring their own providers and keys. |
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DatafoldModel Flexibility & Routing Organization admins register their own LLM provider (OpenAI, Anthropic, Google Gemini, Azure AI Foundry, AWS Bedrock, Google Vertex AI, Databricks, Snowflake Cortex or any OpenAI compatible endpoint) and set models for goals, project defaults and per role overrides; without one the Migration Agent runs on Datafold managed inference. Sourcedocs.datafold.com/integrations/llm-providers.mdread 2026-09-28 |
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DataikuModel Flexibility & Routing Administrators define LLM Mesh connections to OpenAI, Azure OpenAI, Anthropic, AWS Bedrock and SageMaker, Microsoft Foundry, Google Vertex, Mistral, Cohere, NVIDIA NIM, Snowflake Cortex, Databricks foundation model APIs and locally running Hugging Face models, and agents pick from them, with cost control and rate limiting documented. Sourcedoc.dataiku.com/dss/latest/generative-ai/llm-connections.htmlread 2026-09-27 |
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APIs, SDKs & MCP Extensibility Composability layer: stable APIs, SDKs, MCP tool consumption/serving, custom tools, and integration into internal systems. |
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DatafoldAPIs, SDKs & MCP Extensibility A Datafold API and SDK sit beside an MCP server at app.datafold.com/mcp authenticated with an API key, whose permission table names tools such as list_data_sources, create_datadiff and edit_alert that create and change Datafold's own objects. Sourcedocs.datafold.com/api-reference/mcp-server-setup.mdread 2026-09-28 |
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DataikuAPIs, SDKs & MCP Extensibility Agents are exposed as A2A servers at a per agent public API endpoint with well known URI discovery and bearer API key auth, in JSON-RPC and SSE modes, beside a documented MCP server, the public REST API, the Python API and LangChain integration on developer.dataiku.com, and a plugin system. Sourcedoc.dataiku.com/dss/latest/agents/a2a.htmlread 2026-09-27 |
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Testing, Debugging & Optimization Testing, debugging, scoring, retries, fallbacks, quality gates, and optimization loops for improving agent workflows before and after deployment. |
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DatafoldTesting, Debugging & Optimization The Migration Agent's translated code is checked against the source by value level data diffs, and the agent corrects itself from compilation errors and data discrepancies until parity is reached, so its output does not pass until the data matches. The final report links the diffs proving parity or explaining each discrepancy. Sourcedocs.datafold.com/data-migration-automation/datafold-migration-agent.mdread 2026-09-28 |
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DataikuTesting, Debugging & Optimization The Evaluate Agent recipe scores answers and the path taken, with ordered and unordered tool call checks, RAGAS based LLM judges, BERTScore and custom metrics, stores results for side by side version comparison, and Agent Review lets builders and SMEs run test cases against an agent version with human verdicts overriding the judge. Sourcedoc.dataiku.com/dss/latest/agents/evaluation.htmlread 2026-09-27 |
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| Specialist automation | ||
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Browser & Computer Use Browser, desktop, or remote/local computer control for workflows that cannot be handled through stable APIs alone. |
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DatafoldBrowser & Computer Use Datafold's agents work through data source connections, Git apps, the API and MCP tools and do not control a browser, desktop or computer. Sourcedocs.datafold.com/datafold-mcp.mdread 2026-09-28 |
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DataikuBrowser & Computer Use The agent tool list covers APIs, MCP, knowledge search, code and an LLM side web search tool, with no browser or computer use tool documented. Sourcedoc.dataiku.com/dss/latest/agents/index.htmlread 2026-09-27 |
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Pricing snapshot
Sourced from the Index pricing dataset · open each vendor's profile for full detail.
| Pricing | D Datafold |
D Dataiku |
|---|---|---|
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Entry price Lowest public entry point |
Outcome based fixed price scoped per migration by number of legacy objects and complexity, with timeline and data parity guaranteed by contract. No public dollar figure. | Free Edition and a 14 day cloud trial. Paid editions are quoted by sales. |
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Pricing confidence How public the numbers are |
Contact only | Public, partial |
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Billing Primary billing axis |
A fixed price per migration, scoped by the number of legacy objects and the migration's complexity. | Paid editions are priced by number of users and by the platform capabilities enabled. |
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Variable cost Workload / overage exposure |
Low variable cost | Medium variable cost |
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Free tier / trial Try before you buy |
No free tier
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Free tierTrial
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Buying motion Self-serve vs sales call |
Sales call | Sales call |
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Pricing verified When this pricing was last checked |
Product updates
Newest first, each linked to its source.
Dataiku
-
· MCP / tool calling / API
Headless is a new plugin and MCP server that lets coding agents such as Claude Code, Codex and Cursor create and change Dataiku projects, including pipelines, models, dashboards and agents, by handing the build to Cobuild.
Source -
· Human approval / guardrails
DSS 15.1.0 adds a Plan mode to Cobuild, Dataiku's natural language build agent, which proposes a set of high level tasks that users review and refine before the build starts.
Source -
· Integrations
Dataiku launched Cobuild on Snowflake, which combines Snowflake Cortex AI's model access with Dataiku's orchestration layer so users can build and validate AI workflows and agents in plain language.
Source
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