Dataiku vs Toloka
Relationship
Dataiku and Toloka Train do comparable work on model training; both also serve buyers who need to train or fine-tune a model; Toloka's scale not recorded.
Assembled from the recorded fields for this pair, not hand-checked. The comparison below is read from each company’s own profile.
6 of 8 capabilities — Shares agent orchestration, data analysis, evaluation and observability and 3 more.
Ludbee capability tags · from the product recordsShared product type — Both ship AI agent and platform.
Ludbee product recordsAligned comparison
Capability overlap
Shared · 6
Not verified for Toloka · 2
Recorded for Dataiku. Toloka’s product records say nothing either way — a missing record is not a missing capability.
Not verified for Dataiku · 5
Recorded for Toloka. Dataiku’s product records say nothing either way — a missing record is not a missing capability.
Products, side by side
Algorithmic pairing — assembled from recorded fields, not hand-checked
Dataiku
AI agent
AI building agent that turns a business objective written in plain language into a governed Dataiku project of data pipelines, models, agents and applications rendered as an editable visual workflow.
Platform
Platform for building, deploying and governing data, machine-learning and agent workflows across an organisation.
Toloka
AI agent
A hybrid AI-plus-human agent that takes a delegated task - research, data analysis, copywriting, design or development - has AI do the first pass, then routes it to one of 10,000+ vetted experts for verification and multi-layer QA, returning results in 2-24 hours.
Platform
An independent evaluation platform that ranks frontier LLMs on agentic tool-use tasks using private, non-contaminated benchmarks across industry domains, scored on a pass^5 reliability metric, with the underlying RL Gyms and evaluation datasets available to license.
A self-serve service that lowers per-request inference cost through two tools - fine-tuning LoRA adapters on frozen Qwen3 base models to replace a frontier API on a narrow task, and prompt gisting that compresses long instruction prefixes into learned tokens.
No counterpart
Dataiku sells these in a stack layer with no product recorded for Toloka yet — nothing on the other side to compare them against.
Agent platform
Centralised control plane inside the Dataiku platform for creating, orchestrating, deploying and tracking AI agents across teams.
Expert-to-Agent engine that converts subject-matter-expert know-how into governed AI agents grounded in enterprise data with structured reasoning and human oversight.
Infrastructure service
Cross-platform governance product that discovers every AI agent an enterprise is running -- on Microsoft Copilot Studio, Azure Foundry, Salesforce Agentforce, AWS Bedrock, Google Vertex, Databricks, Snowflake Cortex, n8n or Dataiku itself -- measures each agent's business and technical performance, and flags the ones that pose the greatest risk. Announced 2026-09-24; distinct from Dataiku Agent Hub, which creates and operationalizes agents rather than discovering and governing agents built anywhere.
Control layer over the Dataiku LLM Mesh that caps LLM spend, screens prompts and outputs for sensitive or malicious content, and scores model output quality.
Centralised gateway that routes, meters and governs an organisation's connections to multiple LLM providers from inside the Dataiku platform.
Toloka sells these in a stack layer with no product recorded for Dataiku yet — nothing on the other side to compare them against.
Data service
Toloka's catalogue of ready-made training datasets sold outright — three named at the time of writing (Tau-bench Dataset Extension, University-level Math Reasoning, Multimodal Conversations) — as distinct from the custom data work its Platform sells.
Human-in-the-loop data programs -- demonstrations, annotation and evaluation -- for training robotics and physical AI systems.
A self-serve platform where an AI agent turns a described data goal into a full human-annotation pipeline - RLHF and preference data, data collection, instruction tuning, model evaluation, synthetic-data validation and content-moderation QA - with LLM-based quality checks on the output.