Humain vs Toloka

Humain — Foundation Models · Private · 1 of 1 figure sourced  |  Toloka — Infrastructure · Private · 1 of 1 figure sourced

Relationship

HUMAIN COMPUTE and Toloka Train do comparable work on model training; both also serve buyers who need to train or fine-tune a model; scale not recorded for either; ships AI agent, data service and 1 more rather than the same layer.

Assembled from the recorded fields for this pair, not hand-checked. The comparison below is read from each company’s own profile.

4 of 6 capabilitiesDifferent layer

4 of 6 capabilities — Shares agent orchestration, data analysis, model training and 1 more.

Ludbee capability tags · from the product records

Different layer — Toloka ships AI agent, data service and 1 more, not the same layer.

Ludbee product records

Aligned comparison

FieldHumainToloka
Sizenot disclosednot disclosed
Employees——
Founded20252014 11 yrs earlier
StatusPrivatePrivate match
CategoryFoundation ModelsInfrastructure
Stack layerAPI service, Agent platform, Infrastructure serviceAI agent, Data service, Platform
HeadquartersRiyadh, Saudi ArabiaAmsterdam, Netherlands

Capability overlap

Shared · 4

Agent orchestrationData analysisModel trainingWorkflow automation

Not verified for Toloka · 2

GPU cloudModel inference

Recorded for Humain. Toloka’s product records say nothing either way — a missing record is not a missing capability.

Not verified for Humain · 7

Code generationData labellingEvaluation and observabilityGuardrails and safetyMarketing contentText generationTranslation

Recorded for Toloka. Humain’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

Humain

No shared stack layer with the other side.

Toloka

No shared stack layer with the other side.

No counterpart

Humain sells these in a stack layer with no product recorded for Toloka yet — nothing on the other side to compare them against.

API service

HUMAIN FabricAPI service

AI-native data platform that turns fragmented enterprise data into governed, agent-ready assets served over the Model Context Protocol (MCP), with governance built in.

Agent platform

HUMAIN ONEAgent platform

Agent-based interface that connects an organisation's systems and runs tasks across them from one place.

Infrastructure service

HUMAIN COMPUTEInfrastructure service

Saudi data-centre and GPU capacity offered for training and serving models.

Toloka sells these in a stack layer with no product recorded for Humain yet — nothing on the other side to compare them against.

AI agent

TendemAI 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

Toloka ArenaPlatform

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.

Toloka TrainPlatform

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.

Data service

Off-the-shelf DatasetsData 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.

Toloka Physical AIData service

Human-in-the-loop data programs -- demonstrations, annotation and evaluation -- for training robotics and physical AI systems.

Toloka PlatformData service

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.