Neo4j vs Toloka

Neo4j — Infrastructure · Private · $2B valuation · 2 of 2 figures sourced  |  Toloka — Infrastructure · Private · 1 of 1 figure sourced

Relationship

Neo4j Graph Data Science 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.

3 of 5 capabilitiesShared product type

3 of 5 capabilities — Shares agent orchestration, data analysis and model training.

Ludbee capability tags · from the product records

Shared product type — Both ship platform.

Ludbee product records

Aligned comparison

FieldNeo4jToloka
Size$2B valuationnot disclosed
Employees——
Founded—2014
StatusPrivatePrivate match
CategoryInfrastructureInfrastructure match
Stack layerAgent platform, Application, PlatformAI agent, Data service, Platform
HeadquartersSan Mateo, USAAmsterdam, Netherlands

Capability overlap

Shared · 3

Agent orchestrationData analysisModel training

Not verified for Toloka · 2

Knowledge retrievalThreat detection and response

Recorded for Neo4j. Toloka’s product records say nothing either way — a missing record is not a missing capability.

Not verified for Neo4j · 8

Code generationData labellingEvaluation and observabilityGuardrails and safetyMarketing contentText generationTranslationWorkflow automation

Recorded for Toloka. Neo4j’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

Neo4j

Platform

Neo4j Graph Data SciencePlatform

Graph analytics and machine-learning library for Neo4j: 65+ graph algorithms, node embeddings and graph-native ML pipelines for clustering, similarity and classification.

Toloka

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.

No counterpart

Neo4j sells these in a stack layer with no product recorded for Toloka yet — nothing on the other side to compare them against.

Application

GraphAware HumeApplication

Graph-powered intelligence analysis platform for government and enterprise, connecting people, organizations, locations and events into a shared intelligence picture; positioned as an open-standard alternative to Palantir Gotham.

Agent platform

Neo4j Aura AgentAgent platform

Low-code builder for agents grounded in a Neo4j knowledge graph, run as managed endpoints on Aura.

Toloka sells these in a stack layer with no product recorded for Neo4j 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.

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.