DeepSeek vs Toloka

DeepSeek — Foundation Models · Private · $50B valuation · 4 of 4 figures sourced  |  Toloka — Infrastructure · Private · 1 of 1 figure sourced

Relationship

DeepSeek and Tendem do comparable work on code generation; both also serve buyers who need to code with an AI assistant and draft and edit written content; Toloka's scale not recorded; 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.

3 of 6 capabilitiesDifferent layer

3 of 6 capabilities — Shares agent orchestration, code generation and text generation.

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

FieldDeepSeekToloka
Size$50B valuationnot disclosed
Employees160—
Founded20232014 9 yrs earlier
StatusPrivatePrivate match
CategoryFoundation ModelsInfrastructure
Stack layerApplication, Developer tool, Model APIAI agent, Data service, Platform
HeadquartersHangzhou, ChinaAmsterdam, Netherlands

Capability overlap

Shared · 3

Agent orchestrationCode generationText generation

Not verified for Toloka · 3

Agentic codingModel inferenceSummarization

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

Not verified for DeepSeek · 8

Data analysisData labellingEvaluation and observabilityGuardrails and safetyMarketing contentModel trainingTranslationWorkflow automation

Recorded for Toloka. DeepSeek’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

DeepSeek

No shared stack layer with the other side.

Toloka

No shared stack layer with the other side.

No counterpart

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

Application

DeepSeekApplication

Assistant app for chat, reasoning and code questions, built on DeepSeek's own models.

Developer tool

DeepSeek HarnessDeveloper tool

Open-source framework for building agents from composable plugins, distributed under the MIT license as a developer-preview initiative (install via `npx @deepseek-ai/dsh web` or clone from GitHub).

Model API

DeepSeek API PlatformModel API

API access to DeepSeek's chat and reasoning models.

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