Snorkel AI vs Toloka

Data-development platform where teams write programmatic labeling functions and use AI-assisted labeling to build and refine training data for machine learning and LLM systems, instead of hand-labeling manually.

Bespoke dataset, environment and benchmark development for organizations with specialized failure modes existing datasets don't cover.

Off-the-shelf curriculum-structured training/eval datasets for frontier-model task areas (terminal coding, software engineering, enterprise workflows, computer use, scientific research), with rubrics and difficulty tiers built in.

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.

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.