Sama vs Toloka

Sama's data annotation and validation platform, combining annotation tooling, SamaAssure quality assurance, SamaHub project management and reporting, and SamaIQ analytics, with a REST API and CLI for pipeline integration.

Sama Annotate is Sama's enterprise data annotation offering, delivering image, video and 3D point cloud labeling through a full-time in-house annotation workforce working on the Sama Platform, with CLI and API access for automated data workflows.

Sama Curate is a data curation offering that uses filtering and curation algorithms to suggest which assets in a raw dataset should be labeled, so teams annotate only the data most likely to improve an ML model.

Sama GenAI supplies human-generated training and evaluation data for foundation models, covering model validation and fact checking, instruction following, preference ranking, image and video captioning, creative writing, synthetic data creation and RAG evaluation.

Sama Validate is a managed human-in-the-loop service in which trained reviewers inspect and correct a customer's model predictions across image, video and 3D point cloud data and report where the model performs well or poorly.

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.