ClickHouse vs Toloka

Agent-native observability platform ('observability built for agents and humans') combining semantic investigation tools -- trace waterfalls, event-pattern detection, time-series analysis -- with ClickHouse's analytical engine, distinct from the existing ClickHouse Agents no-code builder.

Managed ClickHouse service on AWS, GCP and Azure whose console carries ClickHouse's own AI layer — the ClickHouse Assistant for writing and debugging SQL, the no-code ClickHouse Agents builder, a remote MCP server and Langfuse Cloud for LLM observability — on top of the open-source column-oriented analytics database.

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.