Platform for AI builders
Lower LLM cost and latency while improving quality
Set a quality benchmark for your task, then test changes against it using your own workload and your own numbers.
Developer access
Bring Toloka into your coding workflow
Use the Toloka SDK from your existing coding workflow to run supported evaluation or optimization tasks without learning a new interface. See results on your own workload within days.
Claude
Codex
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SDK
Bring frontier-lab optimization into your workflow
Toloka packages the data and optimization methods used with frontier labs into an agent for your own AI workloads.
10+ years
Frontier lab data programs
Production scale
Enterprise optimization projects
50+ methods
Automated quality control
60+ methods
Platform-level antifraud
Trusted by Leading AI Teams
Choose a tool to improve your workload
These are the tools you can use today. Anything still on the roadmap, or handled by our team for you, lives elsewhere.
Cut cost & latency
Already shipping and paying too much?
Serve the same task for less.
Prompt Gisting
Stop paying for the same prompt on every request. A long static instruction prefix is compressed into a handful of learned tokens, so every future call carries a fraction of the input.
Fine-Tune — distillation
Teach a smaller model your task. A LoRA adapter trains on a frozen open base, so you get task quality at open-model serving cost — including from an open model you already self-host.
Know and improve your quality
Don’t know how good your model is? Find out before your users tell you the hard way.
LLM-as-a-judge
Fast automated scoring, with human verification when you need more confidence in the result.
Human evaluation
Check that your automated score is reliable. Subject-matter experts review the same outputs without seeing the judge’s verdict, so you can measure genuine agreement.
Golden datasets
Create a reliable baseline with a held-out, human-calibrated eval set you can use to measure future changes and benchmark fine-tuning runs.
Training datasets
Turn raw traces into training data that moves the needle. LLM labeling with human review for difficult and ambiguous cases, all within one training-data pipeline.
Data you don’t have yet
Off-the-shelf datasets
Training and evaluation data already collected, cleared and shippable across domains, languages and modalities, By the hour or in volume tiers.
Data collection
Real-world audio, speech and egocentric video, captured to your protocol and taxonomy. Off-the-shelf by the hour, with volume tiers.
Need another tool?
New tools are being added to the platform. Join the waitlist to be the first to know what's new
More optimization methods
Extended SDK workflows
Evaluation automation
Data workflows
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Data handling
We don't train on your data. You own the artefacts, and people only see your items only on tasks where you asked for human verification.
SOC 2 Type II
ISO 27001
ISO 27701
GDPR — details in the security portal.
Results, scoped to the workload that produced them
Toloka Train Fine-tuning
CV parsing pipeline for Mindrift, Toloka's own contributor network
12×–37×
cheaper inference vs. the frontier API
0.94 F1
on the human-labeled golden set, up from 0.85
A few days
from data curation to deployed endpoint
Toloka Train Prompt Gisting
One production workload · 5,000-token instruction prefix on a Qwen base model
5.3×
fewer prompt tokens per request
−15%
GPU cost per sample
+18%
throughput under saturated serving
Both are examples measured on specific workloads, not universal guarantees.
The right expert for every task matched automatically
Automated judging gives you scale. People give the benchmark a reference point. Difficult, ambiguous, and high-risk cases route to a vetted expert in the relevant domain.
Skilled experts in 90+ domains, matched to the task
Contributors in 100+ countries speaking 40+ languages
90+
Domains of expertise
70%+
People with advanced degrees
6000+
Active contributors
Toloka Train in production
Explore the fine-tuning case behind the numbers on this page and why strong launch performance doesn’t always last.
Describe your data goal and let the agent build the workflow
Describe your full data goal. The agent builds your entire collection and annotation pipeline automatically while keeping quality in check throughout.