Platform for AI engineers
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.
Trusted by Leading AI Teams
Toloka's Flywheel
AI has moved past static datasets. Frontier labs are racing to the next benchmark while enterprises struggle to put models into production, and both need the same infrastructure to evaluate, train, and improve models in the real world. Toloka builds that foundation: environments, test cases, graders, and expert data for training agentic AI and adapting it to enterprise workflows.
Evaluation → Building human data → Training → Evaluation. Every frontier and enterprise project feeds the same system: environments get reused, judges get sharper, playbooks accumulate. While customer contexts and data types change, our core infrastructure compounds.
How to choose
Each stage below offers the same three routes. Which one fits depends on how specific your requirements are and how soon you need to start.
Managed service
Use when your requirements are specific to you, or when the work covers the whole loop rather than a single job. Our team scopes it, builds it and runs it alongside yours.
Self-service
Use when your team already knows what it needs. You set the job up in the platform and results come back in 24 to 48 hours.
Off-the-shelf
Use when you want to start now. The data already exists, so there is nothing to collect or label first.
Stage 1: Evaluation
Don't know how good your model is? Find out before your users tell you the hard way
An LLM judge gives you a number quickly. Human review is what makes that number hold up when a decision depends on it.
Managed service
Our team builds the evaluation with you: the task rubric, the graders, blind expert review, and the weekly checks that keep it accurate once the model is live.
Use when the criteria are yours alone and have to be worked out from scratch.
Self-service human + synthetic annotation
Send your model's outputs to Toloka's domain experts and get them scored. An LLM pass takes the straightforward cases first, so expert time goes to the ones that need judgment. Covers LLM-as-a-judge, human evaluation and golden datasets.
Use when you know what to measure and want a number this week.
Toloka Arena
A private benchmark of agentic tasks across seven business domains, run under identical conditions. Check how frontier models rank on reliability, or submit your own.
Use when you are choosing between models rather than improving one.
Stage 2: Building human data
Need training or eval data you don't have yet?
Traces you already have can become training data. What you don't have can be collected, or taken from a set that exists already.
Managed service
RL environments and RL data, multi-stage pipelines, and collection to your own protocol, including on-site sessions with participants in the room.
Use when the data has to be built rather than gathered.
Self-service human + synthetic annotation
Toloka's experts label and correct your data. An LLM pass handles the clear cases; people take the difficult and ambiguous ones. You get back a versioned labeled set. Audio, speech and egocentric video collection run the same way.
Use when your labeling guidelines are settled.
Off-the-shelf library (OTS)
Datasets already collected, cleared and ready to use, across domains, languages and modalities. Priced by the hour, with volume tiers.
Use when something close enough already exists.
Stage 3: Training
Already shipping and paying too much?
Serve the same task for less
Bring your data, pick a model tier and a budget. You get back a smaller model or a compressed prompt, and the evaluation results to compare against what you run today.
Fine-tuning
A LoRA adapter trains on a frozen open base model, so a smaller model handles your task at open-model serving cost. Works from a frontier model or from an open one you already host.
Use when the task is narrow and you run it often.
Gisting
A long static instruction prefix is compressed into a handful of learned tokens, so every later request carries a fraction of the input.
Use when the same long prompt goes out on every call.
Results, scoped to the workload that produced them
Two measured runs. Both are examples rather than guarantees. What the platform gives you is the same measurement on your own workload.
Fine-tuning
A narrow CV-parsing task moved off a frontier API onto a small open model.
12×–37×
cheaper inference than the frontier API on the same task
0.94 F1
on the human-labeled golden set, up from 0.85
A few days
from data curation to a deployed endpoint
How we used it ourselves. This is Toloka's own pipeline, parsing CVs for Mindrift, our contributor network. Our project, not a client's.
Prompt Gisting
A 5,000-token static instruction prefix on a Qwen base model.
5.3×
fewer prompt tokens per request
−15%
GPU cost per sample, at any $/GPU-hour
+18%
throughput under saturated serving
One production workload. The throughput gain grows with concurrency, reaching 1.8× at 256 concurrent requests in the same benchmark.
You see the price before you spend anything
Two ways to buy. Both are priced on what the work actually uses.
The platform
Expert work
Per item, quality control included.
Fine-tuning, gisting
Per GPU-hour used.
Platform
A percentage on top.
An estimate appears during setup, before you commit to a run.
Managed service
Scoped per engagement: a setup fee for the team, data production priced per task by complexity, a compute block for training, and a share tied to measured improvement against a benchmark agreed up front. That share is capped.
Continuing work moves to a monthly retainer covering the monitoring and evaluation software we provision, plus the experts doing ongoing annotation.
Pricing against a benchmark is possible because the evaluation is built first.
Build with agents
Bring Toloka into your coding workflow
Fine-tune, compress prompts, collect or label data from the tools your team already uses. Paste the line below into your coding agent.
Results on your own workload within days.
Built, not staffed
Most of the category resells expert hours, but the market has moved on to complex RL environments that are built, not staffed. Both frontier labs pushing model capabilities and enterprises deploying in production require reliable systems that guarantee model outcomes over time. Toloka delivers that foundation.
10+ years
of frontier-lab data programs
50+
automated quality-control methods
60+
antifraud methods
90+
domains of expertise
70%+
people with advanced degrees
6000+
active contributors
Data handling
We don't train on your data. You own the artefacts, and people see your items only on tasks where you asked for human verification.
SOC 2 Type II
ISO 27001
ISO 27701
GDPR
Microsoft Azure base infrastructure, with private and on-premise storage available.
Need another tool?
New tools are being added to the platform. Join the waitlist to be the first to know what's new
Toloka Platform in production
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.