AI Infrastructure & APIs comparison · 2026

Snorkel AI vs Toloka

Compare Snorkel AI and Toloka as AI Infrastructure & APIs tools on fit, pricing, and the capabilities that actually overlap in 2026. Pick Snorkel AI for teams building post-training and RL datasets for coding agents evaluated on senior-level software engineering tasks and constructing runnable environments so computer-use agents can be trained and scored on long-horizon desktop and web workflows. Pick Toloka for teams producing agent trajectory data to post-train tool-using and computer-use agents and building RL gym environments with MCP replicas for agent evaluation and reinforcement learning.

Updated Aug 22, 2026

Snorkel AI
snorkel.ai

The frontier AI data lab building expert training data, benchmarks, and runnable evaluation environments

Starts at Custom pricing (quote-based)·AI Infrastructure & APIs

Toloka
toloka.ai

Expert-curated training, evaluation and red-teaming data for AI agents and LLMs

Starts at Custom quote (contact form budget bands start at under $25k)·AI Infrastructure & APIs

At a glance

Snorkel AIToloka
CategoryAI Infrastructure & APIsAI Infrastructure & APIs
PricingStarts at Custom pricing (quote-based)Starts at Custom quote (contact form budget bands start at under $25k)
Free tierNoNo
PlatformsWebWeb
Suitable forTeams building post-training and RL datasets for coding agents evaluated on senior-level software engineering tasks and constructing runnable environments so computer-use agents can be trained and scored on long-horizon desktop and web workflowsTeams producing agent trajectory data to post-train tool-using and computer-use agents and building RL gym environments with MCP replicas for agent evaluation and reinforcement learning
CompanySnorkel AI, Inc.Toloka Group, Inc.
Founded20192014

How they differ

Shared AI Infrastructure & APIs rubric, filled from each listing. Not a score.

Model catalog

Snorkel AIToloka
Text modelsNot publishedNot published
Image modelsNot publishedNot published
Video modelsNot publishedNot published
Speech modelsNot publishedNot published
Open-weight modelsNot publishedNot published
Pinned versionsYesNot published

Serving

Snorkel AIToloka
One unified APINot publishedNot published
OpenAI-compatible APINot publishedNot published
Streaming responsesNot publishedNot published
Batch jobsNot publishedNot published
Provider fallbackNot publishedNot published
Published rate limitsNot publishedNot published

Build & tune

Snorkel AIToloka
Fine-tuningLimitedYes
EmbeddingsNot publishedNot published
Vector storeNot publishedNot published
RAG pipelinesYesNot published
Agent frameworkYesYes
Tool / function callingNot publishedNot published

Observe & control

Snorkel AIToloka
Usage dashboardNot publishedNot published
Per-request costsNot publishedNot published
Traces / loggingYesNot published
EvalsYesYes
Prompt managementNot publishedNot published
Zero-retention optionNot publishedNot published

Access

Snorkel AIToloka
Public APINot publishedNot published
Official SDKNot publishedNot published
Self-serve signupNoNot published
Free trialNot publishedNot published
Team workspaceNot publishedNot published
SSO / SAMLNot publishedNot published
Mobile appsNoNot published
Browser extensionNot publishedNot published
Self-host / on-premNot publishedNot published

Commercial

Snorkel AIToloka
Commercial licenseNot publishedNot published
Usage-based pricingNot publishedNot published
Invoice / PONot publishedNot published
SOC 2Not publishedNot published
GDPR / DPANot publishedNot published
Audit logYesNot published
Role-based accessNot publishedNot published

Features

These listings describe different capabilities. What each one ships:

Only Snorkel AI

  • Snorkel Data Series: curriculum-structured datasets with rubrics, reviewer guidance, difficulty tiers, and eval slices
  • Custom data development for bespoke datasets, evals, and benchmark expansions targeting a named failure surface
  • Specialized agents built on expert data and evaluated in real workflows with pass/fail criteria
  • Well-specified expert-level task specs with target distributions, acceptance criteria, and verifier definitions written before data work begins
  • Calibrated expert review, with reviewers trained against gold sets authored by Snorkel researchers and scored for agreement and bias
  • Rubrics distilled into programmatic graders and fine-tuned evaluator models rather than human spot-checks alone
  • Author, multi-reviewer, and final-adjudicator pipeline with full audit trails and label-level provenance
  • Edge-case coverage across difficulty bands and failure modes via expert-authored seeds and templated generation

Only Toloka

  • Environments generation: context-rich simulated environments for evaluating and training agents
  • Training datasets covering specialized agentic skills
  • Evaluation and red-teaming that assesses agent performance and identifies vulnerabilities
  • Agent trajectory demonstrations and step-by-step evaluations across tool-use workflows
  • Virtual environments and RL-gyms with MCP replicas and computer-use testbeds
  • Safety red-teaming for injection vulnerabilities and policy compliance
  • Demonstrations generation for Supervised Fine-Tuning (SFT)
  • Preference collection for RLHF and Direct Preference Optimization (DPO)

Use cases

These listings describe different use cases. What each one ships:

Only Snorkel AI

  • Building post-training and RL datasets for coding agents evaluated on senior-level software engineering tasks
  • Constructing runnable environments so computer-use agents can be trained and scored on long-horizon desktop and web workflows
  • Designing rubrics and programmatic graders for open-ended outputs where correctness is hard to define
  • Standing up environment-first evaluation for agents in regulated work such as insurance underwriting or financial reasoning
  • Sourcing legal research evaluation data through partnerships like BigLaw Bench: Research with Harvey
  • Deploying a specialized enterprise agent grounded in a company's own tools, codebase, corpus, and data permissions
  • Diagnosing why an evaluation rubric breaks down using the RIFT failure mode taxonomy from Snorkel AI research

Only Toloka

  • Producing agent trajectory data to post-train tool-using and computer-use agents
  • Building RL gym environments with MCP replicas for agent evaluation and reinforcement learning
  • Collecting human preference pairs for RLHF and DPO alignment runs
  • Red-teaming assistants for prompt injection vulnerabilities and policy compliance
  • Generating repository-scale programming data for AI coding copilots
  • Sourcing domain-expert reasoning chains in regulated fields such as medicine and law
  • Running customized human evaluation of model outputs across text, image, video and audio
  • Licensing off-the-shelf datasets such as University-level Math Reasoning to bootstrap a benchmark

Plans

Snorkel AI

  • Custom engagement (quote-based) Contact sales

    No pricing page is published on snorkel.ai and no list price appears on the homepage, company page, or llms.txt · Homepage calls to action are "Request dataset samples" and "Talk to our team" rather than a signup or free trial · Scope is negotiated per dataset, benchmark, evaluation harness, environment, or specialized agent program · Snorkel Data Series, custom data development, and specialized agents are all sold through the same sales conversation · Free resources exist outside the commercial contract, including public benchmark leaderboards, research papers, and the Open Benchmarks Grants program

Toloka

  • Custom data engagement Custom quote/mo or Custom quote/yr

    Toloka publishes no pricing page, plan table or public rate card · Toloka scopes each project through the Talk to us form · Toloka contact form budget bands: under $25k, $25-50k, $50-100k, $100k-200k, $200k+ · Toloka timeline options: within a month, 1-3 months, 3-6 months, just researching · Toloka Terms of Use state that website terms do not govern paid services, which sit under separate terms

Snorkel AI strengths

  • Snorkel AI publishes its benchmarks and leaderboards openly, including Senior SWE-Bench and OSWorld 2.0, so buyers can inspect the methodology before contracting
  • Snorkel AI documents a concrete quality process covering task specs, reviewer calibration, adjudication, and label-level provenance rather than vague quality claims
  • Snorkel AI delivers runnable environments and deterministic graders alongside data, so evaluation is reproducible across model versions
  • Snorkel AI has a peer-reviewed research trail dating to the 2017 VLDB data programming paper, with 250+ publications cited on the company page
  • Snorkel AI covers coding, computer use, terminal, legal, financial, and insurance domains rather than one narrow vertical
  • Snorkel AI maintains a published llms.txt that lays out capabilities, benchmarks, and research links in a structured, checkable form

Watch-outs

  • Snorkel AI publishes no pricing page, no plan table, and no list price anywhere on snorkel.ai, so cost cannot be estimated before a sales call
  • Snorkel AI offers no self-serve signup, free tier, or free trial; the only homepage entry points are "Request dataset samples" and "Talk to our team"
  • Vendor contradiction: the Snorkel AI contact page directs users to [email protected] for help using Snorkel, while section 2.4 of the Snorkel AI website terms states the company has no obligation to provide support or maintenance
  • The Snorkel AI website terms grant only a personal, noncommercial license to the site and forbid using the site to build a similar or competitive product
  • The Snorkel AI website terms cap total liability arising from site use at fifty US dollars (US $50) and require individual binding arbitration with a class action waiver
  • The Snorkel AI website terms are still labeled Version 1.0, last updated July 11, 2020, which predates the current frontier-data positioning on the site
  • Snorkel AI works through an embedded delivery model with recruited domain experts, so turnaround depends on expert calibration rather than instant API access
  • Snorkel AI states no minimum engagement size, dataset volume, or contract length publicly, leaving small teams unable to judge fit without contacting sales

Toloka strengths

  • Toloka documents concrete agent-specific deliverables such as RL-gyms with MCP replicas and computer-use testbeds, not generic labeling
  • Toloka names its quality methodology explicitly: post-verification, dynamic overlaps, cross-validation, golden sets, 50+ automated QC methods
  • Toloka reports expert coverage across 50+ knowledge domains and 120+ subdomains, useful for medicine, law and other specialized work
  • Toloka publishes security posture details including ISO 27001, ISO 27701, SOC 2, GDPR, CCPA and HIPAA compliance plus on-premises storage options
  • Toloka sells off-the-shelf datasets and Toloka Arena alongside custom projects, giving smaller teams an entry point

Watch-outs

  • Toloka publishes no pricing page, plan table or unit rates, so no cost can be estimated before a sales call
  • The Toloka contact form's lowest budget band is under $25k, which signals a project floor unsuitable for small experiments
  • Vendor contradiction: the Toloka footer credits Toloka AI BV while the Toloka Terms of Use define Toloka as Toloka Group, Inc. of Wilmington, Delaware, and the privacy notice applies to Toloka AI B.V.
  • Vendor contradiction: Toloka's website Terms of Use explicitly state they do not govern paid services, so the terms a buyer actually signs with Toloka are not published
  • Toloka work is delivered as a managed service, so buyers cannot self-serve a project without contacting Toloka
  • Toloka's Eligibility and Geographic Restrictions policy and terms warn that access may not be lawful in some countries, limiting where contributors and buyers can participate
  • Toloka's privacy notice states that contributor names, emails and Slack, Jira or Git handles can appear inside delivered coding task materials shared with clients
  • Toloka's website terms cap total liability for site-related claims at US $100, and paid-service liability is negotiated separately

Snorkel AI vs Toloka verdict

Who each product is for, then labeled AI takes. Not a generic winner.

Bottom line

Pick Snorkel AI for teams building post-training and RL datasets for coding agents evaluated on senior-level software engineering tasks and constructing runnable environments so computer-use agents can be trained and scored on long-horizon desktop and web workflows. Pick Toloka for teams producing agent trajectory data to post-train tool-using and computer-use agents and building RL gym environments with MCP replicas for agent evaluation and reinforcement learning.

Who Snorkel AI is for

Teams building post-training and RL datasets for coding agents evaluated on senior-level software engineering tasks and constructing runnable environments so computer-use agents can be trained and scored on long-horizon desktop and web workflows

The frontier AI data lab building expert training data, benchmarks, and runnable evaluation environments

Who Toloka is for

Teams producing agent trajectory data to post-train tool-using and computer-use agents and building RL gym environments with MCP replicas for agent evaluation and reinforcement learning

Expert-curated training, evaluation and red-teaming data for AI agents and LLMs

AI take on Snorkel AI

As of August 2026, Snorkel stands out for environment-first agent evaluation, though buyers must validate scope, timing, and commercial terms through sales.

AI take on Toloka

Toloka stands out for agent trajectories and RL environments, but the managed-service model demands procurement tolerance and careful contracting.

Snorkel AI vs Toloka FAQ

Common questions when choosing between Snorkel AI and Toloka.

Is Snorkel AI or Toloka the better AI Infrastructure & APIs tool?

Pick Snorkel AI for teams building post-training and RL datasets for coding agents evaluated on senior-level software engineering tasks and constructing runnable environments so computer-use agents can be trained and scored on long-horizon desktop and web workflows. Pick Toloka for teams producing agent trajectory data to post-train tool-using and computer-use agents and building RL gym environments with MCP replicas for agent evaluation and reinforcement learning.

Which is cheaper, Snorkel AI or Toloka?

Snorkel AI starts at Custom pricing (quote-based). Toloka starts at Custom quote (contact form budget bands start at under $25k). Confirm current pricing on each vendor site.

Who should choose Snorkel AI?

Teams building post-training and RL datasets for coding agents evaluated on senior-level software engineering tasks and constructing runnable environments so computer-use agents can be trained and scored on long-horizon desktop and web workflows

Who should choose Toloka?

Teams producing agent trajectory data to post-train tool-using and computer-use agents and building RL gym environments with MCP replicas for agent evaluation and reinforcement learning

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