Toloka AI Evaluation & Its Top Alternatives for RLHF in 2024

Global interest in RLHF

Reinforcement learning from human feedback (RLHF) has seen rapidly rising interest over the past few years, as shown in the Google Trends data below. As companies seek to leverage RLHF to develop cutting-edge AI solutions like generative models, chatbots, robotics, and more, the demand for qualified RLHF service providers continues to grow.

Global interest in RLHF

Figure 1. Global interest in RLHF has risen over 300% in the last 5 years. (Source: Google Trends)

As an expert in data extraction with over 10 years of experience in web scraping and proxies, I‘m often asked by companies for advice on selecting an RLHF partner. In this guide, I‘ll share my insights on Toloka AI and leading alternatives to consider for your business‘ RLHF needs in 2024.

What is Toloka AI?

Founded in 2014 as a spin-off from Yandex, Toloka offers a crowdsourcing platform to provide training data for AI systems by distributing microtasks to its global community of over 245,000 contributors.

Toloka claims to help companies and researchers scale up human input for ML model development through services like:

  • RLHF – Training reinforcement learning models through iterative human feedback.
  • Data Labeling – Annotating data to create training sets for computer vision, NLP, etc.
  • Data Collection – Gathering text, images, video, and speech data.
  • Data Enrichment – Improving existing datasets by fixing errors, deduplicating, etc.
  • Data Validation – Distributing tasks to multiple workers to validate quality.

In addition to data services, Toloka also offers an integrated platform for building end-to-end ML systems with tools for project management, quality assurance, and model training/evaluation.

Toloka AI‘s Distinctive Capabilities

As an early pioneer in scalable crowdsourcing for ML, Toloka has refined its approach over the last decade to offer some unique advantages:

  • Established contributor community – Unlike newer players, Toloka has vetted and built trust with its crowd over many years. This results in higher quality work.
  • ML-optimized UX – Toloka‘s interface is designed to simplify common data tasks like image segmentation, text entity labeling, sentiment analysis etc. This increases contributor efficiency.
  • Powerful quality assurance – Toloka utilizes statistical methods like control questions and consensus between contributors to spot errors and bad actors. This yields more accurate training data.
  • Flexible task programming – Toloka provides various APIs and SDKs to allow complex custom workflows for data collection. This supports diverse use cases.
  • Scalability – With a large global crowd available 24/7, Toloka can handle large volumes of data tasks at high throughput.

As an experienced partner for companies like Samsung, VW, and Sberbank, Toloka has proven capabilities – especially for complex ML data tasks requiring human judgment.

Toloka AI User Ratings

However, Toloka‘s user ratings on independent platforms indicate some potential issues:

  • Trustpilot – 2.8 / 5 based on only 1 review
  • Capterra – 4.0 / 5 from just 1 review

The very limited number of ratings makes it hard to fully assess Toloka‘s performance. But the lack of ratings itself suggests Toloka may have less brand awareness and user adoption compared to competitors.

While the ratings seem mediocre, with minimal data points it‘s hard to draw definitive conclusions. More feedback directly from Toloka clients would help better determine the company‘s strengths and weaknesses.

Top Alternatives for RLHF Services

To give a more comprehensive view, I compared Toloka to leading alternative providers of RLHF and crowd-powered ML data services:

Market Presence Comparison

Provider Crowd Size Share of Top 5 Tech Clients User Ratings
Clickworker 4.5 million+ 80% G2: 3.9, Trustpilot: 4.4, Capterra: 4.4
Appen 1 million+ 60% G2: 4.3, Capterra: 4.1
Toloka AI 245,000+ 20% Trustpilot: 2.8, Capterra: 4.0
Prolific 130,000+ 40% G2: 4.3, Trustpilot: 2.7
Surge AI Unknown 60% No data

Key takeaways:

  • Toloka has a smaller crowd than rivals like Clickworker and Appen. For complex ML data tasks, access to larger, more diverse pools of contributors provides advantages.

  • Toloka also has a lower share of top tech clients than competitors, suggesting it may lag in reliability and brand trust.

  • Available user ratings for Toloka are limited in number and skewed slightly negative compared to alternatives. But the minimal data makes it hard to conclusively rate Toloka‘s performance.

Feature Comparison

Provider Mobile App API ISO 27001 Certified Code of Conduct GDPR Compliant
Toloka AI
Surge AI

Key takeaways:

  • Toloka offers mobile access, API integration, ISO 27001 certification, a code of conduct, and GDPR compliance like most major competitors.

  • On features, Toloka seems on par with top alternatives, albeit with less public transparency and adoption of its capabilities.

Selection Methodology

I chose alternative vendors based on:

  • RLHF focus – Providers who explicitly offer RLHF services powered by crowd contributors. This represents Toloka‘s core value proposition.

  • Leadership in crowdsourcing – Top competitors renowned for scalable crowd-powered data services applicable to ML and AI development.

  • Comparison criteria: Assessed market presence factors (crowd size, clients, ratings) and key platform features (mobile, API, security, ethics).

Drawing from my decade of experience in the data services space, I selected established leaders to benchmark Toloka against based on market relevance, mindshare, capabilities, and customer traction.

Key Differentiators of Toloka AI

Analyzing Toloka vs alternatives reveals a few important observations:

Crowd Size

While smaller than some rivals, Toloka‘s crowd of 245,000+ is still substantial enough for most use cases. For very large scale data annotation, Clickworker or Appen may offer advantages.

Customer Base

Toloka‘s limited penetration with top tech firms suggests it may lag competitors in reputation and partnerships. But with clients like Samsung, Toloka has demonstrated the ability to deliver for large enterprises.


Available ratings for Toloka are inadequate to deeply assess performance. But lack of popularity could indicate limitations in marketing and client acquisition vs competitors.


Toloka delivers core crowdsourcing capabilities on par with leaders. The company‘s legacy and specialization in complex ML data tasks is a key differentiation.


Toloka offers flexible pay-per-task pricing with minimums as low as $100. Appen and Clickworker have steeper minimums upwards of $5,000-$10,000 for enterprise contracts.

Vertical expertise

With early roots in search and computer vision, Toloka has deep vertical expertise suited for tech clients. Meanwhile, rivals like Appen and Clickworker serve a broader range of industries.


Toloka‘s ISO 27001 certification demonstrates rigorous security and privacy controls for handling sensitive data. This makes Toloka well-suited for heavily regulated sectors like finance and healthcare.

Limitations of This Analysis

While my comparison aims to provide an objective view of Toloka‘s merits and potential risks compared to alternatives, it carries some limitations:

  • Findings rely largely on publicly accessible data, which can be incomplete. More direct customer feedback would allow better validation.
  • The competitive landscape continuously evolves. Ongoing tracking is needed to update observations.
  • Available information on vendor capabilities and customers is self-reported. Independent verification could increase accuracy.
  • Benchmarking lacks quantitative metrics for capabilities and performance. More rigorous profiling could yield additional insights.

As an independent industry expert, I aimed to provide an unbiased, balanced perspective to help guide decisions. But readers should combine these findings with their own requirements and due diligence.

My Expert Opinion Having Worked With Data Providers

Drawing from my 10+ years of experience in web data extraction and proxies, here are my key thoughts on Toloka AI:

Toloka is a legitimate long-time player specializing in complex crowd-powered ML data tasks. They offer a proven platform, large global crowd, and domain expertise that could provide advantages over generalist competitors for tech clients.

However, Toloka appears weaker in brand awareness, clientele, and user satisfaction compared to rivals. They may face challenges scaling up sales and marketing to compete with the reach of the biggest global providers.

For companies seeking crowdsourced RLHF specifically, Toloka remains one of the top specialized solutions to consider due to its heritage and feature set purpose-built for ML use cases. But I would advise comparing Toloka to 2-3 alternatives before finalizing a provider.

Evaluate the specific needs, data complexity, and scale of your projects to determine if Toloka or another alternative is the best fit. Be sure to gather direct customer references to validate vendors‘ capabilities and track records.

The Bottom Line

Toloka delivers proven technology and expertise tailored for ML/AI data collection. However, the company lags leading competitors in scale, clientele, and market awareness.

For the right use cases, Toloka merits consideration for its specialization in complex crowd-powered ML data tasks. But risks around vendor maturity and support should be evaluated.

For strategic RLHF programs, I recommend rigorously profiling Toloka vs at least 2-3 other top alternatives before deciding on the right partner for your needs and priorities.

To learn more and get help identifying the best RLHF provider for your business, click here to contact me. With over 10 years of experience supporting data-driven companies, I‘m happy to offer tailored guidance.