Data as of Aug 25, 2026 · Based on 1,661 AI responses · See how Parse measures this
RLHF Data Collection & Training Platforms
Parse
https://parse.gl
Scale holds the top-ranked position for RLHF platforms, reflecting a market-wide shift where AI assistants increasingly favor specialized, managed data service providers over general-purpose tools. This trend has elevated vendors like and , while early open-source and platform leaders have seen their rankings decline since late 2025.
| # | Brand | What AI says | Mention rate |
|---|---|---|---|
| 1 | High-profile specialist providing curated human annotators for complex preference ranking tasks. | 51% | |
| 2 | A leading vendor for managed RLHF, offering data pipelines and continuous alignment services. | 48% | |
| 3 | Major open-source ecosystem, primarily cited for its widely-used TRL training library. | 44% | |
| 4 | Data management platform frequently mentioned for building human preference datasets for RLHF. | 39% | |
| 5 | 33% | ||
| 6 | Enterprise-grade annotation platform with workflows customized for RLHF feedback loops. | 30% | |
| 7 | Open-source annotation tool with templates designed specifically for pairwise preference collection. | 29% | |
| 8 | A fast-rising crowdsourcing platform for scalable human preference data collection. | 29% | |
| 9 | 28% | ||
| 10 | Provides | 23% | |
| 11 | 21% | ||
| 12 | 16% | ||
| 13 | 14% | ||
| 14 | 14% | ||
| 15 | 13% | ||
| 16 | 13% | ||
| 17 | 12% | ||
| 18 | 12% | ||
| 19 | 12% | ||
| 20 | 10% | ||
| 21 | 10% | ||
| 22 | 10% | ||
| 23 | 9% | ||
| 24 | 9% | ||
| 25 | 8% |
Who wins on each AI
The same market, seen by two models.
Sources AI cited
aws.amazon.com is the page AI reaches for most here, cited in 36% of analyzed answers.
Rose from rank #8 in Oct 2025 to #1 by Mar 2026.
“A general platform for RLHF” → “The ecosystem providing the TRL library for training”
Dropped from rank #2 to #7 between Oct 2025 and Mar 2026.
Jumped from rank #62 to #4 between Oct 2025 and Mar 2026.
| Brand | ChatGPT Search | Google AI Mode | Comparison |
|---|---|---|---|
| 71% | 68% | ||
| 50% | 43% | ||
| 50% | 37% | ||
| 38% | 37% | ||
| 40% | 23% |
The two models disagree most about Weights & Biases (ChatGPT #12, Google #24) and Mercor (ChatGPT #21, Google #13).
Scale holds the top-ranked position for RLHF platforms, reflecting a market-wide shift where AI assistants increasingly favor specialized, managed data service providers over general-purpose tools. This trend has elevated vendors like Scale and Surge AI, while early open-source and platform leaders have seen their rankings decline since late 2025.
Across 1,661 AI responses, Surge AI is mentioned most, named in 51% of them, followed by Scale (48%) and Hugging Face (44%).
Parse measures each brand's mention rate — the share of answers naming it — across 1,661 AI responses to this market's buyer questions. Answers are collected daily and the ranking is published weekly.
Brands enter the ranking when AI answers mention them. Parse collects answers daily and publishes the re-measured set weekly, so new brands appear as AI starts recommending them.
AI responses initially cited a mix of open-source frameworks like trlX and platforms like Labelbox. By early 2026, answers bifurcated, with ChatGPT focusing on open-source libraries like
Hugging Face TRL while Google AI Overviews highlighted managed data platforms such as
Scale and
Argilla. This reflects a broader trend of distinguishing between training software and data collection services.
Brands mentioned
AI responses initially cited a mix of open-source frameworks like trlX and platforms like Labelbox. By early 2026, answers bifurcated, with ChatGPT focusing on open-source libraries like
Hugging Face TRL while Google AI Overviews highlighted managed data platforms such as
Scale and . This reflects a broader trend of distinguishing between training software and data collection services.
Early answers in late 2025 favored Hugging Face and
Amazon SageMaker. Through early 2026, recommendations evolved to present a choice between specialized data providers like and for preference collection and open-source frameworks like and for the training component. This shows an increasing sophistication in understanding the RLHF workflow as a multi-part stack.
Brands mentioned
Early answers in late 2025 favored Hugging Face and
Amazon SageMaker. Through early 2026, recommendations evolved to present a choice between specialized data providers like
Surge AI and
Toloka for preference collection and open-source frameworks like
OpenRLHF and
Hugging Face TRL for the training component. This shows an increasing sophistication in understanding the RLHF workflow as a multi-part stack.