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Data as of Oct 18, 2026Based on 64 AI responses
Reviewed by Dimitry Apollonsky
AI summary
Cohere holds a clear lead, recommended primarily for domain adaptation and fine-tuning embedding models. Amazon SageMaker is the usual answer when buyers require platforms that train on bucket data while keeping ownership of model weights.
Rank and Mentioned in · since Jul 8
| # | Brand | Recommended for | Mentioned in |
|---|---|---|---|
| 1 | fine-tuning services and embedding models adaptable to specialized domain vocabularies | fine-tuning services and embedding models adaptable to specialized domain vocabularies | 59% |
| 2 | high-performance, domain-specific embedding models tailored for legal and financial vocabulary | high-performance, domain-specific embedding models tailored for legal and financial vocabulary | 50% |
| 3 | an open-source Python library for fine-tuning embeddings on industry text | an open-source Python library for fine-tuning embeddings on industry text | 30% |
| 4 | consultancy services adapting text embedding models and retrieval systems to niche terminology | consultancy services adapting text embedding models and retrieval systems to niche terminology | 23% |
| 5 | enterprise search contexts compared against customized domain-adaptation consulting | enterprise search contexts compared against customized domain-adaptation consulting | 23% |
| 6 | open-source model libraries and expert support for custom domain adaptation | open-source model libraries and expert support for custom domain adaptation | 22% |
| 7 | 19% | ||
| 8 | hosting open-source embedding models used for direct fine-tuning workflows | hosting open-source embedding models used for direct fine-tuning workflows | 17% |
| 9 | long-context embedding models suitable for fine-tuning on technical documentation | long-context embedding models suitable for fine-tuning on technical documentation | 16% |
| 10 | 14% | ||
| 11 | consulting services tailoring embedding pipelines for legal, finance, and healthcare jargon | consulting services tailoring embedding pipelines for legal, finance, and healthcare jargon | 14% |
| 12 | broad fine-tuning ecosystems often compared with specialized domain embedding providers | broad fine-tuning ecosystems often compared with specialized domain embedding providers | 14% |
| 13 | 13% | ||
| 14 | 11% | ||
| 15 | 11% |
Week of Aug 24–30 · Since Jul 5
Widest split: Voyage AI, 64% on ChatGPT Search and 35% on Google AI Mode.
| Brand | ChatGPT Search | Google AI Mode | Comparison |
|---|---|---|---|
| Cohere | 64% | 55% | |
| Voyage AI | 64% | 35% | |
| Sentence-Transformers | 21% | 39% | |
| Modulai | 36% | 10% | |
| Glean | 21% | 26% | |
| Hugging Face | 27% | 16% | |
| Google Cloud Gemini Enterprise Agent Platform | 18% | 19% | |
| Hugging Face Hub | 15% | 19% | |
| Jina AI | 15% | 16% | |
| Amazon SageMaker | 15% | 13% |
58% of citations point at the ranked brands’ own sites. youtube.com, arxiv.org, medium.com are the most-cited sites outside the ranked brands’ own domains.
Market map
Mentioned in · since Jul 8
Leading brands by topic
Our semantic search isn't picking up on industry-specific jargon. Can you recommend services that specialize in domain-adapting embedding models?
I want to fine-tune an embedding model on my own text data for better domain adaptation. What's the easiest platform or library for fine-tuning sentence transformers?
I am looking for a fine-tuning platform that uses my bucket data and does not take ownership of the final model weights.
