Embedding models companies, products & suppliers
Models that turn text or media into vectors for retrieval, not generative chat. Jobs: text embeddings; multilingual embeddings; multimodal embeddings.
What are Embedding models?
Models that turn text or media into vectors for retrieval, not generative chat. Jobs: text embeddings; multilingual embeddings; multimodal embeddings.
What problems does it solve?
Retrieval quality is capped by a generic embedding the buyer cannot name.
Typical business use cases
- Text embeddings
- Multilingual embeddings
- Multimodal embeddings
Important capabilities
- Dimension
- MTEB or domain evals
- Hosting
What buyers should evaluate
- Domain fit
- Licence
- Whether vectors leave the tenancy
Risks and governance considerations
Embeddings of confidential text stored in a third-party index.
Procurement checklist
- Model id
- Index location
- Retention
Relevant AI Trustmark assurance
AI Trustmark independent findings appear only when an assessment or certificate exists. Category membership does not imply verification.
Companies and providers
Claimed suppliers appear first so buyers can start with listings the company has taken ownership of. Payment does not buy this order.
Amazon Web Services is Amazon's cloud division selling compute, storage, Bedrock model hosting and related AI infrastructure. Public pages cover regional cloud services for builder
Cohere is a Toronto-headquartered model company selling text, embed and RAG-oriented APIs for enterprises. Public pages emphasise private deployment options alongside hosted APIs.
Google is a US technology company that sells consumer and cloud AI products, including Gemini, Search, and related developer APIs on Google Cloud. Google publishes Imagen, Gemini f
International Business Machines Corporation sells hybrid-cloud software, Granite models and enterprise AI tooling. Public pages distinguish IBM software and models from IBM Consult
Mistral AI is a Paris-headquartered model company selling open-weight and hosted language models. Public pages cover APIs, model licences and enterprise deployments. Mistral AI pub
Make your agents work on your data. Mixedbread turns PDFs, decks, videos, code, Slack, and Drive into evidence your agent can act on, with fewer tokens. Mixedbread publishes Wholem
NVIDIA sells GPUs, CUDA software and AI enterprise stacks used for training and inference. Public pages cover data-centre, cloud and on-prem AI compute. NVIDIA publishes NVIDIA cuO
OpenAI is a US AI research company that sells hosted assistants, APIs and developer tools, including ChatGPT and the OpenAI API. OpenAI publishes ChatGPT Work, omni-moderation, and
Voyage AI provides cutting-edge embedding models and rerankers for search and retrieval. Voyage AI publishes voyage-4-large, voyage-code-4, and voyage-finance-2 as named AI product
Products
Claimed products appear first. Ranking packs and payment do not change this list.
- Cohere Embed· Cohere
- EmbeddingGemma· Google
- Gemini Embedding 2· Google
- Granite Embedding 311M Multilingual R2· IBM
- Mistral Embed· Mistral AI
- Nemotron-3-Embed-1B-BF16· NVIDIA
- text-embedding-3-large· OpenAI
- Titan Text Embeddings V2· Amazon Web Services
- voyage-4-large· Voyage AI
- Wholembed v3· Mixedbread
Related categories
Relevant procurement and assurance guides
Frequently asked questions
What is Embedding models?
Models that turn text or media into vectors for retrieval, not generative chat. Jobs: text embeddings; multilingual embeddings; multimodal embeddings.
What should not be listed as Embedding models?
Products whose buyer job is LLMs, vector databases, or semantic-search products. Those belong on their own category page so search queries are not split.
Has AI Trustmark independently assessed every Embedding models supplier?
No. A category listing is descriptive. Independent assessment is shown only on company or product pages that carry Trustmark evidence.
What are Embedding models?
Models that turn text or media into vectors for retrieval, not generative chat. Jobs: text embeddings; multilingual embeddings; multimodal embeddings.
Why do buyers need to know which model providers sit behind an AI product?
Most products depend on named models or APIs. Buyers should identify those providers, where data is sent, and who is accountable when a model version changes. A supplier that cannot name material model providers is not ready for high-assurance procurement.
How can buyers tell whether customer data is used to train models?
Ask whether prompts, files, logs or outputs are used to train, fine-tune or evaluate models, including by subprocessors. Require the contractual default, any opt-out, and whether the setting can be changed silently. Treat marketing 'we do not train' claims as unverified until evidenced.
What model or provider changes should a buyer insist on being told about?
Material change usually includes a new model family, new region, new subprocessor, new write-capable tool, or a change that affects logging, privacy or human oversight. Those changes should trigger evidence refresh rather than a silent release.
How should buyers verify where AI customer data is processed?
Ask for the named processing locations, cloud regions and any subprocessors that see prompts, files or outputs. A directory listing is not evidence of residency. Independent assessment records the locations that were in scope on the assessment date.