List of Faiss Customers
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Since 2010, our global team of researchers has been studying Faiss customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Faiss for AI Frameworks and Libraries from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Faiss for AI Frameworks and Libraries include: Amazon Web Services, a United States based Communications organisation with 130000 employees and revenues of $107.60 billion, Grab, a Singapore based Professional Services organisation with 11267 employees and revenues of $2.80 billion, Hugging Face, a United States based Professional Services organisation with 500 employees and revenues of $50.0 million and many others.
Contact us if you need a completed and verified list of companies using Faiss, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The Faiss customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight | Insight Source |
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Amazon Web Services | Communications | 130000 | $107.6B | United States | Meta | Faiss | AI Frameworks and Libraries | 2025 | n/a | In 2025, Amazon Web Services exposed Faiss as an engine option within Amazon OpenSearch Service k-NN and vector search capabilities, extending that option to OpenSearch Serverless vector collections to support large scale approximate nearest neighbor retrieval. AWS documentation and public blogs in the US region present Faiss under the AI Frameworks and Libraries category and position the library specifically for retrieval augmented generation and semantic search workloads. Faiss is surfaced as the vector indexing and ANN engine, with configuration choices that include index types, quantization settings, and performance tuning commonly associated with Faiss deployments. These capabilities align with standard AI Frameworks and Libraries functions, enabling dense vector indexing, approximate nearest neighbor search, and model-agnostic vector ingestion pipelines within OpenSearch Service. Integration points described in AWS guidance include Amazon OpenSearch Service serverless vector collections and examples that pair Faiss based retrieval with Amazon SageMaker JumpStart inference and model provisioning workflows. Documentation emphasizes engine selection at the OpenSearch layer, and shows how Faiss integrates into the search stack for RAG workflows, linking model outputs to vector collections managed inside OpenSearch. Governance and rollout are driven through AWS published guidance and examples released in 2025, which provide configuration patterns and quantitative option guidance for customers choosing Faiss. The published materials focus on configuration, operational integration with OpenSearch Service, and deployment patterns for customers adopting Faiss as their AI Frameworks and Libraries solution for vector retrieval and semantic search. | |
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Grab | Professional Services | 11267 | $2.8B | Singapore | Meta | Faiss | AI Frameworks and Libraries | 2024 | n/a | In 2024 Grab implemented Faiss as part of its data and machine learning stack, using AI Frameworks and Libraries tooling to power vector similarity search shortlisting. The work was regionally scoped with internal deployment in Southeast Asia and was documented in an engineering post published October 23, 2024. Faiss functioned as the shortlist index and retrieval layer to surface candidate vectors for downstream ranking. Grab configured Faiss to produce compact shortlists that were then re ranked by an internal large language model, creating an LLM assisted retrieval pipeline described in the engineering write up. The implementation emphasized vector similarity search, index management and shortlisting capabilities, and integration with Grab's internal LLM re ranker and broader data and ML stack for complex, nuanced queries. The engineering post explicitly reported improved retrieval quality when Faiss shortlists were re ranked by the LLM. | |
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Hugging Face | Professional Services | 500 | $50M | United States | Meta | Faiss | AI Frameworks and Libraries | 2022 | n/a | In 2022, Hugging Face integrated Faiss into its Datasets library to provide fast nearest neighbor indexes for semantic search and Retrieval Augmented Generation workflows across its developer platform. This integration situates Faiss within the AI Frameworks and Libraries layer, and it was implemented to support ML engineering use cases focused on vector search and retrieval based augmentation. The implementation exposes a public API, Datasets.add_faiss_index, which creates and persists Faiss indexes directly from dataset objects, enabling in library index creation, queryable nearest neighbor search, and embedding based retrieval pipelines. Faiss is used as the core vector index engine while the Datasets.add_faiss_index API manages index lifecycle, serialization, and access patterns that are consistent with library oriented workflows. Operationally the rollout targeted Hugging Face's global developer audience, with the Datasets.add_faiss_index API documented in the Hugging Face docs and observed in repository issues and community usage from 2022 onward. Governance for the capability is community facing, with public documentation and repository issue activity driving adoption, troubleshooting, and iterative enhancements rather than private enterprise change control. |
Buyer Intent: Companies Evaluating Faiss
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