Embeddings and RAG (retrieval-augmented generation) form the plumbing that lets AI systems answer from your data instead of guessing. Embedding models convert text, images, and code into vectors that capture meaning; retrieval systems find the most relevant vectors for a query; rerankers reorder results for precision; and the retrieved context is handed to an LLM to ground its answer. The buyers are AI engineers building assistants, search features, and agents — and increasingly platform teams standardizing retrieval for a whole company.
The category spans three layers. Model providers like Voyage AI, Jina AI ($37M raised), and Mixedbread compete on retrieval-benchmark accuracy, multilingual coverage, and multimodal support. Infrastructure players like Pinecone ($228M), Qdrant ($50M), and Chroma ($20M) store and search vectors at scale. And application-layer companies such as Contextual AI ($100M) package the whole pipeline into specialized enterprise RAG agents, while Nomic AI pairs open-source embeddings with data-mapping tools.
Leaders separate on retrieval quality under real conditions, not leaderboard scores: how well does the stack handle messy PDFs, tables, domain jargon, and multi-hop questions? Hybrid retrieval — combining vector, keyword, and metadata search with reranking — consistently beats vectors alone, so favor tools that support it natively. Buying considerations include embedding-model swap costs (re-indexing an entire corpus isn't free), latency at your query volume, whether you need self-hosted open source or a managed API, and per-token versus per-query pricing. NeuronFeed tracks 15 companies in this category with $581M in combined funding.