Embeddings

The mathematical translation that helps machines understand meaning — and the technology powering every modern AI application in your enterprise.

Embeddings are the unsung workhorse of the AI revolution. Every time your search engine finds a relevant document, your recommendation system suggests a product, or your AI chatbot understands a customer's intent, embeddings are working behind the scenes. The vector database market — purpose-built to store and query embeddings — grew from $300 million in 2022 to over $3 billion in 2025, reflecting the explosive demand for embedding-powered AI applications. For business leaders, understanding embeddings is essential for making informed decisions about AI investments, search capabilities, and data infrastructure.

What It Is

An embedding is a mathematical representation of data — text, images, audio, or any other information — as a list of numbers (a vector). These numbers capture the semantic meaning of the data in a way that computers can process. Words with similar meanings have similar embeddings — the embedding for "car" is numerically close to "automobile" and "vehicle," but far from "banana" or "democracy." This allows machines to understand relationships between concepts without explicit programming.

The business analogy is a highly sophisticated filing system. Imagine you could assign every document, product, customer profile, and support ticket a unique set of coordinates on a massive multi-dimensional map, such that similar items are placed near each other. A customer complaint about "billing errors" would be filed near other billing-related queries, near fraud reports, and near payment processing issues — but far from product feature requests or shipping complaints. Embeddings create this map automatically, at scale, for millions or billions of items.

The power of embeddings extends beyond text. Image embeddings enable visual similarity search (finding products that look similar to a customer's uploaded photo). Audio embeddings power voice recognition and music recommendation. Even cross-modal embeddings exist — allowing an AI to find images that match a text description, which is the technology behind AI image generation and visual search features.

Why It Matters for Business

Embeddings transform three critical business capabilities: search, personalisation, and AI application development. Traditional keyword search fails when users describe what they want using different words than what appears in the document. Embedding-based "semantic search" understands that a search for "how to cancel my subscription" should return results about "ending your membership" or "account closure procedures" — even when those exact keywords do not appear. Companies deploying semantic search report 30–50% improvements in search relevance and 15–25% increases in content engagement.

Personalisation powered by embeddings is what distinguishes leaders like Spotify and Netflix. Spotify's recommendation algorithm uses embeddings to understand that a user who enjoys "relaxing acoustic music for Sunday mornings" might also enjoy a specific jazz playlist — not because of keyword overlap, but because the embedding space captures the mood, tempo, and instrumentation characteristics that connect these seemingly different items. Spotify attributes $1 billion+ in annual subscriber retention value to its embedding-powered recommendation system.

For enterprise AI applications, embeddings are the foundation of Retrieval-Augmented Generation (RAG) — the dominant architecture for building AI assistants that can answer questions about your company's proprietary data. RAG works by embedding your documents into a vector database, finding the most relevant documents for a user's query, and feeding those documents to a language model to generate an accurate, sourced response. Over 80% of enterprise AI deployments in 2025 use some form of RAG, making embeddings infrastructure a strategic necessity.

Enterprise Use Cases

Notion, the productivity platform used by over 30 million people, uses embeddings to power its AI-powered search and Q&A features. When a user asks "What did the team decide about the Q3 product launch?", the system uses embeddings to find relevant meeting notes, project documents, and Slack conversations — even if the exact words "Q3 product launch" never appear in those documents. Notion reported that AI-powered search reduced average time-to-information from 12 minutes to under 30 seconds for enterprise customers.

Shopify uses product embeddings to power its "similar products" recommendations across millions of merchant storefronts. The embedding system understands visual similarity (products that look alike), semantic similarity (products that serve similar purposes), and behavioural similarity (products that similar customers tend to buy together). Merchants using Shopify's embedding-powered recommendations report an average 18% increase in average order value.

In healthcare, Mayo Clinic uses medical text embeddings to match patients with relevant clinical trials. The system embeds both patient medical records and trial eligibility criteria, then identifies matches based on semantic similarity rather than exact keyword matching. This approach increased clinical trial matching accuracy by 40% compared to traditional methods, accelerating patient enrolment and advancing medical research.

Vendor Landscape

Embedding generation is dominated by a few key providers. OpenAI's text-embedding-3-large model produces 3,072-dimensional embeddings at approximately $0.13 per million tokens. Cohere offers multilingual embeddings with competitive pricing, favoured for international enterprises. Google provides embedding models through Vertex AI, and Anthropic offers embeddings alongside its language models. For self-hosted options, the open-source BGE (BAAI General Embedding) and E5 models from Microsoft Research offer quality approaching proprietary alternatives with no per-query costs. On the storage side, Pinecone is the leading purpose-built vector database, with enterprise pricing starting at approximately $70,000 annually for production deployments. Weaviate and Milvus (and its managed version Zilliz Cloud) are strong open-source alternatives. Qdrant has gained significant traction for its Rust-based performance. Major databases now include vector capabilities: PostgreSQL (via pgvector), MongoDB Atlas Vector Search, and Elasticsearch all support embedding storage and similarity search, allowing organisations to add AI capabilities without new infrastructure.

Strategic Considerations

Invest in embedding infrastructure when your organisation plans to deploy AI-powered search, recommendation systems, RAG-based AI assistants, or any application that requires understanding the semantic meaning of text, images, or other data. This is virtually every organisation deploying AI, so the question is not whether but how to implement embeddings effectively.

Start with your existing database infrastructure — if you use PostgreSQL, MongoDB, or Elasticsearch, their built-in vector search capabilities may suffice for initial use cases, avoiding the cost and complexity of a dedicated vector database. Migrate to a purpose-built vector database when query volumes exceed millions of queries per day or when you need sub-10-millisecond retrieval latency at scale. Pay close attention to embedding model selection: larger, more expensive models are not always better for your specific use case. Evaluate multiple models against your actual data before committing. Finally, plan for embedding freshness — as your content changes, embeddings must be regenerated, which requires automated pipelines and incremental processing capabilities.