RAG at Scale: The Hidden Trade-Offs of Single Vector Embedding

Balancing performance and precision in enterprise search and data retrieval

A common pattern for improving the quality of AI-assisted content categorization and search in enterprise systems is the Retrieval-Augmented Generation (RAG) pattern. RAG combines two steps: first, a retrieval component fetches the most relevant documents or knowledge snippets from an indexed data source; second, a large language model (LLM) uses both the retrieved context and the user’s query to generate a more accurate and grounded response.

The indexed data source is typically a vector database that stores vector embeddings of enterprise data – whether text, images, or other unstructured content. I observed that many systems today represent each data item with a fixed-length single vector embedding, which makes similarity search and large-scale comparisons straightforward and efficient.

While single-vector embeddings are efficient and widely used, they compress all of an item’s information into a single representation. Vector dimensionality is typically in the range of 128–1024, set by LLM architecture or application constraints. This design choice introduces trade-offs in vector search accuracy, especially for long, multi-topic, or multi-modal content. These limitations naturally raise the question of whether there are alternative approaches that could offer a better performance in enterprise applications.

Introduction

I remember a similar challenge with achieving high retrieval quality in a Content Management System (CMS) due to the limit on maximum size of the indexable text per document in the underlying search engine (e.g., current limits are 10MB in Google Cloud Search, 16MB in Basic going up to 256MB in S3 Azure Cognitive Search, 10MB in Elastic, etc.). Some engines also restrict the size of a document that can be submitted for indexing. Even if large documents are allowed, many search engines will truncate (only index a prefix of content), or drop its content beyond certain size, or allow only metadata indexing for very large documents to optimize indexing efficiency and ultimately, query latency.

Drawing a parallel between traditional and AI-powered search:

Aspect	Limit on Indexable Content Size in Search Engines	RAG Single-vector Embedding
Nature of Degradation	Binary cutoff—past the size limit, information is lost entirely.	Continuous approximation—all content is represented, but less distinctly.
Loss Mode	Complete truncation of tail content	Blending of multiple concepts
Impact on Content Retrieval	1. Missing content means queries that depend on the truncated portion will not retrieve the document. 2. Relevance ranking suffers because term frequency and context weighting are incomplete. 3. The document may appear in search for generic queries (metadata, title) but fail for detailed keyword matches.	1. Compression causes semantic dilution: multiple topics in one document blur into an averaged representation. 2. Queries aligned with dominant themes retrieve well; niche or secondary topics are harder to match. 3. Precision suffers on long, multi-topic, or multi-modal items unless chunking is used.

Note, that in both cases retrieval quality is reduced, but in different ways:

Limit on indexable content size → sharp cutoff, retrieval “blind spot” beyond the size threshold.
Single-vector embeddings → semantic blur, retrieval “fuzziness” for less dominant content.

In practice, enterprises face both problems simultaneously: large documents may be truncated during indexing, and even the indexed portion may be poorly represented if compressed into a single embedding vector.

When using traditional search engines in CMS, the limit on indexable content size was typically mitigated by per-processing of documents using one of the following approaches:

1. Document Chunking / Segmentation

Approach: Split long documents into smaller sections
Benefits: Each chunk is under system limits which improves retrieval as even niche sections can be retrieved independently.
Advanced Approach: Hierarchical structuring to maintain relationships between chunks

2. Document Summarization

Approach: Use an NLP model for extractive summarization to reduce document size.
Benefits: Captures key information within strict byte / MB caps by reducing noise and redundancy.

3. Document Metadata Enrichment

Approach: Allow end-users to enter relevant metadata on submitted documents. Extract key entities (author, topic, department, effective date) from document, or use media recognition for tagging. On binary file submission, index metadata alongside its content.
Benefits: Improves precision when content chunks are incomplete. Enables hybrid search (keyword + metadata + vector). Allows keyword search on media files.

Metadata enrichment is often the most effective way to organize enterprise content. Most CMS platforms let you define a metadata model that allows consistently classifying documents into business-specific categories. In practice, this works much like multi-dimensional embeddings, where each dimension reflects a different facet of the content.

Single vs. Multi-Vector Embeddings

Trade-offs in search retrieval quality highlight an important design question: how can we capture the richness of enterprise data in RAG? Can we mitigate the limitations introduced by compressing each item into a single fixed-length vector by using multi-vector embeddings in RAG?

Such approach allows representing data such as document, image or video by multiple vectors, with each vector capturing a different aspect, feature, or modality. This allows retrieval systems to use multi-dimensional queries aligned with the most relevant aspects of an item, improving accuracy in multi-topic or complex content scenarios.

Applying common sense we can draw the following comparison between Single vs. Multi-Vector Embedding s in enterprise RAG:

Aspect	Single-Vector Embedding	Multi-Vector Embedding
Representation	One fixed-length vector per item (document, image, record).	Multiple vectors per item, each representing a passage, feature, or modality.
Storage & Indexing	Very compact: one vector per item.	Larger footprint: several vectors per item.
Retrieval Speed	High: only one similarity comparison per item..	Slower: must compare against multiple vectors per item..
Accuracy	Captures overall meaning but loses detail. Struggles with long or multi-topic items.	Captures fine-grained structure and multi-topic content. Higher accuracy for long, multi-aspect content (aligns query to relevant aspects).
Best for	Good for short, single-topic content. Quick retrieval, clustering, semantic similarity, recommendations.	Long documents, detailed search (passage-level), multi-modal content, complex Q&A.
Enterprise Fit	Scales efficiently for large document sets with consistent structure (FAQs, tickets, product catalogs).	Better for complex knowledge bases, regulatory documents, technical manuals, and multimedia repositories.
Typical Use Cases	FAQ bots, recommendation engines, log/event clustering	Contract analysis, scientific literature search, compliance monitoring, video/audio retrieval.

We can map the above feature comparison to use cases / examples:

Use Case	Single-Vector Embedding	Multi-Vector Embedding
Document Retrieval	A whole research paper encoded as one vector → good for finding “papers about quantum computing.”	Each chapter in the research paper encoded separately → good for finding the exact section discussing “quantum entanglement.”
Content Recommendations	One vector for each user profile → efficient matching of similar users/items.	Multiple vectors for each user’s interests / shopping history / social activity → better at recommending based on diverse interests.
Image Categorization	One vector for the entire image → useful for “find similar images.”	Vectors for different image regions → useful for “find images with a cat in the corner.”

Another way to look at single vs. multi-vector embedding is to think about person’s business card (summary) vs. resume (details).

Single- vs Multi-Vector Embeddings in RAG: Industry Landscape Analysis

Using common sense is a good starting point, but the next question is: who in the industry is actually using single-vector versus multi-vector embeddings in their RAG systems?

That is a tough and engaging question so that I decided to ask ChatGPT and Claude for the answer. Both kindly agreed to help me with creating the following report. In summary, it is clear that most enterprise RAG implementations still rely on single-vector embeddings, while a smaller but growing group of platforms and research-driven companies are adopting multi-vector strategies.

Single-Vector Embedding Approaches (Dominant in Production)

Leading Vector Database Providers

Traditional Database Providers

Oracle Database 23ai – Native AI Vector Search capabilities with single-vector embeddings, integrated RAG support using Select AI, and the ability to store vectors alongside relational data
Microsoft Azure – Multiple vector database services including Azure Cosmos DB, Azure AI Search, and Azure SQL Database with native vector data types
SQL Server 2025 – New native VECTOR data type supporting up to 1,536 dimensions with approximate vector indexes and vector search capabilities
PostgreSQL with pgvector – Open-source extension supporting vectors up to 2,000 dimensions (4,000 with halfvec), widely adopted across cloud providers
MongoDB Atlas – Native vector search supporting embeddings up to 8,192 dimensions with both ANN and ENN search algorithms

Vector Database Providers

Pinecone – The dominant managed vector database service focusing on single-vector embeddings, used by companies building RAG applications at scale
Chroma – Popular for prototyping and smaller-scale RAG applications, emphasizing simplicity with single-vector approaches
Weaviate – Open-source vector database with strong hybrid search capabilities, primarily single-vector focused
Qdrant – High-performance vector database built in Rust, optimized for single-vector embedding

Leading Embedding Model Providers:

OpenAI – Their text-embedding-3 models are widely used in production RAG systems with single-vector representations
Cohere, Voyage AI – Commercial embedding providers focusing on single-vector dense embeddings
Google – Provides single-vector embedding models through their APIs

Multi-Vector Embedding Approaches (Emerging/Research-Focused)

Academic/Research Leaders:

Stanford University – Original creators of ColBERT, the foundational multi-vector retrieval model
Jina AI – Commercial leader in multi-vector embeddings with their Jina-ColBERT-v2 model supporting 89 languages

Production-Ready Implementations:

DataStax – Integrated ColBERT into their RAGStack 1.0 and Astra DB platform for enterprise RAG deployments
Weaviate – Now supports multi-vector embeddings with ColBERT integration
Qdrant – Added multi-vector support, demonstrating how standard dense models can be adapted for late interaction

Early Production Adopters:

Spotify – Uses in-memory vector search with stateless deployments (via Kubernetes) for serving millions of users, though the specific approach isn’t detailed

Key Multi-Vector Technologies:

ColBERT Family:

ColBERT/ColBERTv2 – Token-level embeddings with “late interaction” for improved contextual understanding
ColPali/ColQwen – Multimodal extensions processing images and PDFs as visual patches

Commercial Solutions:

RAGatouille – Library making ColBERT accessible for production use
ColBERT Live! – Production-ready ColBERT implementation by DataStax

Current Industry Split

Single-Vector: ~95% of Production Systems

The vector database market exploded from $1.73 billion in 2024 to a projected $10.6 billion by 2032, with most implementations using single-vector approaches for simplicity and efficiency

Multi-Vector: ~5% but Growing Rapidly

Multi-vector approaches saw “noticeable acceleration” starting in summer 2024, with increased model availability and supporting infrastructure

The Rise and Evolution of RAG in 2024 A Year in Review

Future Outlook

The December 2024 RAGFlow review predicted: “In 2025, we can expect rapid growth and evolution of multimodal RAG, and we will integrate these capabilities into RAGFlow at the appropriate time”. So far, this is not a realized outcome.

Reality Check

“In a world where unstructured data — text, images, video, and beyond — is growing exponentially, the ability to connect and retrieve meaning across different modalities is becoming essential” – but this is describing potential rather than widespread adoption.
Most 2025 multimodal RAG content focuses on tutorials, demonstrations, and “how-to” guides rather than case studies of large-scale production deployments – An Easy Introduction to Multimodal Retrieval-Augmented Generation
Research papers from late 2024 are still exploring “optimal approaches” for multimodal RAG rather than reporting on mature, widespread implementations – Beyond Text: Optimizing RAG with Multimodal Inputs for Industrial Applications

While there were industry expectations for rapid growth and evolution of multimodal RAG in 2025, the actual adoption has been more measured. Multimodal RAG remains largely in the tutorial, demonstration, and pilot phase rather than mainstream production deployment. Multi-vector approaches like ColBERT are primarily used for specialized reranking tasks rather than replacing single-vector systems as the primary retrieval method. Enterprise RAG in 2025 has focused more on platform consolidation and production stability rather than adopting cutting-edge multi-vector techniques. Key factors limiting adoption:

Infrastructure maturity: Multi-vector infrastructure “acceleration” noted since summer 2024, but supporting models and tools are still developing
Cost concerns: Multi-vector storage and computational costs remain significant barriers
Enterprise caution: Enterprises approaching new AI techniques “with even more caution” due to potential negative impacts
Focus on stability: Organizations prioritizing production reliability over advanced features