Vector databases: what you need to know before production

You have your product requirements. You want to build a system that will search 10 million embeddings with sub-100ms latency and handle thousands of concurrent queries without performance degrading. But choosing between standalone vector databases and unified platforms creates architectural decisions that affect your costs, latency, and operational overhead for years.

Traditional databases can't handle vector similarity at scale. Keyword search fails when users ask questions in different ways. And stitching together separate systems for vectors, caching, and operational data multiplies your complexity without improving performance.

This article explains what vector databases actually do, how they differ from traditional databases, when you need one versus simpler approaches, and how to evaluate architectures that scale from thousands to billions of vectors without bottlenecks.

What is a vector database?

Structured data fits neatly in rows and columns: relational databases handle it well. But unstructured data like images, documents, and audio doesn't. You can store it, but you can't search it by meaning. Vector databases solve this: they store embeddings that capture semantic content, making unstructured data searchable by similarity rather than exact matches.

NoSQL databases store unstructured data like images, documents, and audio. Vector databases add a specific capability: storing and searching the embeddings that represent this data. Through embedding models, you transform unstructured content into numerical vectors that capture semantic meaning, making similarity search possible.

Different models produce different vector sizes. OpenAI's text-embedding-3-small outputs 1,536 dimensions; Cohere's embed-v3 outputs 1,024; open source models on HuggingFace's MTEB leaderboard range from 384 to 4,096 dimensions. Your choice affects storage costs and search performance, but most teams start with a standard model and optimize later based on actual results.

These embeddings capture semantic information about whatever data they represent. Vectors from the same model always have the same length: a 1,536-dimension model produces 1,536-dimension vectors whether you're embedding a sentence or a paragraph.

Their capabilities in handling large-scale datasets, providing fast and accurate vector search, and integrating with existing technologies make them a cornerstone for businesses and researchers aiming to leverage the power of AI.

How vector databases work

Vector databases convert unstructured data into searchable numerical representations through a multi-step process.

Embedding generation

Embedding generation transforms your raw data into fixed-length vectors using machine learning models. A text embedding model converts "customer support inquiry" into a numerical representation that captures its meaning, not the exact words, but the semantic concepts they represent.

Different embedding models produce vectors of different sizes. Your choice affects storage costs and search performance, but most developers start with standard models from OpenAI, Cohere, or HuggingFace and optimize later based on actual performance needs.

Index structures

Once you have embeddings, you need an index to search them efficiently. The index algorithm you choose determines how fast searches run, how much memory they consume, and how accurate results are.

For most production workloads, HNSW delivers the best speed-accuracy trade-off.

Similarity search

Once you have vectors and an index, you need a way to measure how close vectors are to each other. Different distance metrics work better for different types of data, similar to how you might measure physical distance in miles versus city blocks depending on what you're trying to find.

Query execution in production involves several steps. Your query gets embedded using the same model as your stored data. The database searches the index structure for nearest neighbors. Results get ranked by similarity score. Optional metadata filtering removes irrelevant matches. Top K results return to your application.

Performance depends on your dataset size and the index algorithm you choose. Smaller datasets with simpler indexes search faster but may sacrifice some accuracy. Larger datasets require more sophisticated indexing to maintain reasonable query times.

Vector database architectures

When implementing vector search, you face a critical architectural decision: standalone vector database, embedded extension, or unified platform. This choice determines your operational complexity, performance ceiling, and infrastructure costs.

*Typical latency ranges with optimized configurations. Actual performance varies based on hardware, index parameters, and query complexity.

Standalone vector databases work best when you need sub-50ms latency at 100M+ vectors and can manage multiple specialized systems. Embedded extensions make sense for tight relational integration with modest latency requirements under 20M vectors. Unified platforms fit when you're building complete AI applications that need vectors alongside caching and operational data, and you'd rather manage one system than three.

Understanding Query Vectors in Vector Databases

Query vectors are a fundamental concept in the functionality of vector databases, serving as the cornerstone for the advanced search capabilities these systems offer. A query vector is essentially a vector representation of a search query, which could be derived from any form of unstructured data, such as a text description, an image, or an audio clip. This vector encapsulates the essence of the query in a numerical form, enabling the database to perform a similarity search against the stored vectors to find the most relevant results.

When a user submits a query to a vector database, the system first converts this query into its vector representation using the same vectorization process applied to the stored data. This ensures that the query and the database content are in the same dimensional space, making it possible to measure the similarity between the query vector and the database vectors.

The database then utilizes algorithms like Euclidean distance or cosine similarity to identify and rank the stored vectors based on their closeness to the query vector, effectively finding the pieces of data that best match the user’s query.

The ability to convert queries into vectors and search for similar items makes vector databases incredibly powerful tools for a wide range of applications, from personalized recommendation systems to sophisticated content retrieval and NLP tasks. Query vectors allow these databases to understand and interpret the nuances and context of the search query, leading to more accurate and relevant search results compared to traditional keyword-based search methods.

Use Cases

Vector databases have gained prominence due to their pivotal role in supporting the development and deployment of AI applications. As these applications become more sophisticated, the need for efficient data storage and retrieval systems that can handle complex queries and large volumes of data has become critical. Vector databases, with their ability to efficiently store and manage high-dimensional vector data, are increasingly being recognized as an essential infrastructure component for AI-driven technologies.

Vector databases are instrumental in powering a wide range of applications across various industries due to their unique ability to manage and search high-dimensional data efficiently.

RAG systems for LLMs

RAG pipelines query vector databases to retrieve relevant context before passing it to language models. If your LLM needs to reference internal docs, FAQs, or knowledge bases, vector search ensures you retrieve semantically relevant information even when query phrasing differs from stored content. This has become one of the most common use cases as teams build AI applications that need to ground responses in proprietary data.

Recommendation systems

Recommendation systems leverage vector databases to understand user preferences and content features, offering personalized suggestions in e-commerce, streaming services, and social media platforms. When you're comparing thousands or millions of products, content items, or user profiles, vector similarity search handles "find similar items" queries that traditional databases can't process efficiently at scale.

Image and video retrieval

Vector databases enable fast and accurate search of visual content, crucial for digital libraries, stock image websites, and surveillance systems. They work by comparing the similarity between vectors representing images or video frames. When users upload an image and want to find similar ones, vector search understands visual concepts like composition, color palette, and subject matter rather than relying on manual tags.

Natural language processing

Vector databases support NLP applications, such as semantic search, chatbots, and language translation services, by storing and searching text represented as vectors to capture contextual similarities. Semantic search uses embeddings to find conceptually similar items even when exact keywords don't match. Your chatbot can surface relevant FAQs when users phrase questions in unexpected ways.

Semantic caching for cost reduction

Semantic caching uses vector similarity to recognize when queries mean the same thing despite different wording. "What's your refund policy?" and "How do I return items?" become cache hits instead of separate LLM calls. Teams typically achieve 30-70% cost reductions through semantic caching when query patterns repeat frequently, with FAQ-heavy workloads like customer support seeing even higher savings.

Fraud detection and security

By analyzing behavioral patterns and detecting anomalies in real-time, vector databases help in identifying fraudulent transactions and potential security breaches, enhancing the safety of online systems. Vector representations of transaction patterns, user behavior, and network activity allow systems to spot outliers that traditional rule-based approaches miss.

Biometric identification

The use of vector databases in biometric systems, such as facial recognition and fingerprint identification, allows for the rapid and precise matching of biometric data for security and authentication purposes. Biometric data converts naturally into vector embeddings, and vector similarity search handles the "is this the same person?" question across millions of stored profiles in milliseconds.

AI agent memory

Agents need to recall relevant past interactions without exact keyword matches. Vector databases let agents search conversation history, previous decisions, and learned context based on semantic similarity, enabling more intelligent autonomous behavior. As agents work through tasks, they build context that needs retrieval later, and vector search makes this possible without exact phrase matching.

Build with Redis for vector search

Most vector database guides present vectors in isolation, but production AI apps need more than embedding storage. You need caching for duplicate queries, operational data for user state, and real-time coordination for multi-step workflows.

Redis provides vector search alongside the data structures and caching features AI applications already use. Your RAG pipeline gets vector retrieval for context, semantic caching for duplicate queries, and session storage for conversation history—all through one API with sub-millisecond latency.

Native vector indexing with HNSW and FLAT algorithms supports multiple embedding models and dimensions. Redis benchmarks show sub-100ms vector search at billion-scale with 90% precision at 200ms median latency. Hybrid search through Redis Query Engine combines vector similarity with metadata filtering without separate query languages or re-ranking steps.

Redis handles vector search, semantic caching, agent memory, and operational data without stitching together multiple vendors. Active-Active Geo Distribution provides 99.999% uptime, automatic failover, and linear scalability as your dataset grows from thousands to billions of vectors.

Ready to build? Try Redis free to test vector search with your embeddings, explore Redis for AI docs for implementation guides covering RAG, semantic caching, and agent memory patterns, or meet with our team to discuss your architecture requirements.

FAQs about vector databases

What is a vector database?

A vector database stores and searches vector embeddings—numerical representations of unstructured data like text, images, and audio. Unlike traditional databases that match exact values, vector databases find similar items by measuring distance between vectors in high-dimensional space. This enables semantic search, RAG systems, and recommendation engines that understand meaning rather than just matching keywords.

When do I need a vector database?

You need a vector database when you're searching tens of thousands of items or more and require sub-100ms query latency at scale. The exact threshold depends on your query volume, acceptable latency, and whether you need real-time updates. For smaller datasets (<10K items with low query frequency), in-memory brute-force search may suffice.

How much does a vector database cost?

Costs vary widely based on provider, query throughput, replication needs, and feature requirements. For specific pricing, refer to the Redis pricing calculator or contact your vendor.

What's the difference between vector databases and traditional databases?

Traditional databases store structured data in rows and tables, searching by exact matches or range queries. Vector databases store high-dimensional embeddings and search by semantic similarity. Traditional databases excel at exact lookups—finding user records by ID or filtering products by price. Vector databases excel at finding similar items based on meaning. Most production AI apps need both working together.

Should I use a standalone vector database or unified platform?

Standalone vector databases focus exclusively on vector operations but require separate systems for caching, operational data, and session management. You'll typically run 3-5 systems in production, each with different APIs and failure modes. Unified platforms integrate vectors with caching and operational features in one system, reducing vendor count and simplifying architecture. Choose standalone for pure vector workloads where you're comfortable managing multiple databases. Choose unified when building complete applications or when team velocity matters more than absolute vector-specific optimization.

What's the difference between HNSW, FLAT, and IVF indexing?

HNSW uses graph-based approximate search for fast queries with 90-95% accuracy at scale. FLAT uses brute-force exact search, guaranteeing perfect results but taking longer as datasets grow. IVF partitions vectors into clusters for memory efficiency with slightly lower accuracy. For most production workloads, HNSW delivers the best speed-accuracy trade-off.

Redis supports both HNSW and FLAT indexing. HNSW indexes in Redis achieve 90%+ precision at billion-scale with sub-100ms latency. Unlike implementations that require index rebuilds, Redis updates indexes incrementally during writes, maintaining consistent query performance under concurrent load.

How do I choose between cosine similarity, dot product, and Euclidean distance?

Use cosine similarity when comparing text or documents of varying lengths where magnitude doesn't matter. Use dot product when magnitude carries meaning, like user activity levels in recommendations. Use Euclidean distance when measuring absolute differences in feature values, like spatial coordinates. Test all three on your actual data to find what works best.

Can vector databases handle real-time updates?

Yes, but with trade-offs. Adding new vectors requires index updates, which can temporarily affect query performance during high-write periods. In-memory systems like Redis update indexes incrementally while serving queries, minimizing disruption. Disk-based systems may batch updates for efficiency, trading fresher data for consistent query performance. For workloads with both high query volume and frequent updates, you'll need to architect specifically for this pattern.