The rise of AI applications has created a new infrastructure requirement: the ability to search by meaning rather than keywords. Whether you're building a RAG system, semantic search engine, or recommendation platform, you need a database that understands similarity — and that's exactly what vector databases provide.
The vector database market has exploded alongside the AI boom, with solutions ranging from purpose-built systems like Pinecone to vector extensions for databases you already use. Choosing the right option requires understanding how these systems work and what tradeoffs matter for your use case.
TL;DR: Vector databases store embeddings (numerical representations of data) and enable fast similarity search across millions or billions of vectors. They're essential infrastructure for RAG, semantic search, and recommendations. Key decision factors: scale requirements, latency needs, filtering complexity, managed vs self-hosted, and integration with your existing stack. Purpose-built options (Pinecone, Weaviate, Qdrant) offer the best features; PostgreSQL with pgvector works for smaller scales.
A vector database is a specialized data store optimized for storing, indexing, and querying high-dimensional vectors. Unlike traditional databases that match exact values or text patterns, vector databases find items that are semantically similar — even if they share no common words.
Traditional search relies on keyword matching. Search for "automobile maintenance" and you'll miss documents about "car repair" unless someone manually configured synonyms. This limitation becomes critical when dealing with natural language, images, or any unstructured data.
Vector databases enable semantic search by representing data as dense vectors (embeddings) where similar concepts occupy nearby positions in high-dimensional space. A search for "automobile maintenance" finds "car repair" because their vector representations are close together.
Embeddings are numerical representations of data — typically arrays of hundreds or thousands of floating-point numbers. They're generated by machine learning models trained to place similar items near each other in vector space.
For text, models like OpenAI's text-embedding-3 or open-source alternatives like BGE convert sentences or paragraphs into vectors. Similar models exist for images (CLIP), audio, and other data types. The embedding model choice significantly impacts search quality.
For a deeper explanation of embeddings in context, see our guide on how RAG systems work.
Imagine a library where books are shelved not by author or Dewey Decimal, but by topic and theme. Books about machine learning sit near books about statistics, which sit near books about data analysis. When you ask for something about "predicting customer behavior," the librarian walks to the right neighborhood and pulls relevant books — even if none contain that exact phrase.
Vector databases work similarly, using mathematical distance to find the closest matches to your query.
Understanding the internals helps you make better architecture decisions and troubleshoot performance issues.
Naive similarity search compares your query against every stored vector — fine for thousands of vectors, impossible for millions. Vector databases use specialized indexing algorithms to make search fast:
Most production systems use HNSW for its balance of accuracy, speed, and reasonable memory usage.
How do you measure "closeness" between vectors? Common metrics include:
Match your metric to your embedding model — most text embedding models are designed for cosine similarity.
Real applications rarely want pure vector search. You might need to filter by date, category, user permissions, or other metadata before or after similarity matching.
Vector databases handle this through:
Filtering capabilities vary significantly between databases — evaluate based on your actual query patterns.
The market has matured rapidly, with options spanning purpose-built solutions, traditional database extensions, and cloud provider offerings.
These systems were designed from the ground up for vector workloads:
| Database | Hosting | Key Strengths | Considerations |
|---|---|---|---|
| Pinecone | Managed only | Simplest to operate, excellent hybrid search, fast | Higher cost, no self-hosted option |
| Weaviate | Both | Built-in vectorization, GraphQL API, good filtering | More complex deployment |
| Qdrant | Both | Rust performance, advanced filtering, efficient | Newer, smaller ecosystem |
| Milvus | Both (Zilliz Cloud) | Massive scale, multiple index types | Operational complexity |
| Chroma | Self-hosted | Developer-friendly, great for prototypes | Less mature for production scale |
Major databases have added vector capabilities, enabling vector search without new infrastructure:
| Database | Vector Extension | Best For |
|---|---|---|
| PostgreSQL | pgvector | Small-medium scale, existing Postgres users |
| Elasticsearch | Native (8.0+) | Existing ES users, combined text + vector search |
| MongoDB | Atlas Vector Search | Existing MongoDB users, document + vector |
| Redis | RediSearch | Low-latency requirements, caching integration |
These options reduce operational complexity if you're already running these databases, but may lag purpose-built solutions in features and performance at scale.
Cloud providers offer integrated vector search within their AI platforms:
These options provide tight integration with cloud AI services but may have vendor lock-in implications.
Selection depends on your specific requirements. Work through these decision factors:
How many vectors do you need to store and search?
How fast do queries need to return?
What metadata filtering do you need?
Who manages the infrastructure?
Cost models vary significantly:
| If you need... | Consider... |
|---|---|
| Quickest production path | Pinecone (managed, minimal config) |
| Self-hosted flexibility | Weaviate or Qdrant |
| Maximum scale | Milvus or Pinecone enterprise |
| Minimum new infrastructure | pgvector (if using Postgres) or MongoDB Atlas |
| Lowest cost at small scale | pgvector or Chroma |
| Best hybrid search | Pinecone, Weaviate, or Elasticsearch |
Regardless of which database you choose, these practices improve performance and reliability.
Use the same embedding model for indexing documents and encoding queries. Mixing models produces meaningless similarity scores. Store the model identifier as metadata to prevent future confusion.
HNSW indexes have tunable parameters (M, efConstruction, efSearch) that trade off build time, memory, and search accuracy. Start with defaults, then tune based on your accuracy/latency requirements. Most databases provide guidance for common workloads.
Plan your metadata schema upfront. Include fields you'll filter on (dates, categories, sources, permissions). Some databases handle certain data types better than others — test your actual filter patterns.
Vector databases require ongoing attention:
Costs can escalate quickly at scale:
Vector databases power a range of production AI applications.
The most common use case. Vector databases store document embeddings for retrieval when users ask questions. Quality of vector search directly impacts RAG answer quality.
Search engines that understand intent, not just keywords. Users find relevant content even when query terms don't match document text exactly.
Product, content, or connection recommendations based on embedding similarity. "Users who liked this also liked" without explicit preference data.
Find outliers by identifying data points far from their neighbors in embedding space. Applicable to fraud detection, quality control, and security monitoring.
Visual similarity search using image embeddings from CLIP or similar models. Find products by photo, detect duplicate images, or organize visual content.
At Virtido, we help enterprises evaluate, deploy, and optimize vector database infrastructure — from technology selection to production optimization.
We've built vector search systems for clients across FinTech, healthcare, e-commerce, and enterprise software. Our staff augmentation model provides vetted talent in 2-4 weeks with Swiss contracts and full IP protection.
Vector databases have become essential infrastructure for AI applications. Whether you're building RAG systems, semantic search, or recommendation engines, understanding how these systems work helps you make better architecture decisions.
The market offers options for every scale and operational preference — from managed simplicity with Pinecone to self-hosted flexibility with Weaviate or Qdrant, to incremental adoption with pgvector. Start with your requirements (scale, latency, filtering, budget) and choose accordingly.
As AI applications mature, vector database capabilities will continue to expand. Hybrid search, advanced filtering, and tighter LLM integration are active areas of development. The fundamentals covered here provide a foundation for evaluating new developments as the ecosystem evolves.
Traditional databases store and query structured data using exact matches (SQL WHERE clauses, key lookups). Vector databases store high-dimensional vectors and query by similarity — finding the closest vectors to a query rather than exact matches. This enables semantic search where "car repair" finds "automobile maintenance" because their vector representations are similar.
Yes, with the pgvector extension. It adds vector data types and similarity search to PostgreSQL. This works well for smaller datasets (under 1-5 million vectors) and when you want to keep vector data alongside relational data. For larger scale or advanced features like hybrid search, purpose-built vector databases typically perform better.
For most RAG applications, Pinecone offers the easiest path to production with excellent hybrid search. Weaviate and Qdrant are strong self-hosted alternatives with good filtering and hybrid search capabilities. If you're already using Elasticsearch, its vector search features may be sufficient. The "best" choice depends on your scale, ops preferences, and budget.
Purpose-built vector databases can handle billions of vectors — Pinecone, Milvus, and Weaviate have all demonstrated deployments at this scale. Practical limits are usually cost and operational complexity rather than technical capacity. PostgreSQL with pgvector is typically recommended for up to a few million vectors; beyond that, consider purpose-built solutions.
For prototypes and small-scale applications, using your existing database (PostgreSQL + pgvector, MongoDB Atlas Vector Search, Elasticsearch) reduces operational complexity. As you scale beyond millions of vectors or need advanced features, purpose-built vector databases offer better performance, features, and cost efficiency. Start simple and migrate when you hit limitations.
Costs vary widely. Pinecone serverless starts around $0.33/million queries plus storage. Managed Weaviate or Zilliz Cloud ranges from $25-500+/month depending on scale. Self-hosted costs are infrastructure only (compute + storage) but require ops expertise. pgvector has no licensing cost. Expect $100-500/month for small production workloads, scaling to thousands for enterprise deployments.
Migration involves exporting vectors and metadata from the source, transforming to the target format, and re-importing. Most databases support bulk export/import. The challenge is re-embedding if you change embedding models or handling differences in metadata schemas. Plan for testing retrieval quality after migration. Some vendors provide migration tools or services.
Pinecone's value depends on your situation. For teams wanting minimal ops overhead, fast time-to-production, and strong hybrid search, Pinecone is often worth the premium. For cost-sensitive deployments or teams comfortable with infrastructure management, self-hosted alternatives like Weaviate or Qdrant offer similar capabilities at lower per-vector costs. Evaluate based on your ops capacity and scale.
HNSW (Hierarchical Navigable Small World) builds a graph structure enabling fast, accurate search with good memory efficiency. It's the default choice for most workloads. IVF (Inverted File Index) clusters vectors and searches relevant clusters; it's faster to build and update but slightly less accurate. Most production systems use HNSW unless they have specific requirements for frequent updates.
Create a representative test dataset matching your production scale and query patterns. Measure query latency (p50, p95, p99), throughput (queries per second), and recall (accuracy compared to exact search). Test with realistic filtering patterns. Most vector databases provide benchmarking tools. Run tests under load conditions matching expected production traffic.