Imagine this scenario: A user visits a bookstore’s website and searches for the phrase “Best New Fiction.” Without further context, the site might return a list of books such as “The Heaven and Earth Grocery Store,” which was released in 2023, but it’s not the book the user is looking for. They might be more interested in sci-fi books, as that’s evident if the search system has access to their historical reading history. However, the site’s search algorithm still returns “James McBride’s The Heaven and Earth Grocery Store,” which might not be the most relevant or up-to-date book in the library’s collection.
This is where vector databases come in. Unlike traditional databases, which store and organize data based on a fixed set of attributes (e.g., ID, name, publication date), vector databases store numerical values that represent the semantic data of an object. This allows search systems to find items that are similar to, not identical to, the objects they are compared against.
Vector databases store these numerical values as vectors, which are sequences of numbers. When a user searches for a phrase or word, the search system can analyze the vector of words in the query and compare them to the vectors in the database to find items that contain similar or related
Imagine this scenario. A user goes to a bookstore’s website and looks up the phrase Best New Fiction. Without further context, the site might return something like James McBride’s The Heaven & Earth Grocery Store, which came out in 2023. That’s new by some standards, but not likely what this particular user is looking for. They might be more into sci-fi instead of literature, which would be evident if the search system has access to the user’s history. This is where vector databases come in.
Unlike traditional databases, which pair an object with some sort of unique ID, vector databases store numerical values that represent the semantic data of an object. This lets search systems find items that are similar instead of identical, which makes vector databases for recommendation engines, chat systems, and, of course, AI ecosystems.
In this article, we’re going to take a look at ten of the most popular vector databases with APIs. We’ll examine the strengths and weaknesses of each vector database, discuss any similarities and differences, and examine how they work with AI systems.
| Name | Open Source | API Style | Hybrid Search | Best For | Deployment | Notes |
|---|---|---|---|---|---|---|
| Pinecone | No | REST, Python SDK | Yes | RAG, production apps | Managed only | Easiest to use, closed-source |
| Weaviate | Yes | REST, GraphQL | Yes | Custom models, open-source apipelines | Self-hosted/Cloud | Modular, strong community |
| Qdrant | Yes | REST, gRPC, SDKs | Yes | Filtering, on-disk indexing | Self-hosted/Cloud | Rust-based, good hybrid support |
| OpenSearch | Yes | REST | Yes | Enterprises extending ELK stack | Self-hosted/Cloud | Hybrid + ANN via plugin integrations |
| Chroma | Yes | Python API | Partial | Prototypes, notebooks | Local-first | Ideal for LangChain-based apps |
| Milvus | Yes | gRPC, REST | Yes | Billion-scale workloads | Self-hosted | GPU support, complex configuration |
| Zilliz | No | REST, SDKs | Yes | Managed Milvus at scale | Fully managed | Great for enterprise RAG at scale |
| Vald | Yes | gRPC | Limited | K8s-native, microservices | Kubernetes only | Steep learning curve |
| Elasticsearch | Yes | REST | Yes | Hybrid e-commerce, news search | Self-hosted/Cloud | Combines BM25 and vector relevance |
| Astra DB | Partial | REST, gRPC, CQL | Limited | High-throughput + vector storage | Managed/Self-host | Vector support still emerging |
Pinecone
Pinecone is a cloud-native vector database explicitly designed to be used with AI. It’s typically used for semantic search and as a backend for retrieval augmented generation (RAG). Pinecone automatically indexes and shards data, with a simple API that allows inserting or querying vectors. It also allows users to filter metadata, allowing users to refine their data using structured filters. One main downside is that it can’t be deployed locally.
Weaviate
Weaviate is an open-source, extensible vector database written in Go, explicitly designed to be used with AI. It offers schema support, allowing users to define objects and properties, making it ideal for AI that uses both structured and unstructured data. It also provides native support for OpenAI, Cohere, and HuggingFace. Weaviate is popular with developers building virtual assistants, RAG systems, and custom search engines.
Quadrant
Qdrant is a Rust-based vector database focused on high-performance approximate nearest neighbor (ANN) search. It offers support for filterable vector search and gRPC/REST APIs. It specializes in fast indexing and recall, making it an excellent choice for large applications that require low latency and high availability. It also offers native integration for LangChain, FastAPI, and Python, making it easy to get started. Quadrant is a popular choice for developers working with chat systems, image comparison, and recommendation systems.
OpenSearch
OpenSearch is a fork of Elasticsearch that allows developers to implement vector similarity search using k-nearest neighbors (KNN) and ANN. Its main selling point is combining traditional keyword-based search and semantic search. This makes OpenSearch a good choice for developers looking to expand traditional search systems with semantic vector search. It’s impressively powerful, but it can take some work to configure the ANN indexes compared to Pinecone or Qdrant.
Chroma
Chroma is another open-source vector database that’s designed for native AI use. It features an intuitive, easy-to-use Python interface and tight couplings with LangChain. Unlike the other vector databases we’ve discussed so far, Chroma is designed less for scalability and more for fast deployment. This makes Chroma an excellent choice for developers looking to prototype products or develop local RAG tools. Its small, efficient size makes Chroma a fine choice to be run in-memory.
Milvus
Milvus is an open-source vector database designed for working with large datasets. It offers support for billions of vectors as well as various indexing strategies, including IVF, HNSW, and DiskANN. Milvus supports both REST and gRPC APIs, making it easy to work with. It also includes a service called Zilliz Cloud, making it easy to deploy as well. Milvus is an excellent choice for developers working on image or video search, biometrics, or large RAG systems.
Vespa
Vespa is a search engine and vector database developed by Yahoo that’s now open source. It supports on-the-fly model inference, ranking structures, and native vector search. This makes Vespa ideal for large-scale applications that require search-like products or news recommendations. It’s harder to set up than Chroma or Pinecone, but its performance makes it worthwhile to work with.
Vald
Vald is an open-source vector database built using Kubernetes. It uses Facebook AI Similarity Search (FAISS) for familiarity search and clustering of vectors, which provides dynamic index scaling, auto-healing, and numerous deployment options. Vald is a good choice for developers creating Kubernetes-native applications or heavy concurrent searches. Its only downside is its API uses gRPC, which may be a drawback for developers used to working with REST.
Elasticsearch
Elasticsearch has been a popular choice for traditional search for some time. They’ve since expanded to support vector databases, offering the best of all worlds. Elasticsearch combines BM25 keyword relevance with cosine or dot product vector similarity, making it one of the most powerful, versatile tools for hybrid search. Elasticsearch is a good choice for developers building product or news recommendation systems, or knowledge base retrieval that requires a mix of relevance signals. Its main downside is that it doesn’t perform as well at scale as Qvdrant or Milvus.
Astra DB
Astro DB is a multi-model database designed by Apache Cassandra that’s increasingly integrating vector embeddings. It’s built for extensive deployments that require extensive read/write capabilities and scalability. Developers use Astra DB to store vector embeddings alongside transactional and document data. Astra DB’s vector database is still developing, but its functionality and scalability make it worthy of a look.
Final Thoughts on Vector Databases for AI
Vector databases are only going to become more critical as AI continues to grow and datasets continue to expand. Developers looking to make their applications perform their absolute best would do well to get comfortable with vector databases now. That includes AI applications, as vector databases are imperative for AI to function.
Users looking for scalability would do well to give Pinecone, Chroma, Astra DB, or Vald a look. Developers looking for an open-source vector database should try Weaviate, Qdrant, or Milvus. Anyone who wants to combine traditional search with vector databases should try OpenSearch or ElasticSearch.
