Vector Databases Compared: Pinecone, Milvus, Chroma, Weaviate, FAISS, and more

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Table of contents

• What is a vector database?
• Why are vector databases so hot right now?
• Vector database use cases
• Choosing the right vector database
• Deployment Options
• Scalability
• Performance and benchmarking
• Data Management
• Vector Similarity Search
• Integration and API
• Security
• Community and Ecosystem
• Pricing
• Additional Features
• Check back soon

What is a vector database?

In the world of data, not everything fits neatly into rows and columns. This is especially true when dealing with complex, unstructured data like images, videos, and natural language. That's where vector databases come in.

A vector database is a type of database that stores data as high-dimensional vectors, which are essentially lists of numbers that represent the features or characteristics of an object. Each vector corresponds to a unique entity, like a piece of text, an image, or a video.

But why use vectors? The magic lies in their ability to capture semantic meaning and similarity. By representing data as vectors, we can mathematically compare them and determine how similar or dissimilar they are. This enables us to perform complex queries like "find me images similar to this one" or "retrieve documents that are semantically related to this text."

Why are vector databases so hot right now?

Vector databases have gained significant popularity in recent years, particularly in the fields of artificial intelligence (AI) and machine learning (ML). As AI and ML models become more sophisticated, they require efficient ways to store, search, and retrieve the vast amounts of unstructured data they work with.

Traditional databases, which are designed for structured data, often struggle to handle the complexities and scale of vector data. Vector databases, on the other hand, are purpose-built for this task. They offer specialized indexing and search algorithms that can quickly find similar vectors, even in databases with billions of entries.

Vector database use cases

The ability to search for similar vectors opens up a world of possibilities for AI and ML applications. Some common use cases include:

• Recommendation Systems: By representing user preferences and item features as vectors, vector databases can power highly personalized recommendation engines.
• Image and Video Search: Vector databases enable searching for visually similar images or videos, revolutionizing content-based retrieval.
• Natural Language Processing: By encoding text into vectors, vector databases facilitate semantic search, topic modeling, and document clustering.
• Fraud Detection: Vector databases can help identify patterns and anomalies in financial transactions, aiding in fraud detection efforts.

Choosing the right vector database

With the growing demand for vector databases, several options have emerged in the market. Each database has its own strengths, trade-offs, and ideal use cases. In this blog post, we'll dive into a comprehensive comparison of popular vector databases, including Pinecone, Milvus, Chroma, Weaviate, Faiss, Elasticsearch, and Qdrant.

By understanding the features, performance, scalability, and ecosystem of each vector database, you'll be better equipped to choose the right one for your specific needs.

These are just a few examples of how vector databases are driving innovation across industries.

Deployment Options

Pinecone is the odd one out in this regard. Because Pinecone is a fully-managed service for performance and scalbility reasons, you can't run an instance locally.

Milvus, Chroma, Weaviate, Faiss, Elasticsearch and Qdrant can all be run locally; most provide Docker images for doing so.

Vector Database	Local Deployment	Cloud Deployment	On-Premises Deployment
Pinecone	❌	✅ (Managed)	❌
Milvus	✅	✅ (Self-hosted)	✅
Chroma	✅	✅ (Self-hosted)	✅
Weaviate	✅	✅ (Self-hosted)	✅
Faiss	✅	❌	✅
Elasticsearch	✅	✅ (Self-hosted)	✅
Qdrant	✅	✅ (Self-hosted)	✅

Scalability

Meaningful scalability metrics require defined constraints. The following is a quick look at which scaling schemes each vector database supports.

Stay tuned for performance and benchmarking stats.

Vector Database	Horizontal Scaling	Vertical Scaling	Distributed Architecture
Pinecone	✅	✅	✅
Milvus	✅	✅	✅
Chroma	✅	✅	✅
Weaviate	✅	✅	✅
Faiss	❌	✅	❌
Elasticsearch	✅	✅	✅
Qdrant	✅	✅	✅

Performance and benchmarking

Coming soon!

Data Management

Pinecone supports "collections" which are like checkpoints or save states on your indexes.

You can use collections to create new indexes. Generally speaking, they're a convenience method for moving lots of data between indexes.

Vector Database	Data Import	Data Update/Deletion	Data Backup/Restore
Pinecone	✅	✅	✅
Milvus	✅	✅	✅
Chroma	✅	✅	✅
Weaviate	✅	✅	✅
Faiss	✅	✅	❌
Elasticsearch	✅	✅	✅
Qdrant	✅	✅	✅

Vector Similarity Search

One of the reasons vector databases are so useful is that they can tell us about the relationships between things and how similar or dissimilar they are.

There are a variety of distance metrics that allow vector databases to do this, and different vector databases will implement various distance metrics.

For a helpful introduction to how the different distance metrics compare, check out Pinecone's guide here.

Vector Database	Distance Metrics	ANN Algorithms	Filtering	Post-Processing
Pinecone	Cosine, Euclidean, Dot Product	Proprietary (Pinecone Graph Algorithm)	✅	✅
Milvus	Euclidean, Cosine, IP, L2, Hamming, Jaccard, Tanimoto	HNSW, IVF_FLAT, IVF_SQ8, IVF_PQ, RNSG, ANNOY	✅	✅
Chroma	Cosine, Euclidean, Dot Product	HNSW	✅	✅
Weaviate	Cosine	HNSW	✅	✅
Faiss	L2, Cosine, IP, L1, Linf	IVF, HNSW, IMI, PQ	❌	❌
Elasticsearch	Cosine, Dot Product, L1, L2	HNSW	✅	✅
Qdrant	Cosine, Dot Product, L2	HNSW	✅	✅

Integration and API

While REST APIs are more commonly encountered, GRPC APIs are geared toward performance and throughput in latency critical scenarios or when its necessary to move large amounts of data quickly.

Depending on your requirements and network, GRPC can be several times faster than REST.

Vector Database	Language SDKs	REST API	GraphQL API	GRPC API
Pinecone	Python, Node.js, Go, Rust	✅	❌	✅
Milvus	Python, Java, Go, C++, Node.js, RESTful	✅	❌	✅
Chroma	Python	✅	❌	❌
Weaviate	Python, Java, Go, JavaScript, .NET	✅	✅	✅
Faiss	C++, Python	❌	❌	✅
Elasticsearch	Java, Python, Go, Ruby, PHP, Rust, Perl	✅	❌	❌
Qdrant	Python, Rust	✅	❌	✅

Security

Pinecone encrypts vectors in flight and at rest.

Vector Database	Authentication	Data Encryption	Access Control
Pinecone	✅	✅	✅
Milvus	✅	✅	✅
Chroma	❌	❌	❌
Weaviate	✅	✅	✅
Faiss	❌	❌	❌
Elasticsearch	✅	✅	✅
Qdrant	✅	✅	✅

Community and Ecosystem

Pinecone itself is not open-source, meaning that you cannot browse to the source code for the core database or supporting services or contribute to them.

That said, Pinecone is very active in terms of open-source, publishing client SDKs in the multiple languages, as well as tools like Canopy, which facilitates creating a Retrieval Augmented Generation (RAG) app very quickly.

Vector Database	Open-Source	Community Support	Integration with Frameworks
Pinecone	❌	✅	✅
Milvus	✅	✅	✅
Chroma	✅	✅	✅
Weaviate	✅	✅	✅
Faiss	✅	✅	✅
Elasticsearch	✅	✅	✅
Qdrant	✅	✅	✅

Pricing

Pinecone offers a free tier which allows you to create and maintain one index, and to store roughly 100,000 vectors in it.

Since Pinecone is fully-managed, you must either use the free tier or pay for access to higher tier plans.

Vector Database	Free Tier	Pay-as-you-go	Enterprise Plans
Pinecone	✅	✅	✅
Milvus	✅	❌	❌
Chroma	✅	❌	❌
Weaviate	✅	❌	✅
Faiss	✅	❌	❌
Elasticsearch	✅	✅	✅
Qdrant	✅	❌	❌

Additional Features

Metadata is any arbitrary strings, numbers, or objects that you want to associate with a vector. You can think of it like a JavaScript object:

{
  "category": "shoes",
  "color": "blue",
  "size": 10,
}

You define your metadata in your application code and attach it to a vector at upsert time, meaning when you make your request to your vector database to store a vector.

Metadata is an incredibly powerful concept and very complimentary with core vector database features; it's the link between ambiguous human language and structured data.

This is the foundation of the architecture where a human user asks for a product and the AI shopping assistant immediately returns the items they're describing.

Vector Database	Metadata Support	Batch Processing	Monitoring and Logging
Pinecone	✅	✅	✅
Milvus	✅	✅	✅
Chroma	✅	✅	❌
Weaviate	✅	✅	✅
Faiss	❌	✅	❌
Elasticsearch	✅	✅	✅
Qdrant	✅	✅	✅

Check back soon

I'm treating this post as a living piece of content - so it will receive some updates over time. Be sure to bookmark / share it if you'd like to stay up-to-date.