Keywords AI

Chroma vs Weaviate

Compare Chroma and Weaviate side by side. Both are tools in the Vector Databases category.

Quick Comparison

	Chroma	Weaviate
Category	Vector Databases	Vector Databases
Pricing	Open Source	Open Source
Best For	Python developers who want a simple, embedded vector database for prototyping	Developers who need a flexible, open-source vector database with multimodal and hybrid search
Website	trychroma.com	weaviate.io
Key Features	Lightweight embedded vector database Python-native API Runs in-process without a server Simple document and query interface Open-source and free	Open-source vector database Hybrid search (vector + keyword) Multi-modal support GraphQL and REST APIs Kubernetes-native deployment
Use Cases	Local development and prototyping Small to medium RAG applications Embedded vector search in Python apps Research and experimentation Serverless and edge deployments	Multimodal search across text, images, and more RAG with hybrid retrieval E-commerce product search Content recommendation engines Self-hosted vector search

When to Choose Chroma vs Weaviate

Choose Chroma if you need

Local development and prototyping
Small to medium RAG applications
Embedded vector search in Python apps

Pricing: Open Source

Choose Weaviate if you need

Multimodal search across text, images, and more
RAG with hybrid retrieval
E-commerce product search

Pricing: Open Source

How to Choose a Vector Databases Tool

Key criteria to evaluate when comparing Vector Databases solutions:

Query performanceSearch latency and throughput at your expected data scale and query volume.

ScalabilityHow well the database handles growing from thousands to billions of vectors.

Hosting modelFully managed cloud, self-hosted, or embedded options depending on your infrastructure needs.

Filtering supportAbility to combine vector similarity search with metadata filters efficiently.

Integration ecosystemNative integrations with popular frameworks like LangChain, LlamaIndex, and Haystack.

About Chroma

Chroma is an open-source embedding database designed for simplicity and developer experience. It provides a lightweight, easy-to-use API for storing, querying, and filtering embeddings locally or in the cloud. Chroma is the default vector store in many LLM frameworks like LangChain and LlamaIndex, making it extremely popular for prototyping and building RAG applications quickly.

View Chroma profile →Visit website

About Weaviate

Weaviate is an open-source vector database that combines vector search with structured filtering and generative capabilities. It supports multiple vectorization modules, hybrid search (combining BM25 and vector search), and built-in integrations with LLMs for retrieval-augmented generation. Weaviate offers both self-hosted and managed cloud deployments, with a GraphQL API that makes it easy to query complex data structures.

View Weaviate profile →Visit website

What is Vector Databases?

Purpose-built databases for storing, indexing, and querying high-dimensional vector embeddings used in semantic search, RAG, and recommendation systems.

Browse all Vector Databases tools →

Frequently Asked Questions

What is a vector database?▾

A vector database stores high-dimensional numerical representations (embeddings) of data like text, images, or audio, and enables fast similarity search across millions or billions of vectors using approximate nearest neighbor algorithms.

Do I need a dedicated vector database or can I use pgvector?▾

For small to medium datasets (under 10 million vectors), pgvector in PostgreSQL works well and avoids adding another service. For larger datasets or when you need advanced features like hybrid search and real-time indexing, a dedicated vector database is recommended.

How do I choose the right embedding model for my vector database?▾

Match the embedding model to your use case. For general text search, models like OpenAI text-embedding-3 or Cohere embed-v3 work well. For domain-specific applications, consider fine-tuned models. Always benchmark with your actual data.