Weaviate vs. Chroma
Compare Weaviate vs. Chroma for vector search workloads. We want you to choose the most suitable vector database for your use case, even if it’s not us.
As AI technologies evolve, vector similarity search has become essential for powering modern AI applications like retrieval-augmented generation (RAG), semantic search, and recommendation engines. There are various vector search solutions available, including purpose-built vector databases, vector search libraries, and traditional databases with vector search as an add-on. Selecting the right solution is crucial for the success of your AI applications.
Weaviate and Chroma both bring unique strengths to vector search workloads, each with its own capabilities and limitations. The best choice depends on your specific use case and requirements. In the following sections, we’ll compare both databases regarding functionality, scalability, and availability, helping you determine the most suitable option for your needs—even if it’s not us.
Weaviate vs. Chroma at a Glance
Yes. Purpose-built vector database
Yes. Purpose-built vector database
BSD 3-Clause License
Apache 2.0
12,027
16,982
On-prem, Cloud, Embedded
Embedded
Weaviate overview
Weaviate is an open-source vector database that simplifies the development of AI applications. It combines object and vector storage, enabling efficient vector searches with structured filtering. It supports various media types like text and images and provides fast, low-latency searches based on semantic properties.
Chroma overview
Chroma is a lightweight, open-source vector database optimized for rapid prototyping and AI workflows. Its embedded-first architecture simplifies integration into local environments, offering a seamless experience for developers working on retrieval-augmented generation (RAG) and machine learning pipelines. Chroma is perfect for fast, iterative development and research-focused AI projects.
Benchmarking Weaviate and Chroma on your own
VectorDBBench is an open-source benchmarking tool designed for users who require high-performance data storage and retrieval systems, particularly vector databases. This tool allows users to test and compare the performance of different vector database systems using their own datasets and determine the most suitable one for their use cases. Using VectorDBBench, users can make informed decisions based on the actual vector database performance rather than relying on marketing claims or anecdotal evidence.
VectorDBBench is written in Python and licensed under the MIT open-source license, meaning anyone can freely use, modify, and distribute it. The tool is actively maintained by a community of developers committed to improving its features and performance.
Check out the VectorDBBench Leaderboard for a quick look at the performance of mainstream vector databases.
Compare Weaviate with others
The Definitive Guide to Choosing a Vector Database
Overwhelmed by all the options? Learn key features to look for & how to evaluate with your own data. Choose with confidence.