Best Embedded Vector Databases

Chroma has rapidly become the default embedded vector database for Python developers. With version 1.4.1 and over 24,000 GitHub stars, it has achieved remarkable adoption, surpassing 8 million monthly downloads. Its design philosophy is "embedded-first": you import it as a Python library and have a working vector database in three lines of code.

What sets Chroma apart is its auto-vectorization capability. You can pass raw text strings, and Chroma will automatically generate embeddings using built-in models, eliminating the need to manage embedding pipelines separately. Its v1 release introduced a Rust-based execution engine for improved performance, copy-on-write collections for safer concurrent access, and native BM25 and SPLADE hybrid search that combines keyword and semantic retrieval in a single query.

Chroma also supports a client/server mode for teams that need to share a database across services, though it remains primarily optimized for single-node embedded use. For a deeper look at how Chroma compares to other popular options, see our FAISS vs Chroma and Chroma vs Qdrant comparisons.

LanceDB takes a distinctly different approach by building on top of the Apache Arrow columnar format via its custom Lance storage format. This gives it zero-copy, disk-native access to data, meaning it can query vectors directly from disk without loading entire indices into memory. For datasets that exceed available RAM, this is a game-changer.

LanceDB is the only embedded vector database that offers first-class support for Python, JavaScript/Node.js, and Rust simultaneously, making it uniquely versatile across tech stacks. Its built-in data versioning means every mutation creates an immutable snapshot, enabling time-travel queries and effortless rollbacks. The RabitQ quantization algorithm and 30x faster KMeans clustering (introduced in recent releases) make it competitive on performance while maintaining a small resource footprint.

For multimodal AI applications that work with text, images, video, and tabular data together, LanceDB is the strongest embedded option. Its columnar format naturally accommodates heterogeneous data types in a single table, avoiding the impedance mismatch that plagues vector-only databases.

FAISS (Facebook AI Similarity Search) from Meta AI Research is the gold standard for raw vector search performance. It powers similarity search at Meta's scale, handling over 1.5 trillion vectors across production systems. While technically a library rather than a database, it is often used in an embedded fashion within applications.

FAISS offers unmatched GPU acceleration through CUDA and ROCm, with support for an extensive library of index types including IVF (inverted file), HNSW, PQ (product quantization), OPQ (optimized product quantization), and scalar quantization. This flexibility allows engineers to precisely tune the trade-off between recall accuracy, memory usage, and query speed.

The trade-off is clear: FAISS provides no database features. There is no persistence layer, no metadata filtering, no document storage, and no built-in API. You get a highly optimized search kernel and build everything else yourself. Many popular vector databases, including Chroma, actually use FAISS or hnswlib under the hood for their core search algorithms. For more details, see our FAISS vs Weaviate comparison.

sqlite-vec is a pure C extension for SQLite that adds vector search capabilities through virtual tables. As the successor to the earlier sqlite-vss project, it achieves something remarkable: vector search that runs anywhere SQLite runs, including browsers via WebAssembly, mobile devices, edge servers, and serverless functions.

Written in pure C with zero external dependencies, sqlite-vec compiles to an incredibly small binary. Its SQL-based interface means any developer familiar with SQLite can start using vector search immediately. You store vectors in virtual tables and query them with standard SQL syntax extended with distance functions. This makes it trivial to combine vector similarity with traditional relational queries in a single statement.

The limitation is that sqlite-vec lacks advanced index types and GPU support, so its performance ceiling is lower than FAISS or hnswlib. However, for applications that need vector search on constrained devices or in the browser, it is effectively the only viable option.

hnswlib is a header-only C++11 library that implements the Hierarchical Navigable Small World (HNSW) algorithm for approximate nearest-neighbor search. It is focused on doing one thing exceptionally well: fast, accurate vector search with minimal overhead.

hnswlib supports L2 (Euclidean), cosine, and inner product distance metrics, and allows incremental index construction, meaning you can add vectors to an existing index without rebuilding it from scratch. It achieves excellent recall accuracy, often above 0.99, and does so with remarkably low memory overhead compared to alternatives.

Notably, hnswlib is the search engine used under the hood by Chroma for its vector indexing. It has bindings for C++, Python, and R. While it lacks persistence, filtering, and database features, its simplicity and performance make it an excellent choice for developers who need a lightweight ANN search component to embed in custom systems.

Relative Performance Rankings

Raw Query Speed (CPU): FAISS and hnswlib are effectively tied for the fastest CPU-based search, delivering sub-millisecond latency on million-scale datasets. Chroma, which uses hnswlib internally, adds overhead from its database layer (typically 5-20ms). LanceDB achieves strong disk-based performance, while sqlite-vec is the slowest for pure vector search but competitive for combined SQL+vector queries.

GPU-Accelerated Search: FAISS is the only option with GPU support, and it delivers 10-100x speedups over CPU for large-scale batch searches. If your workload demands GPU acceleration, FAISS is the only embedded choice.

Disk Performance: LanceDB excels here with its zero-copy disk-native design. It can query datasets larger than RAM without performance cliffs, while other options either require full in-memory loading (FAISS, hnswlib) or have limited disk-based performance (Chroma).

Portability: sqlite-vec wins by a wide margin. Its ability to run in browsers, on mobile devices, and in serverless environments makes it the clear choice for constrained or distributed deployments.

RAG Prototyping and Python AI Projects

Best choice: Chroma. Its zero-config setup, auto-vectorization, and extensive LangChain/LlamaIndex integrations make it the fastest path to a working RAG pipeline. Most tutorials and courses use Chroma as the default, which means abundant learning resources and community support.

Mobile, Edge, and Browser Applications

Best choice: sqlite-vec. When you need vector search on a smartphone, IoT device, or in the browser via WebAssembly, sqlite-vec is the only realistic option. Its tiny footprint and zero-dependency design make it deployable virtually anywhere.

Multimodal Applications and Versioned Data

Best choice: LanceDB. If your application handles images, video, and text together, or if you need data versioning for reproducibility and auditing, LanceDB's Arrow-based columnar format is purpose-built for this. Its Node.js support also makes it the top pick for JavaScript-heavy teams.

High-Performance Research and GPU Workloads

Best choice: FAISS. For research teams, batch processing pipelines, or any scenario where raw throughput and GPU acceleration matter, FAISS remains unrivaled. Its rich set of index types and quantization methods give you fine-grained control over the accuracy/speed trade-off. See also our FAISS vs Qdrant comparison.

Lightweight Embedding in Custom Systems

Best choice: hnswlib. When you need a minimal, high-quality ANN search component to embed in a larger system without the overhead of a full database, hnswlib delivers excellent recall with the simplest possible API. It is ideal for academic research or building custom search infrastructure.

What is an embedded vector database?

An embedded vector database runs directly inside your application process rather than as a separate server. You import it as a library in your programming language. This eliminates network latency, simplifies deployment, and reduces operational complexity. Think of it as the difference between SQLite (embedded) and PostgreSQL (client-server) for traditional databases.

When should I use an embedded vector database vs. a client-server one?

Use embedded databases for prototyping, single-application deployments, edge/mobile applications, and datasets under a few million vectors. Switch to client-server solutions like Pinecone or Weaviate when you need multi-application access, horizontal scaling, managed infrastructure, or enterprise features like RBAC and audit logging.

Can embedded vector databases handle production workloads?

Yes, depending on the scale. Chroma and LanceDB are increasingly used in production for applications with up to several million vectors. FAISS powers production search at Meta's scale (though typically within custom infrastructure). For most applications under 10 million vectors, an embedded database is a perfectly valid production choice.

Which embedded vector database has the best performance?

FAISS delivers the best raw performance, especially with GPU acceleration. For CPU-only workloads, hnswlib matches FAISS for pure ANN search speed. Chroma offers the best balance of performance and features for typical applications. LanceDB excels for disk-based performance with datasets larger than available RAM.

Can I run vector search in the browser?

Yes, but only with sqlite-vec compiled to WebAssembly. None of the other embedded vector databases currently support browser environments. sqlite-vec's pure C implementation with zero dependencies makes it uniquely suited for WASM compilation and in-browser vector search.

How do these compare to managed vector databases like Pinecone?

Managed databases like Pinecone offer horizontal scaling, high availability, managed infrastructure, and enterprise support, but at a cost. Embedded databases are free, simpler to deploy, and eliminate network latency, but require you to manage scaling, backups, and availability yourself. For teams building their first AI application or working on smaller-scale projects, embedded databases are the smarter starting point.