Alibaba's zvec: A Lightning-Fast Vector Database That Fits In-Process

Zvec is an open-source, in-process vector database from Alibaba that delivers production-grade semantic search without external infrastructure. Built on Proxima—Alibaba’s battle-tested vector search engine that powers production workloads across the Alibaba Group—zvec enables developers to index and query billions of vectors in milliseconds using a simple Python API. Unlike traditional vector databases that require dedicated servers, zvec runs entirely in-process, making it ideal for edge AI applications, embedded systems, and rapid prototyping where deployment complexity must be minimized.

What is Zvec?

Zvec is a lightweight, feature-rich vector database designed for in-process deployment. Released as open source by Alibaba in early 2025, it represents a significant addition to the embedded database landscape, offering capabilities previously available only in server-based alternatives.¹

At its core, zvec stores and retrieves vector embeddings—numerical representations of unstructured data like text, images, or audio. These embeddings capture semantic meaning, allowing AI systems to find conceptually similar items even when they don’t share exact keywords. For example, a query for “automobile” can return documents containing “car” or “vehicle” because their embeddings occupy nearby positions in vector space.²

The database is built on Proxima, Alibaba’s high-performance vector search engine that has been battle-tested across demanding production workloads within the Alibaba Group.³ This foundation gives zvec enterprise-grade reliability and performance characteristics from day one.

💡 Key Insight: Zvec’s in-process architecture means it runs wherever your code runs—notebooks, servers, CLI tools, or edge devices—without requiring Docker containers, Kubernetes clusters, or managed cloud services.

How Does Zvec Work?

Zvec operates as an embedded library that integrates directly into applications. Installation requires only pip install zvec, after which developers can create collections, insert documents with vector embeddings, and perform similarity searches through a straightforward Python API.⁴

Core Architecture

Data in zvec is organized into collections—self-contained containers similar to tables in relational databases. Each collection follows a schema defining:

• Scalar fields: Strings, integers, floats, booleans, and arrays for metadata
• Vector fields: Dense or sparse embeddings with specified dimensions and data types⁵

import zvec

# Define collection schema
schema = zvec.CollectionSchema(
    name="example",
    vectors=zvec.VectorSchema("embedding", zvec.DataType.VECTOR_FP32, 768),
)

# Create and open collection
collection = zvec.create_and_open(path="./zvec_example", schema=schema)

# Insert documents
collection.insert([
    zvec.Doc(id="doc_1", vectors={"embedding": [0.1, 0.2, ...]}),
])

# Search by vector similarity
results = collection.query(
    zvec.VectorQuery("embedding", vector=[0.4, 0.3, ...]),
    topk=10
)

Vector Types and Indexing

Zvec supports both dense and sparse vectors, enabling different search paradigms:⁶

For dense vectors, zvec offers multiple data type options: VECTOR_FP32 (standard), VECTOR_FP16 (half precision for memory efficiency), and VECTOR_INT8 (quantized for reduced storage).⁷

Hybrid Search Capabilities

A standout feature is zvec’s hybrid search capability, which combines semantic similarity with structured filters. This allows queries like “find products similar to this image WHERE price < $100 AND category = ‘electronics’”.⁸ The database uses inverted indexes on scalar fields to efficiently filter results before applying vector similarity calculations.

⚠️ Important: Every vector field must be indexed to enable similarity search. Scalar fields should have inverted indexes built on any fields used in filtering queries.

Why Does Zvec Matter?

The vector database market has exploded alongside the generative AI boom, but most solutions follow a client-server architecture requiring significant infrastructure investment. Zvec addresses a critical gap: high-performance vector search for resource-constrained environments.

The Edge AI Challenge

Edge AI applications—running machine learning models on local devices rather than cloud servers—face unique constraints:⁹

• Limited connectivity: Cannot rely on network calls to remote databases
• Resource restrictions: Limited CPU, memory, and storage compared to data centers
• Latency requirements: User experiences demand sub-100ms response times
• Deployment complexity: Teams want to avoid managing additional services

Traditional vector databases like Milvus, Weaviate, or Qdrant excel at cloud-scale deployments but require container orchestration, persistent volumes, and network configuration. Zvec eliminates this overhead by embedding the database directly within the application process.

Performance Benchmarks

Zvec’s performance metrics demonstrate its production readiness. According to benchmarks conducted using VectorDBBench—the industry-standard evaluation framework—the database achieves:¹⁰

These tests were conducted on standard cloud instances (16 vCPU, 64 GiB RAM) using the Cohere 10M dataset, which contains 10 million 768-dimensional vectors.¹¹

Comparison: In-Process Vector Databases

The embedded vector database landscape includes several alternatives. Here’s how zvec compares to leading options as of February 2025:

Key Differentiators

Against Chroma: While Chroma offers simplicity and broad language support, zvec’s Proxima foundation provides proven scalability from Alibaba’s production environments. Chroma requires a separate server process for production deployments, whereas zvec remains fully embedded.¹²

Against LanceDB: LanceDB emphasizes multimodal data and columnar storage formats. Zvec focuses specifically on vector search optimization with native sparse vector support—useful for hybrid semantic/keyword search implementations.¹³

Against pgvector: pgvector benefits from PostgreSQL’s maturity and ACID guarantees but requires running a full Postgres server. Zvec offers lighter-weight deployment for applications that don’t need relational database features.¹⁴

Against Faiss: Faiss (Meta’s library) provides low-level vector operations but requires significant boilerplate to build complete database functionality. Zvec offers higher-level abstractions for collection management, schema evolution, and hybrid filtering.¹⁵

Real-World Applications

Zvec’s architecture suits several high-impact use cases:

Retrieval-Augmented Generation (RAG)

RAG systems enhance large language models by retrieving relevant context from knowledge bases before generating responses. Zvec enables RAG implementations on edge devices or in air-gapped environments where cloud connectivity is unavailable.¹⁶

Image Search

By storing image embeddings from models like CLIP or ResNet, applications can enable visual similarity search—finding products, artworks, or photographs that look alike without requiring manual tagging.¹⁷

Code Intelligence

Development tools can index code embeddings to enable natural language search of codebases. Developers describe functionality in plain English (“find authentication middleware”) and retrieve relevant functions regardless of naming conventions.¹⁸

Recommendation Engines

E-commerce and content platforms can generate user embeddings from behavior data and find similar items or users. Running this in-process enables real-time personalization without round-trip latency to remote services.

Getting Started with Zvec

Installation requires Python 3.10 through 3.12 and runs on Linux (x86_64/ARM64) or macOS (ARM64):¹⁹

pip install zvec

The package includes precompiled binaries, avoiding compilation dependencies. For custom builds or unsupported platforms, source compilation instructions are available in the project’s documentation.

After installation, the typical workflow involves:

1. Define a schema specifying vector dimensions and metadata fields
2. Create a collection at a local file path
3. Insert documents with pre-computed embeddings
4. Query using vector similarity with optional filters

ℹ️ Tip: Zvec is a “bring your own vectors” database. You’ll need to generate embeddings using models like OpenAI’s text-embedding-3, Cohere’s embed models, or open-source alternatives like sentence-transformers before insertion.

Frequently Asked Questions

Q: What makes zvec different from other vector databases? A: Zvec’s primary differentiator is its in-process architecture combined with Alibaba’s production-proven Proxima search engine. Unlike client-server databases requiring separate infrastructure, zvec embeds directly into applications while maintaining performance comparable to server-based solutions.

Q: Can zvec handle billions of vectors? A: According to Alibaba’s documentation, zvec can search billions of vectors in milliseconds. The exact capacity depends on available RAM and storage, as indexes must fit in memory for optimal performance.

Q: Does zvec support hybrid search (combining vector similarity with filters)? A: Yes. Zvec supports hybrid search that combines semantic similarity with structured filters on scalar fields, enabling precise queries like finding similar products within specific price ranges or categories.

Q: What embedding models work with zvec? A: Zvec is embedding-model agnostic. It accepts vectors from any source—OpenAI, Cohere, Hugging Face, or custom models—provided they match the dimensionality specified in your collection schema.

Q: Is zvec suitable for production use? A: Yes. Zvec is built on Proxima, which powers production workloads across Alibaba’s ecosystem including Taobao, Tmall, and other services handling massive scale.

The Bottom Line

Alibaba’s zvec represents a meaningful addition to the vector database ecosystem, specifically addressing the underserved embedded database segment. While it won’t replace server-based solutions for all use cases, it fills a crucial gap for edge AI, rapid prototyping, and applications where operational simplicity outweighs the need for distributed scalability.

For teams building AI applications that must run offline, on resource-constrained devices, or without DevOps support for additional infrastructure, zvec offers a compelling combination of performance, features, and deployment simplicity. Its foundation on Alibaba’s proven Proxima engine provides confidence that the underlying technology has been validated at internet scale.

The vector database landscape continues to evolve rapidly, but zvec’s focus on in-process deployment with production-grade capabilities positions it uniquely for the growing edge AI market. As more applications require semantic search capabilities without cloud dependencies, solutions like zvec will become increasingly essential infrastructure.