Top 5 Open Source Vector Databases: A Comprehensive Comparison Guide for 2025
Introduction
Vector search, also known as vector similarity search, has quickly evolved from an experimental technology to a must-have component in many AI applications. As developers and technical leaders, we're increasingly looking for ways to handle similarity-based queries that traditional databases simply weren't designed to handle efficiently.
Whether you're building a product recommendation system or implementing semantic search, the underlying challenge is the same: how do you efficiently find the "nearest neighbors" to a query vector in a potentially massive dataset? That's where vector search engines come in.
The good news is that the open source community has stepped up with multiple high-quality options. The challenging part? Figuring out which one is right for your specific use case, technical requirements, and team expertise.
In this guide, we'll walk through the most popular open-source vector search engines available today, compare their strengths and limitations, and provide practical insights to help you make an informed decision. We'll cover everything from the technical foundations to specific implementation considerations, with a focus on real-world applications.
Understanding Vector Search: Core Concepts
Before diving into specific engines, let's establish some shared understanding of what vector search actually involves.
What Are Vector Embeddings?
At its core, vector search relies on embedding data into vectors—essentially converting information (text, images, audio, or any other data type) into lists of floating-point numbers that capture semantic meaning. These vectors typically range from dozens to thousands of dimensions.
For example, a text embedding model might encode the sentence "The weather is nice today" into a 384-dimensional vector where semantically similar sentences like "It's a beautiful day" would be positioned nearby in this high-dimensional space.
Vector Search vs. Traditional Search
Traditional search engines typically use inverted indices and exact keyword matching. Vector search, in contrast, measures the distance between vectors to find similar items, regardless of exact keyword overlap.
Consider these approaches:
Traditional keyword search matches "red leather jacket" with documents containing exactly those words. Vector search, however, can match "red leather jacket" with items that are conceptually similar, even if described as "scarlet biker coat" because it understands the semantic similarity rather than requiring exact term matches.
Key Performance Metrics
When evaluating vector search engines, several metrics matter:
Query speed is measured in milliseconds or queries per second (QPS), indicating how quickly results are returned. Recall represents the percentage of relevant results actually retrieved compared to what should have been retrieved. Index build time tells you how long it takes to create the search index, while memory usage reflects RAM requirements for both indexing and querying. Scalability refers to a system's ability to handle increasing data volumes and query loads without experiencing performance degradation.
Understanding these fundamentals will help frame our exploration of the specific engines.
Popular Vector Search Use Cases
Vector search isn't just a theoretical concept—it's powering some of the most innovative applications being built today. Here are the key use cases where vector search engines are making a significant impact:
Retrieval Augmented Generation (RAG)
RAG has become one of the most common applications of vector search, combining the power of large language models with knowledge retrieval. In RAG implementations, documents are converted to vector embeddings and stored in a vector database like Milvus, Faiss, and Zilliz Cloud. When a query arrives, the system retrieves the most relevant documents based on vector similarity. These retrieved documents provide context to an LLM, allowing for more accurate, up-to-date responses.
This approach helps address the hallucination problem in LLMs while enabling them to access domain-specific information that wasn't included in their training data.
AI Agents and Knowledge Retrieval
AI agents often need to make decisions based on relevant information scattered across various sources. Vector search enables these agents to quickly retrieve context-relevant information from large knowledge bases, identify similar past interactions or decisions, and construct memory systems that understand semantic similarity.
For developers building AI agents, the choice of vector database can significantly impact both performance and capabilities.
Recommendation Systems
E-commerce platforms, streaming services, and content sites rely heavily on recommendation engines to increase engagement. Vector search powers these systems by representing user preferences and item features as vectors, finding items similar to those a user has liked previously, and identifying users with similar taste profiles.
The right vector search engine can make the difference between recommendations that feel random versus those that seem to understand user preferences intuitively.
Semantic Search Applications
Text search that understands meaning rather than just keywords is transforming how we interact with information. Vector search enables finding conceptually similar documents even when terminology differs, understanding user intent behind queries, and supporting multilingual search where concepts align across languages.
Image and Multimedia Similarity Search
Beyond text, vector search excels at finding similar images, audio, or videos. This capability powers applications like identifying visually similar products in e-commerce, finding music with similar acoustic properties, and detecting near-duplicate media assets.
These applications require vector engines that can handle diverse embedding types efficiently.
Now that we’ve learned about the essence of vector search and its common use cases, let’s explore the top vector databases, particularly open-source options.
Milvus
Milvus is the most popular open-source vector database with more than 35,000 stars on GitHub. It first appeared in 2019 and has since gained significant traction in the developer community. Created specifically to handle large-scale similarity searches, Milvus was designed from the ground up to address the unique challenges of vector data management.
Architecture and Technical Capabilities
Milvus uses a cloud-native architecture with separated storage and compute layers. Stateless query nodes handle search requests, storage nodes manage data persistence, and coordinator nodes handle cluster management. This separation allows Milvus to scale horizontally as data volumes and query loads increase—a critical consideration for production deployments.
The platform supports multiple index types, including HNSW (Hierarchical Navigable Small World), IVF (Inverted File), DiskANN, and others, providing developers with flexibility to optimize for different workloads. Milvus also offers hybrid search capabilities, combining vector similarity with scalar filtering and full-text search, which proves valuable when search needs to consider both semantic similarity and keyword matching, as well as metadata constraints.
Milvus supports multiple distance metrics, including Euclidean, Cosine, and Inner Product, making it adaptable to various embedding types and similarity definitions. Its storage architecture includes time travel capabilities, allowing point-in-time queries and backups.
Milvus can be used to build various types of AI applications, from demos running locally in Jupyter Notebooks to massive-scale Kubernetes clusters handling tens of billions of vectors. Currently, there are three Milvus deployment options: Milvus Lite, Milvus Standalone, and Milvus Distributed.
Performance Characteristics
In benchmarks, Milvus demonstrates query latency typically in single-digit milliseconds for million-scale datasets, making it suitable for real-time applications. The platform supports ANNS (Approximate Nearest Neighbor Search) algorithms that trade perfect recall for substantial speed improvements—an essential trade-off for practical applications.
Memory usage in Milvus is managed through disk-based storage with memory caching, allowing it to handle datasets larger than available RAM. This approach makes Milvus more cost-effective for large vector collections compared to purely in-memory solutions.
For most production workloads, Milvus strikes a balance between recall accuracy and query speed, with tunable parameters that enable adjustments tailored to specific requirements. However, this flexibility comes with added complexity in configuration and optimization.
Migration Simplicity
A notable advantage of Milvus is the straightforward migration path from other vector databases. Through open-source migration tools like the Vector Transport Service (VTS) tool, moving data from other vector search engines to Milvus is simplified. This tool supports automated schema mapping, incremental data migration, and data validation during the transfer process. This makes Milvus particularly attractive for teams that have outgrown their current solution or want to standardize on a single platform.
That said, migration always involves some effort and risk, so thorough testing remains necessary, despite the use of these tools.
Zilliz Cloud: Fully Managed Milvus
While the open-source Milvus is powerful on its own, it requires local machines and engineering resources to deploy, operate, and maintain when building production-level applications. Zilliz, the engineering team behind Milvus, has created a fully managed Milvus on Zilliz Cloud, eliminating all the operational overhead for its customers so that they can invest more in creation and their business, rather than devoting all resources to infrastructure management.
This Zilliz Cloud service provides additional feature sets, simplified deployment and operations, automatic scaling and resource management, advanced security features, and SLA-backed reliability. The managed service also includes continuous updates and optimizations, eliminating the need for in-house expertise.
For teams focused on building applications rather than managing infrastructure, Zilliz Cloud provides a way to leverage Milvus without operational overhead.
Community and Ecosystem
The Milvus ecosystem has grown substantially, with an active GitHub repository that features regular releases. The project provides client SDKs for Python, Java, Go, and other languages, as well as integration with popular AI models and ML frameworks, including LangChain and LlamaIndex. Additionally, it features a growing community forum and comprehensive documentation.
This ecosystem maturity reduces implementation risks and provides multiple resources for troubleshooting. However, like any open-source project, community support can sometimes be unpredictable compared to paid support options.
Faiss
Faiss, short for Facebook AI Similarity Search, is a popular vector search library that was developed and open-sourced by Facebook AI Research (now Meta) in 2017. Unlike some other options in this comparison, Faiss was created by researchers for researchers, initially focusing on academic and experimental workloads before being adopted for production systems.
Technical Overview
Faiss takes a different approach from some other vector search solutions. It's implemented in C++ with Python bindings for performance and designed as a library rather than a standalone service. One distinguishing feature is its optimization for both CPU and GPU execution, with certain workloads seeing dramatic speedups on GPU hardware.
The library offers multiple index types tailored for various scenarios. IndexFlatL2 offers exact search with L2 distance for perfect accuracy. IndexIVFFlat implements an inverted file with flat storage for improved query speed. IndexHNSW leverages Hierarchical Navigable Small World graphs for efficient approximate search. IndexPQ utilizes product quantization for memory efficiency, allowing even modest hardware to search billions of vectors.
Strengths and Limitations
One of Faiss's major strengths is raw performance. It's often the fastest option for in-memory vector search when properly configured. The library achieves memory efficiency through clever compression techniques, such as product quantization, which can reduce vector storage requirements by an order of magnitude.
Faiss also stands out with native GPU support for even faster processing, making it ideal for research environments with access to GPU resources. The library offers fine-grained control with detailed parameter tuning options for those who want to optimize their workloads.
However, Faiss comes with notable limitations. It has no built-in persistence layer, meaning developers must handle saving and loading indexes themselves. It requires more integration work than turnkey solutions since it's a library rather than a service. Faiss is also less suited for distributed deployments without additional engineering work. So, many developers use Faiss for experimenting or prototyping.
Perhaps most significantly, Faiss has a steeper learning curve than some alternatives. The documentation, while comprehensive, assumes a strong understanding of the underlying algorithms and techniques.
Annoy
Annoy, which stands for "Approximate Nearest Neighbors Oh Yeah," was developed by Spotify and open-sourced in 2013, making it one of the older solutions in this comparison. Created specifically to power Spotify's music recommendation system, Annoy takes a distinct approach optimized for read-heavy workloads with relatively static data.
Approximate Nearest Neighbors Approach
Annoy uses random projection binary search trees as its core algorithm. Each tree splits the vector space differently, creating a forest of trees that collectively provide good approximations of the true nearest neighbors. As more trees are added to the forest, the probability of finding the true nearest neighbors increases, allowing a trade-off between accuracy and resource usage.
This approach differs significantly from the graph-based methods used by many newer vector search engines.
Performance Trade-offs
Annoy makes specific trade-offs that distinguish it from more general-purpose solutions. It's read-optimized, delivering very fast performance at query time, but this comes at the cost of write flexibility. Once built, Annoy indexes don't change—new data requires rebuilding the index.
The system is disk-based, with indexes that can be memory-mapped for efficiency. This allows Annoy to handle datasets larger than available RAM while maintaining good query performance. However, Annoy offers limited functionality beyond core approximate nearest neighbor search, lacking many features found in more comprehensive solutions.
These design choices make Annoy different from databases designed for frequent updates and complex queries.
Integration Options
Annoy offers Python bindings with scikit-learn compatibility, making it accessible to data scientists and ML engineers. Its C++ core provides good performance despite the simplified API. The library supports easy serialization and deserialization of indexes, facilitating offline build processes.
The API is simple and focused exclusively on nearest neighbor search, making it easy to learn, but it is limited in functionality. Unlike more comprehensive vector databases, Annoy requires additional infrastructure for features like persistence, scaling, and query filtering.
Weaviate
Weaviate emerged in 2019 as a different approach to vector search. Unlike pure vector databases, Weaviate combines vector search capabilities with a knowledge graph, creating a hybrid system designed to add contextual understanding to similarity queries.
What sets Weaviate apart is its graph-based data model. In Weaviate, data objects can be connected through semantic relationships, and these connections add context to vector-based queries. This allows queries to blend vector similarity with graph traversal, supporting more sophisticated searches than simple nearest-neighbor matching. For instance, a deployment might store product embeddings and also model relationships between products, categories, and brands. A user query could then return not only similar items but also those connected through shared attributes or behaviors.
This hybrid model enables expressive querying, but it also introduces additional complexity in data modeling and indexing. Developers must manage both vector embeddings and graph relationships, which can increase the learning curve and operational overhead.
Weaviate uses HNSW-based indexing for efficient vector search and supports flexible filtering applied either pre- or post-search. It scales through sharding, allowing it to handle growing datasets and query loads. However, distributed setups can become more complex to configure and operate, particularly at larger scales.
While Weaviate performs well across a variety of use cases, it's not always the top performer in pure vector search benchmarks. Its additional graph features, while powerful, can lead to slower response times when executing complex queries that combine vector search with multiple relationship traversals. This makes it better suited to applications that benefit from contextual enrichment, rather than those requiring ultra-low latency on high-throughput vector-only workloads.
Qdrant
Qdrant (pronounced "quadrant") is a newer entrant to the vector database space, first appearing in 2021. Qdrant provides both REST and gRPC APIs for interacting with the database, making it accessible from virtually any programming language. Its storage is isolated in collections, similar to tables in traditional databases, providing logical separation of different data types. The architecture offers point-in-time consistency guarantees and ACID-compliant operations for data reliability. This approach makes Qdrant more familiar to developers coming from traditional database backgrounds, reducing the learning curve.
A key strength of Qdrant is its ability to combine vector search with traditional filtering. The platform offers rich filter expressions that execute efficiently as part of the search process. Its payload-based filtering integrates directly into the search rather than being applied as a post-processing step. It also supports complex boolean conditions, including AND, OR, and NOT operations across multiple fields, and allows boosting results based on specific filter conditions—useful for nuanced ranking in hybrid search.
However, this filtering flexibility comes with trade-offs. As filter expressions become more complex or datasets grow, query performance may degrade, particularly when many filters are applied in high-cardinality fields. Additionally, while Qdrant supports distributed deployments, its horizontal scaling features are still evolving compared to more mature systems, and operational tooling around large-scale clustering remains relatively limited. These factors should be considered when evaluating Qdrant for high-scale or highly dynamic workloads.
Comparison Table: Key Features of Top Vector Search Engines
| Engine | Architecture | Filtering | Managed Option | Distributed | Update Frequency |
| Milvus | Cloud-native, storage/compute separation | Excellent | Zilliz Cloud | Yes | Real-time |
| Faiss | Library, C++ with Python bindings | Limited | No | Manual | Batch |
| Annoy | Forest of binary trees | No | No | No | Offline only |
| Weaviate | Knowledge graph + vector DB | Good | Weaviate Cloud | Yes | Real-time |
| Qdrant | Rust-based, collections | Good | Qdrant Cloud | Yes | Real-time |
Other Notable Vector Search Options
Beyond the main purpose built options highlighted above, many traditional databases start to offer vector search capability as an add-on.
Elasticsearch with Vector Search
Elasticsearch, already widely adopted for text search, has added vector search capabilities in recent versions. This functionality introduces kNN (k-Nearest Neighbors) search to the Elasticsearch ecosystem, enabling organizations to utilize their existing infrastructure for vector search requirements.
The integration with existing Elasticsearch features enables teams to combine traditional text search, faceting, and aggregations with vector similarity on a single platform. The familiar API reduces the learning curve for teams already using Elasticsearch.
This approach works well for organizations already invested in the Elastic ecosystem who need to add vector capabilities without adopting an entirely new database. However, performance may not match purpose-built vector databases for large-scale, vector-only workloads.
Vespa
Vespa is Yahoo's open source search engine that combines traditional search, vector search, and sophisticated ranking in a single platform. It offers real-time indexing and searching, with updates immediately available for query, unlike some solutions that require batch processing or index rebuilding.
The platform provides sophisticated ranking frameworks that can combine multiple signals, including vector similarity, text relevance, and business rules. It scales to large deployments with a distributed architecture and has been battle-tested in production at major internet companies.
Vespa's comprehensive feature set makes it suitable for complex search applications, though this comes with increased complexity compared to more focused solutions. It requires more resources to deploy and maintain than simpler vector search options.
pgvector
pgvector is an extension that adds vector data types and operations to PostgreSQL, allowing vector search within a traditional relational database. It supports multiple index types including IVF and HNSW for efficient similarity search on vector columns.
The key advantage is the ability to use SQL queries combining vector and relational data, making it easy to add vector search to existing applications without adopting a separate database. This option leverages existing PostgreSQL infrastructure and expertise, potentially reducing operational overhead.
The main limitation is that performance may not match dedicated vector databases for very large vector collections or high query volumes. It represents a pragmatic compromise rather than an optimized solution for vector-only workloads. What is most important, does SQL really necessary for AI workloads in the future?
Emerging Options
The vector database space continues to evolve with newer projects entering the field. Chroma focuses specifically on embeddings for LLM applications, with simplified APIs for RAG implementations. Marqo emphasizes simplicity and cloud-native operations, aiming to reduce the operational burden of vector search. LanceDB offers embedded vector search capabilities, targeting edge devices and applications that need to operate offline.
These emerging options show the continued innovation in the space, though they generally lack the production history and ecosystem maturity of more established solutions.
Choosing the Right Vector Search Engine
With so many options available, selecting the right vector search engine requires careful consideration of your specific needs and constraints.
Decision Framework
When evaluating vector search engines, start by considering your scale requirements—how many vectors will you store and query, both now and in the future? Different engines have different scaling characteristics and sweet spots.
Next, assess your query patterns. Will you perform pure vector search, or do you need to combine vector similarity with filtering, relationship traversal, or other operations? Some engines excel at pure vector search but struggle with complex hybrid queries.
Update frequency is another important consideration. If your data changes frequently or requires real-time updates, solutions like Annoy that require rebuilding indexes will be problematic. Conversely, if your data is relatively static, simpler architectures may offer performance advantages.
Integration needs matter as well. Do you need a standalone service, a library to embed in your application, or an extension to an existing database? Your current infrastructure and team expertise may make certain options more practical than others.
Finally, consider your team's expertise with specific technologies. The best technical solution on paper may not be the best choice if your team lacks the skills to implement and maintain it effectively.
Scaling Considerations
Different engines approach scaling in different ways, and understanding these differences is crucial for achieving long-term success. Milvus offers horizontal scaling with separated storage and compute, allowing independent scaling of different components as needs change. Faiss excels at vertical scaling, particularly with GPU acceleration, but requires more custom work for distributed deployments.
Your anticipated growth trajectory should influence your choice, with some solutions better suited to gradual scaling while others may require significant re-architecture as you grow.
Total Cost of Ownership
When selecting a vector search engine, consider all aspects of total cost of ownership. Infrastructure costs include RAM and CPU requirements, which vary significantly between solutions. Some engines require substantial memory for optimal performance, while others can operate effectively with more modest resources.
Operational complexity affects ongoing maintenance costs. Deployment, monitoring, and maintenance effort varies widely, with some solutions requiring specialized expertise while others integrate more easily with standard DevOps practices.
Development time is another important factor. The learning curve and integration complexity of different engines can significantly impact project timelines and success rates. Solutions with better documentation, more examples, and more intuitive APIs typically result in faster implementation.
Support options range from community forums to commercial support agreements. Consider your organization's requirements for response times and support guarantees when evaluating options.
Finally, consider potential migration costs. If your needs change, how difficult would it be to switch to a different solution? Engines with standard APIs and export capabilities provide more future flexibility.
Future-Proofing
Vector search technology is evolving rapidly; therefore, selecting a solution that can adapt to your changing needs is crucial. Examine community activity and release cadence to assess ongoing development. Projects with regular updates and active discussion forums are more likely to remain relevant and up-to-date.
Corporate backing and sustainability matter for long-term viability. Projects supported by established companies or foundations generally have more stable development trajectories.
Aligning the feature roadmap with your anticipated needs helps ensure the solution grows in directions that benefit your use cases. Finally, flexibility to adapt as requirements change provides insurance against unexpected shifts in project requirements.
Benchmarking with Real-world Workloads
Benchmark results are often the first thing teams look at when comparing vector search engines, but many published benchmarks fail to reflect real-world usage. Synthetic tests tend to focus on idealized conditions—fixed datasets, uniform queries, and read-heavy workloads—while ignoring the complexities of real applications. In production, your system may need to support frequent updates, concurrent queries, multi-modal filtering, and hybrid search across structured and unstructured data. These challenges can drastically affect actual performance, scalability, and reliability.
To make an informed choice, prioritize benchmarks that replicate your expected workload patterns as closely as possible. Testing with real datasets, realistic query volumes, and operational constraints will provide a more accurate picture of how a vector search engine performs in your environment.
VDBBench is an open-source benchmark designed from the ground up to simulate production reality. Unlike synthetic tests that cherry-pick scenarios, VDBBench pushes databases through continuous ingestion, rigorous filtering conditions, and diverse scenarios, just like your actual production workloads.
VDBBench GitHub: https://github.com/zilliztech/VectorDBBench.
Conclusion and Next Steps
Vector search has moved beyond niche applications to become a fundamental building block for many modern applications. The open source ecosystem offers multiple strong options, each with distinct advantages and trade-offs.
For most teams just starting with vector search, Milvus provides a good balance of features, performance, and operational simplicity. Its comprehensive functionality and growing ecosystem make it suitable for a wide range of use cases, while fully managed options like Zilliz Cloud reduce operational overhead.
For specific needs, alternatives like Faiss (performance-focused), Weaviate (knowledge graph integration), Qdrant (filtering capabilities), or Annoy (read-optimized workloads) may be better fits.
Whatever you choose, start small, benchmark thoroughly against your specific workload, and validate assumptions before committing to a production deployment. Vector search technology continues to evolve rapidly, so staying engaged with the community around your chosen solution is essential for long-term success.
Ready to get started? Most of these projects offer excellent quickstart guides, Docker containers for easy experimentation, and active communities eager to help newcomers. The best way to evaluate is to build a small proof of concept with your actual data and query patterns.
Happy searching!
Fendy FengFendy Feng is the Technical Marketing Writer at Zilliz. She has extensive experience developing and enhancing the impact of open-source projects in various global markets by producing high-quality, tailored content. Before joining Zilliz, Fendy worked as a Content Strategist at PingCAP, a fast-growing E-Series startup renowned for its open-source distributed SQL database.
- Introduction
- Understanding Vector Search: Core Concepts
- Popular Vector Search Use Cases
- Milvus
- Faiss
- Annoy
- Weaviate
- Qdrant
- Comparison Table: Key Features of Top Vector Search Engines
- Other Notable Vector Search Options
- Choosing the Right Vector Search Engine
- Conclusion and Next Steps
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