What is vector data best used for?

Vector data is best used for representing meaning and similarity, enabling machines to search, compare, and reason about unstructured data at scale and in production environments. Power semantic search where results are based on meaning rather than exact keywords (text, images, audio). Enable retrieval-augmented generation (RAG) by matching user queries to the most relevant documents or chunks. Drive recommendation systems by finding similar users, products, or content based on learned embeddings. Support multimodal applications that unify text, images, video, and audio in a single similarity space. Improve classification, clustering, and anomaly detection by operating on dense numerical representations instead of raw data. In enterprise systems, vector datasets must be versioned, validated, and reproducible to safely power downstream AI applications. Learn how to safely validate and promote vector datasets into production .

Why do you need a vector database?

You need a vector database to efficiently search, retrieve, and serve results based on semantic similarity rather than exact matches in production systems. Store and query high-dimensional embeddings produced by LLMs and neural networks for text, images, audio, or video. Enable semantic search, recommendations, and RAG pipelines where relevance is learned, not rule-based. Perform fast approximate nearest neighbor (ANN) search at scale, which traditional databases are not optimized for. Combine vectors with metadata filtering and hybrid search to improve precision and control in production systems. Support dynamic data (frequent inserts, updates, deletes) required by real-world AI applications. Handle continuous ingestion and embedding updates without rebuilding the entire system from scratch. As vector workloads mature, operational concerns – versioning, validation, and rollback – become as important as retrieval performance.

What’s the difference between vector databases and vector libraries?

Vector libraries handle static similarity search, while vector databases support dynamic, production workloads. Use vector libraries (Faiss, ScaNN) for in-memory or research-oriented benchmarks. Use vector databases (Milvus, Weaviate, or managed services like Pinecone) for mutable data and live applications. Prefer databases when you need inserts, deletes, metadata filtering, and scaling. Evaluate durability, replication, and operational overhead for long-running systems. Vector databases introduce operational complexity that must be managed across environments (dev, staging, production).

What is the best open-source database for vector search?

There is no single “best” open-source vector database, but Milvus, Weaviate, and Qdrant are the most widely adopted and production-proven choices today. Choose Milvus if you need extreme scale (billions of vectors), multiple ANN index types (HNSW, IVF, PQ), and strong ecosystem backing. Choose Weaviate if you want built-in integrations with LLM providers, hybrid search, and a developer-friendly API for semantic applications. Choose Qdrant if you prioritize payload filtering, performance predictability, and a clean, production-ready architecture. Evaluate databases based on recall@k, tail latency, metadata filtering, and operational cost, not just raw benchmarks. Plan for index rebuilds and embedding changes, which are inevitable in real LLM and RAG systems. No matter which database you choose, embedding changes and index rebuilds are inevitable in real-world LLM and RAG systems. Learn how reproducibility and rollback matter in ML and vector-based systems.

Is there a free vector database?

Yes, several free vector databases are available, primarily as open-source projects that you can run yourself. Use Milvus, Weaviate, or Qdrant as fully open-source vector databases suitable for production use, with no licensing cost. Developer-focused options like Chroma are useful for prototyping but often require additional infrastructure for enterprise deployments. Use Faiss or ScaNN if you only need an in-process vector library for experiments or static datasets. Expect to pay indirectly via infrastructure, operations, and index rebuild costs , even when the software is free. Be aware that “free tiers” of managed services often have limits on vector count, queries, or performance . Plan for embedding and index changes , which can make reproducibility and rollback difficult without version control.

How does lakeFS help manage vector embeddings and indexes?

lakeFS adds a data version control layer for managing embeddings, index artifacts, and vector-related metadata across the AI lifecycle. Version embeddings, index manifests, and encoder configs as immutable commits. Rebuild embeddings and indexes on isolated branches when models, parameters, or datasets change, without impacting production. Validate recall and latency in CI/CD before promoting a new index. Roll back instantly by serving a previous commit or tag. This allows teams to safely evolve vector search systems while meeting enterprise requirements for governance, auditability, and rollback. Learn more about lakeFS’s Git-for-data approach applied to AI and vector workloads.

Best 17 Vector Databases for 2026 [Top Picks]

In the real world, teams may have to deal with data that isn’t neatly organized into rows and columns. That’s especially true when you’re working with complex, unstructured data such as photos, videos, and natural language.

This is where vector databases come in to save the day.

What is the best vector database you can choose for your project? What are the leading vector databases on the market today? Dive into our overview of the 17 most popular vector databases on the market to understand your options and pick the best tool for your project.

Key Takeaways

Vector databases specialize in high-dimensional search: Unlike relational or NoSQL systems, vector databases are optimized for storing and querying vector embeddings used in LLM and neural network applications.
Use cases vary between libraries and databases: Vector libraries are best for static data scenarios like academic benchmarks, while full vector databases support dynamic applications such as semantic search and personalized recommendations.
Metadata and hybrid search are key differentiators: Many modern vector databases, including Pinecone, MongoDB, and Qdrant, support metadata filtering and hybrid search, enhancing relevance and control in retrieval tasks.
Open-source dominance supports customization: Most featured solutions (e.g., Milvus, Weaviate, Deep Lake, Faiss) are open source, allowing teams to tailor the database for specific infrastructure or performance needs.
Data lineage and versioning are emerging priorities: Tools like Deep Lake integrate version control and reproducibility features, enabling better tracking, rollback, and experiment management in LLM workflows.

What is a Vector Database?

Vector databases first emerged a few years ago to power a new generation of search engines based on neural networks. Today, they play a new role: helping organizations deploy applications based on large language models like GPT4.

Vector databases differ from standard relational databases, such as PostgreSQL, which were built to store tabular data in rows and columns. They’re also distinct from newer NoSQL databases like MongoDB, which store data as JSON. That’s because a vector database is designed to store and retrieve just one type of data: vector embeddings.

Vector embeddings are the distilled representations of the training data produced as an output from the training stage of the machine learning process. They serve as the filter through which fresh data is processed during inference.

What vector database solutions are available today to help you store and retrieve high-dimensional vectors? Before we move on to the review of the 17 most promising vector databases and libraries, let’s clarify the difference between these two technologies.

Vector libraries vs. vector databases

While specialized vector databases are storage systems developed for efficient management of dense vectors, vector libraries are integrated into existing database management systems (DBMS) or search engines to provide similarity search.

Vector libraries are a good choice for static data applications such as academic information retrieval benchmarks. Vector databases are useful for applications that require frequent data changes, such as e-commerce suggestions, image search, and semantic search.

17 Best Vector Databases You Should Consider in 2026

1. Pinecone

Open source? No

GitHub stars: –

What problem does it solve?

Pinecone is a managed, cloud-native vector database with a straightforward API and no infrastructure requirements. Users can launch, operate, and expand their AI solutions without the need for any infrastructure maintenance, service monitoring, or algorithm troubleshooting.

The solution processes data quickly and lets users use metadata filters and sparse-dense index support for high-quality relevance, guaranteeing speedy and accurate results across a wide range of search needs.

Key features

Detection of duplicates.
Rank tracking.
Data search.
Classification.
Deduplication.

2. MongoDB

MongoDB: https://www.mongodb.com/

GitHub stars: 25.2k

What problem does it solve?

MongoDB Atlas is the most popular managed developer data platform that can handle a large variety of transactional and search workloads. Atlas Vector Search uses a specialized vector index that is automatically synced with the core database and can be configured to run on separate infrastructure, offering the benefits of an integrated database with the independent scaling that is often why users would look to a vector database.

Key features

Integrated database + vector search capabilities
Independent provisioning for database and search index
Storage for 16 MB of data per document
High availability, strong transaction guarantees, multiple levels of data durability, archiving, and backup
Industry leader in transactional data encryption
Hybrid search

3. Milvus

Open source? Yes

GitHub stars: 21.1k

What problem does it solve?

Milvus is an open-source vector database designed to facilitate vector embedding, efficient similarity search, and AI applications. It was published in October 2019 under the open-source Apache License 2.0 and is now a graduate project under the auspices of the LF AI & Data Foundation.

The tool simplifies unstructured data search and delivers a uniform user experience independent of the deployment environment. To improve elasticity and adaptability, all components in the refactored version of Milvus 2.0 are stateless.

Use cases for Milvus include image search, chatbots, and chemical structure search.

Key features

Searching trillions of vector datasets in milliseconds.
Unstructured data management is simple.
Reliable vector database that is always available.
Highly scalable and adaptable.
Search hybrid.
Unified Lambda structure.
Supported by the community and acknowledged by the industry.

4. Chroma

Open source? Yes

GitHub stars: 7k

What problem does it solve?

Chroma DB is an open-source, AI-native embedding vector database that aims to simplify the process of creating LLM applications powered by natural language processing by making knowledge, facts, and skills pluggable for machine learning models at the scale of LLMs – as well as avoiding hallucinations.

Many engineers have expressed a desire for “ChatGPT but for data,” and Chroma offers this link via embedding-based document retrieval. It also provides ‘batteries included’ with everything teams need to store, embed, and query data, including strong capabilities like filtering, with more features like intelligent grouping and query relevance on the way.

Key features

Feature-rich: queries, filtering, density estimates, and many other features.
LangChain (Python and JavScript), LlamaIndex, and more will be added shortly.
The same API that runs in your Python notebook scales to your cluster for development, testing, and production.

5. Weaviate

Open source? Yes

GitHub stars: 6.7k

What problem does it solve?

Weaviate is a cloud-native, open-source vector database that is resilient, scalable, and quick. The tool can convert text, photos, and other data into a searchable vector database using cutting-edge machine learning models and algorithms.

It can perform a 10-NN neighbor search in single-digit milliseconds over millions of items. Engineers can use it to vectorize their data during the import process or submit their own vectors, ultimately creating systems for question-and-answer extraction, summarization, and categorization.

Weaviate modules enable the use of prominent services and model hubs like OpenAI, Cohere, or HuggingFace, as well as the use of local and bespoke models. Weaviate is designed with scale, replication, and security in mind.

Key features

Built-in modules for AI-powered searches, Q&A, combining LLMs with your data, and automated categorization.
Complete CRUD capabilities.
Cloud-native, distributed, grows with your workloads and operates nicely on Kubernetes.
Seamlessly transfer ML models to MLOps using this database.

6. Deep Lake

Open source? Yes

GitHub stars: 6.4k

What problem does it solve?

Deep Lake is an AI database powered by a proprietary storage format designed specifically for deep-learning and LLM-based applications that leverage natural language processing. It helps engineers deploy enterprise-grade LLM-based products faster via vector storage and an array of features.

Deep Lake works with data of any size, is serverless, and allows you to store all data in a single location.

It also offers tool integrations to help streamline your deep learning operations. For example, using Deep Lake and Weights & Biases, you can track experiments and achieve full model repeatability. The integration delivers dataset-related information (URL, commit hash, view ID) to your W&B runs automatically.

Key features

Storage for all data types (embeddings, audio, text, videos, images, pdfs, annotations, and so on).
Querying and vector search,
Data streaming during training models at scale.
Data versioning and lineage for workloads.
Integrations with tools like LangChain, LlamaIndex, Weights & Biases, and many more.

7. Qdrant

Open source? Yes

GitHub stars: 11.5k

What problem does it solve?

Qdrant is an open-source vector similarity search engine and database. It offers a production-ready service with an easy-to-use API for storing, searching, and managing points-vectors and high dimensional vectors with an extra payload.

The tool was designed to provide extensive filtering support. Qdrant’s versatility makes it a good pick for neural network or semantic-based matching, faceted search, and other applications.

Key features

JSON payloads can be connected with vectors, allowing for payload-based storage and filtering.
Supports a wide range of data types and query criteria, such as text matching, numerical ranges, geo-locations, and others.
The query planner makes use of cached payload information to improve query execution.
Write-Ahead during power outages, with the update log recording all operations, allowing for easy reconstruction of the most recent database state.
Qdrant functions independently of external databases or orchestration controllers, which simplifies configuration.

8. Elasticsearch

Open source? Yes

GitHub stars: 64.4k

What problem does it solve?

Elasticsearch is an open-source, distributed, and RESTful analytics engine that can handle textual, numerical, geographic, structured, and unstructured data. Based on Apache Lucene, it was initially published in 2010 by Elasticsearch N.V. (now Elastic). Elasticsearch is part of the Elastic Stack, a suite of free and open tools for data intake, enrichment, storage, analysis, and visualization.

Elasticsearch can handle a wide range of use cases – it centrally stores your data for lightning fast search, finetuned relevance, and sophisticated analytics that scale easily. It expands horizontally to accommodate billions of events per second while automatically controlling how indexes and queries are dispersed throughout the cluster for slick operations.

Key features

Clustering and high availability.
Automatic node recovery and data rebalancing.
Horizontal scalability.
Cross-cluster and data center replication, which allows a secondary cluster to operate as a hot backup.
Cross-datacenter replication.
Elasticsearch identifies errors in order to keep clusters (and data) secure and accessible.
Works in a distributed architecture that was built from the ground up to provide constant peace of mind.

9. Vespa

Open source? Yes

GitHub stars: 4.5k

What problem does it solve?

Vespa is an open-source data serving engine that allows users to store, search, organize, and make machine-learned judgments over massive data at serving time.

Huge data sets must be dispersed over numerous nodes and examined in parallel, and Vespa is a platform that handles these tasks for you while maintaining excellent availability and performance.

Key features

Writes are acknowledged back to the client, issuing them in a few milliseconds when they are durable and visible in queries.
While servicing requests, writes can be delivered at a continuous rate of thousands to tens of thousands per node per second.
Data is copied with redundancy that may be configured.
Queries can include any combination of structured filters, free text search operators, and vector search operators, as well as enormous tensors and vectors.
Matches to a query can be grouped and aggregated based on a query definition.
All of the matches are included, even if they are running on several machines at the same time.

10. Vald

Open source? Yes

GitHub stars: 1274

What problem does it solve?

Vald is a distributed, scalable, and fast vector search engine. Built with cloud-native in mind, it employs the quickest ANN algorithm, NGT, to help find neighbors.

Vald offers automated vector indexing and index backup, as well as horizontal scaling, allowing it to search across billions of feature vector data. It’s simple to use and extremely configurable – for example, the highly configurable Ingress/Egress filter you can customize to work with the gRPC interface.

Key features

Vald offers automatic backups through Object Storage or Persistent Volume, allowing for disaster recovery.
It distributes vector indexes to numerous agents, each of which retains a unique index.
The tool replicates indexes by storing each index in many agents. When a Vald agent goes down, automatically rebalance the duplicate.
Highly adaptable – you may choose the number of vector dimensions, replicas, and so forth.
Python, Golang, Java, Node.js, and more programming languages are supported.

11. ScaNN

Open source? Yes

GitHub stars: –

What problem does it solve?

ScaNN (Scalable Nearest Neighbors) is a method for efficiently searching for vector similarity at scale. Google’s ScaNN proposes a brand-new compression method that significantly increases accuracy. This allows it to outperform other vector similarity search libraries by a factor of two, according to ann-benchmarks.com.

It includes search space trimming and quantization for Maximum Inner Product Search, as well as additional distance functions like Euclidean distance. The implementation is intended for x86 processors that support AVX2.

12. Pgvector

Open source? Yes

GitHub stars: 4.5k

What problem does it solve?

pgvector is a PostgreSQL extension for searching for vector similarity. You can also use it to keep embeddings as well. Ultimately, pgvector helps you store all of the application data in one place.

Its users get to benefit from ACID compliance, point-in-time recovery, JOINs, and all of the other fantastic features we love PostgreSQL for.

Key features

Exact and approximate nearest neighbor search
L2 distance, inner product, and cosine distance
any language with a PostgreSQL client

13. Faiss

Open source? Yes

GitHub stars: 23k

What problem does it solve?

Developed by Facebook AI Research, Faiss is an open-source library for fast, dense vector similarity search and grouping. It includes methods for searching sets of vectors of any size, up to those that may not fit in RAM. It also comes with code for evaluation and parameter adjustment.

Faiss is based on an index type that maintains a set of vectors and offers a function for searching through them using L2 and/or dot product vector comparison. Some index types, such as precise search, are simple baselines.

Key features

Returns not just the nearest neighbor but also the second nearest, third nearest, and k-th nearest neighbor.
You can search several vectors at once rather than just one (batch processing).
Uses the greatest inner product search rather than a minimal Euclidean search.
Other distances (L1, Linf, etc.) are also supported to a lesser extent.
Returns all elements within a specified radius of the query location (range search).
Instead of storing the index in RAM, you can save it to disk.

14. ClickHouse

Open source? Yes

GitHub stars: 31.8k

What problem does it solve?

ClickHouse is an open-source column-oriented DBMS for online analytical processing that enables users to produce analytical reports in real time by running SQL queries. The actual column-oriented DBMS design is at the heart of ClickHouse’s uniqueness. This distinct design provides compact storage with no unnecessary data accompanying the values, which significantly improves processing performance.

It uses vectors to process data, which improves CPU efficiency and contributes to ClickHouse’s exceptional speed.

Key features

Data compression is a feature that significantly improves ClickHouse’s performance.
ClickHouse combines low-latency data extraction with the cost-effectiveness of employing standard hard drives.
It uses multicore and multiserver setups to accelerate massive queries, which is a rare feature in columnar DBMSs.
With robust SQL support, ClickHouse excels at processing a wide range of queries.
ClickHouse’s continuous data addition and quick indexing meet real-time demands.
Its low latency provides quick query processing, which is critical for online activities.

15. OpenSearch

Open source? Yes

GitHub stars: –

What problem does it solve?

This is an interesting solution among other vector databases. Using OpenSearch as a vector database brings together the power of classical search, analytics, and vector search into a single solution. The vector database features of OpenSearch help speed up AI application development by minimizing the work required for developers to operationalize, manage, and integrate AI-generated assets.

You can bring in your models, vectors, and information to enable vector, lexical, and hybrid search and analytics, with built-in performance and scalability.

Key features

As a vector database, OpenSearch may be used for a variety of purposes, such as search, personalization, data quality, and vector database engine.
Among its search use cases, you can find multimodal search, semantic search, visual search, and gen AI agents.
You can create product and user embeddings using collaborative filtering techniques and fuel your recommendation engine with OpenSearch.
To aid data quality operations, OpenSearch users can use similarity search to automate pattern matching and duplication in data.
The solution lets you create a platform with an integrated, Apache 2.0-licensed vector database that offers a dependable and scalable solution for embeddings and power vector search.

16. Apache Cassandra

Open source? Yes

GitHub stars: 8.3k

What problem does it solve?

Cassandra is a distributed, wide-column store, NoSQL database management system that is free and open-source. It was designed to handle massive volumes of data across many commodity servers while maintaining high availability with no single point of failure.

Cassandra will soon be equipped with vector search, which demonstrates the Cassandra community’s dedication to delivering dependable innovations quickly. Cassandra’s popularity is growing among AI developers and businesses dealing with big data volumes as provides them with the capabilities to build complex, data-driven applications.

Key features

Cassandra will have a new data type to facilitate the storage of high dimensional vectors. This will allow for the manipulation and storage of Float32 embeddings, which are extensively used in AI applications.
The tool will also provide a new storage-attached index (SAI) dubbed “VectorMemtableIndex” to support approximate nearest neighbor (ANN) search capabilities.
It will offer a new Cassandra Query Language (CQL) operator, ANN OF, to make it easier for users to run ANN searches on their data.
Cassandra’s new vector search feature is designed as an extension to the existing SAI framework, eliminating the need to redesign the fundamental indexing engine.

17. KDB.AI Server

Overview of the UI - kdb products — Source: *https://kdb.ai/*

Open source? No

GitHub stars: –

What problem does it solve?

KDB.AI is a knowledge-based vector database and search engine that enables developers to create scalable, dependable, and real-time apps by offering enhanced search, recommendation, and personalization for AI applications that use real-time data.

Key features

KDB.AI is unique among vector databases because it allows developers to add temporal and semantic context to their AI-powered applications.
KDB.AI integrates seamlessly with popular LLMs and machine learning workflows and tools, such as LangChain and ChatGPT.
Its native support for Python and RESTful APIs allows developers to perform common operations such as data ingestion, search, and analytics in their preferred applications and languages.

Best Vector Databases: Comparison

Database	Open Source	Supported index types
Pinecone	No	–
MongoDB	No	HNSW
Milvus	Yes	Multiple index types: FLAT, IVF_FLAT, IVF_PQ, IVF_SQ8, HNSW, HNSW_SQ, HNSW_PQ, HNSW_PRQ, and SCANN
Chroma	Yes	HNSW
Weaviate	Yes	HNSW
Deep Lake	Yes	Inverted and BM25
Qdrant	Yes	HNSW
Elasticsearch	No	HNSW (32, 8, or 4 bit) FLAT (32, 8, or 4 bit)
Vespa	Yes	HNSW
Vald	Yes	NGT
ScaNN	Yes	SCANN
Pgvector	Yes	HNSW/IVFFlat
Faiss	Yes	HNSW, IVFFlat, LSH, PQ and more
ClickHouse	Yes	HNSW
OpenSearch	Yes	HNSW
Apache Cassandra	Yes	HNSW
KDB.AI	No	Multiple index types: Flat, qFlat, IVF, IVFPQ, HNSW and qHnsw

How to Choose the Best Vector Database for Your Project

Choosing the right vector database can significantly impact your application, but it’s not always easy. There are numerous things to consider, ranging from the database’s performance and scalability to its interoperability with your current systems. When picking a vector database for your project, consider the following factors:

Search accuracy: The database should return accurate search results. This is especially relevant for applications that need high precision.
Scalability: As your data expands, the database should be able to keep up without sacrificing performance.
Performance: Evaluate the database’s speed and efficiency. This includes the speed at which data is stored, retrieved, and searched.
Language clients: These are language-specific libraries that allow developers to interface with the database. To make the integration process easier, choose one that is both intuitive and efficient.
Data type support: Ensure the database supports the data types you’ll be working with. Some databases are better suited to specific data types than others.
System integration: Consider how well the database connects with your current systems. A smooth integration can save time and resources.
Documentation: You must have detailed documentation to follow important guidelines while you build up your implementation. The documentation should also offer troubleshooting and optimization suggestions.

Expert Tip: Decouple Your Vector Store from Data Reality with Commit-Pinned Snapshots

Idan Novogroder Software Engineer

Idan has an extensive background in software and DevOps engineering. He is passionate about tackling real-life coding and system design challenges. As a key contributor, Idan played a significant role in launching, maintaining, and shaping lakeFS Cloud, which is a fully-managed solution offered by lakeFS. In his free time, Idan enjoys playing basketball, hiking in beautiful nature reserves, and scuba diving in coral reefs.

Don’t let Milvus, pgvector, Pinecone, or Weaviate dictate your end-to-end stack. Keep your embeddings, index manifests and encoder parameters versioned in lakeFS and expose a stable “semantic dataset” via commit IDs, not mutable paths.

This gives you reproducible inputs and the ability to hot-swap ANN backends (HNSW/IVF/SCANN) without breaking RAG or search relevance.

Pin RAG pipelines to a specific lakeFS commit for consistent recall/latency.
Build or rebuild indexes on a dedicated feature branch when encoders/params change.
Validate QoS (recall@k, tail latency) in CI/CD before merge.
Tag and promote successful builds
Roll back instantly by serving the previous commit/tag.

Tactical Insight: Treat index builds like release artifacts; only promote versions that pass relevance and SLO gates. Version every build, and let automation control which tag is considered production.

Tech & Workflow Context:
Use Spark or Ray to generate embeddings, dbt for upstream features, and Airflow or Dagster to orchestrate the branch → build → bench → tag → merge cycle.

Exporters publish index files (HNSW/IVF/PQ/ScaNN) or backend apply manifests, along with a resolver mapping {commit → vector backend URI}for Milvus, pgvector, Weaviate, or Pinecone.

Engineering Impact or Tradeoff:

Pros: reproducibility, backend flexibility, and one-click rollback via versioned commits.
Cons: additional storage for embeddings and index artifacts – mitigated by lakeFS zero-copy branching and retention/GC policies for untagged builds.

Conclusion

As real-world data increasingly takes the form of complex, unstructured content like images, videos, and natural language, traditional databases often fall short. Vector databases fill this gap, offering a powerful solution for managing and retrieving vector embeddings that fuel modern AI applications.

Unlike relational or NoSQL databases, vector databases are purpose-built to support the demands of neural network-based search and LLM-powered tools. We hope our detailed overview of 17 leading vector databases helps you discover which one aligns best with your project needs. Vector databases will become commonplace as AI continues to dominate the tech industry, and it’s only natural that more and more tools will emerge in this market.

How are you using vector database solutions today? Let us know in our Slack community!

Best 17 Vector Databases for 2026 [Top Picks]

Key Takeaways