Compare best Vector Database tool alternative for developer in 2023.

Vector Search

Whether you bring your own vectors or use one of the vectorization modules, you can index billions of data objects to search through.

Hybrid Search

Combine multiple search techniques, such as keyword-based and vector search, to provide state-of-the-art search experiences.

Generative Search

Improve your search results by piping them through LLM models like GPT-3 to create next-gen search experiences.

Easy to use

Get started on the free plan with an easy-to-use API or the Python client.

Scalable

Scale from zero to billions of items, with no downtime and minimal latency impact.

Pay for what you use

Start free, then pay only for what you use with usage-based pricing.

No ops overhead

No need to maintain infrastructure, monitor services, or troubleshoot algorithms.

Reliable

Choose a cloud provider and region — we'll take care of uptime, consistency, and the rest.

Secure

Pinecone is SOC 2 Type II certified, GDPR-ready, and built to keep data secure.

Easy to Use API

Provides the OpenAPI v3 specification to generate a client library in almost any programming language. Alternatively utilise ready-made client for Python or other programming languages with additional functionality.

Fast and Accurate

Implement a unique custom modification of the HNSW algorithm for Approximate Nearest Neighbor Search. Search with a State-of-the-Art speed and apply search filters without compromising on results.

Filtrable

Support additional payload associated with vectors. Not only stores payload but also allows filter results based on payload values. Unlike Elasticsearch post-filtering, Qdrant guarantees all relevant vectors are retrieved.

Rich data types

Vector payload supports a large variety of data types and query conditions, including string matching, numerical ranges, geo-locations, and more. Payload filtering conditions allow you to build almost any custom business logic that should work on top of similarity matching.

Distributed

Cloud-native and scales horizontally. No matter how much data you need to serve - Qdrant can always be used with just the right amount of computational resources.

Efficient

Effectively utilizes your resources. Developed entirely in Rust language, Qdrant implements dynamic query planning and payload data indexing. Hardware-aware builds are also available for Enterprises.

Easy to Use

With Milvus vector database, you can create a large scale similarity search service in less than a minute. Simple and intuitive SDKs are also available for a variety of different languages.

Blazing Fast

Milvus is hardware efficient and provides advanced indexing algorithms, achieving a 10x performance boost in retrieval speed.

Highly Available

Milvus vector database has been battle-tested by over a thousand enterprise users in a variety of use cases. With extensive isolation of individual system components, Milvus is highly resilient and reliable.

Highly Scalable

The distributed and high-throughput nature of Milvus makes it a natural fit for serving large scale vector data.

Cloud-native

Milvus vector database adopts a systemic approach to cloud-nativity, separating compute from storage and allowing you to scale both up and out.

Feature-rich

Support for various data types, enhanced vector search with attribute filtering, UDF support, configurable consistency level, time travel, and more.

Open source

Simple

As easy as pip install, use in a notebook in 5 seconds

Feature-rich

Search, filtering, and more

Integrations

Plugs right in to LangChain, LlamaIndex, OpenAI and others

JavaScript Client

npm install chromadb and it ships with @types

Free

Apache 2.0 and open source

Open source and free

Milvus Expertise

The creators of Milvus built Zilliz Cloud. Using our years of experience with over a thousand enterprise Milvus users across various industries, we provide state-of-the-art vector database services and solutions.

Highly Available

At Zilliz Cloud, we are dedicated to providing our customers with the best possible experience. We understand that uptime is crucial for your business, so we offer a 99.9% monthly uptime for all the products on our cloud.

Easy to Use

Deploy large-scale vector search with ease. Whether you're looking to build a recommendation engine, an NLP chat application, or a semantic search service, Zilliz Cloud can get you sprinting with just a few simple steps.

Blazing Fast

With its state-of-the-art design, Zilliz Cloud enables 10x faster vector retrieval, making its ability to quickly and efficiently handle large amounts of data unparalleled. If you're looking for a powerful and effective vector database solution, Zilliz Cloud is a clear choice.

Highly Scalable

Zilliz Cloud is a robust platform that can easily handle large-scale vector data. Its distributed and high-throughput nature ensures you can process massive amounts of data quickly and efficiently to handle a wide range of data-intensive use cases.

Security & Governance

We know that data is the lifeblood of your business so we are proud to say that Zilliz Cloud is fully compliant with the SOC 2 standard. Our commitment to compliance means that your data is always in safe hands when stored on Zilliz Cloud.

Free trial

Enterprise

Data and writes

• Documents in Vespa may be added, replaced, modified (single fields or any subset) and removed.
• Writes are acknowledged back to the client issuing them when they are durable and visible in queries, in a few milliseconds.
• Writes can be issued at a sustained volume of thousands to tens of thousands per node per second while serving queries.
• Data is replicated with a configurable redundancy.
• An even data distribution, with the desired redundancy is automatically maintained when nodes are added, removed or lost unexpectedly.
• Data corruption is automatically repaired from an uncorrupted replica of the data.
• Data is written over a simple HTTP/2 API, or (for high volume) using a small, standalone client.
• Document data schemas allow fields of any of the usual primitive types as well as collections, structs and tensors.
• Any number of data schemas can be used at the same time.
• Documents may reference each other and field from referenced documents may be used in queries without performance penalty.
• Write operations can be processed by adding custom Java components.
• Data can be streamed out of the system for batch reprocessing.

Queries

• Queries may contain any combination of structured filters, free text and vector search operators.
• Queries may contain large tensors and vectors (to represent e.g a user).
• Queries choose how results should be ranked and specify how they should be organized (see sections below).
• Queries and results may be processed by adding custom Java components - or any HTTP request may be turned into a query by custom request handlers.
• Query response times are typically in tens of milliseconds and can be maintained given any load and data size by adding more hardware.
• A streaming search mode is available where search/selection is only supported on predefined groups of documents (e.g a user's document). In this mode each node can store and serve billions of documents while maintaining low response times.

Ranking and inference

• All results are ranked using a configured ranking function, selected in the query.
• A ranking function may be any mathematical function over scalars or tensors (multi-dimensional arrays).
• Scalar functions include an "if" function to express business logic and decision trees.
• Tensor functions include a powerful set of primitives and composite functions which allows expression of advanced machine-learned ranking functions such as e.g. deep neural nets.
• Functions can also refer to Onnx models invoked locally on the content nodes.
• Multiple ranking phases are supported to allocate more cpu to ranking promising candidates.
• A powerful set of text ranking features using positional information from the documents is provided out of the box.
• Other ranking features include 2d distance and freshness.

Organizing data and presenting results

• Matches to a query can be grouped and aggregated according to a specification in the query.
• All the matches are included, even though they reside on multiple machines executing in parallel.
• Matches can be grouped by a unique value or by a numerical bucket.
• Any level of groups and subgroups are supported, and multiple parallel groupings can be specified in one query.
• Data can be aggregated (counted, averaged etc.) and selected within each group and subgroup.
• Any selection of data from documents can be included with the final result returned to the client.
• Search engine style keyword highlighting in matching fields is supported.

Configuration and operations

• Vespa can be installed using rpm files or a Docker image - on personal laptops, owned datacenters or in AWS.
• An application of Vespa is fully specified as a separate buildable artifact: An application package - individual machines or processes need never be configured individually.
• Systems may contain multiple clusters of each type (stateless and stateful), each containing any number of nodes.
• Systems of any size may be specified by two short configuration files in the application package.
• Document schemas, Java components and ranking functions/models are also configured in the application package.
• An application package is deployed as a single unit to Vespa to realizes the system desired by the application.
• Most application changes (including Java component changes) can be performed by deploying a changed application package - the system will manage its own change process while serving and handling writes.
• Most document schema changes (excluding field type changes) can be made while the system is live.
• Application package changes are validated on deployment to prevent destructive changes to live systems.
• Vespa has no single-point-of-failures and automatically routes around failing nodes.
• System logs are collected to a central server in real time.
• Selected metrics may be emitted to a third-party metrics/alerting system from all the nodes.

Open source

Asynchronize Auto Indexing

Usually the graph requires locking during indexing, which cause stop-the-world. But Vald uses distributed index graph so it continues to work during indexing.

Customizable Ingress/Egress Filtering

Vald implements it's own highly customizable Ingress/Egress filter. Which can be configured to fit the gRPC interface.

Cloud-native based vector searching engine

Horizontal scalable on memory and cpu for your demand.

Auto Indexing Backup

Vald supports to auto backup feature using Object Storage or Persistent Volume which enables disaster recovery.

Distributed Indexing

Vald distribute vector index to multiple agent, each agent stores different index.

Index Replication

Vald stores each index in multiple agents which enables index replicas. Automatically rebalance the replica when some Vald agent goes down.

Easy to use

Vald can be easily installed in a few steps.

Highly customizable

You can configure the number of vector dimension, the number of replica and etc.

Multi language supported

Golang, Java, Nodejs and python is supported.

Open source

Durable storage with precise management

High-availability storage with total control over versioning, access, and painless rollback capability.

Powerful embedding operations

Partitioning, sub-indices, averaging, and more enabled.

Nearest neighbor approximation

Achieve high-similarity recommendations using the computationally efficient HNSW algorithm.

Free