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What is Weaviate?

Weaviate is an open-source, AI-native database that helps organizations build and ship AI applications on a single, scalable foundation. It stores data objects alongside the vector embeddings produced by your chosen machine learning models and scales smoothly to billions of records. Teams can supply their own vectors or use Weaviate's built-in vectorization, then query their data through vector, keyword, and hybrid search to surface the most relevant results, even with complex filters. By integrating with leading large language models, Weaviate makes it straightforward to build retrieval-augmented generation, grounded question answering, and intelligent search over proprietary data. Beyond core retrieval, Weaviate offers a growing platform: the Query Agent converts natural-language questions into precise, cited queries; Engram provides managed memory that lets AI agents retain context over time; and Weaviate Embeddings handles vectorization as a managed service. Organizations can self-host under an open-source license or adopt fully managed Weaviate Cloud on AWS, GCP, or Azure, with SOC 2 Type II compliance, multi-tenancy, replication, and role-based access control. From semantic search and recommendations to agentic automation, Weaviate turns business data into AI-powered products.

What is Vald?

Vald is an advanced and scalable distributed search engine specifically optimized for swift approximate nearest neighbor searches of dense vectors. Utilizing a Cloud-Native framework, it incorporates the fast ANN Algorithm NGT to effectively identify neighboring vectors. With functionalities such as automatic vector indexing and backup capabilities, Vald can effortlessly manage searches through billions of feature vectors. The platform is designed to be user-friendly, offering a wealth of features along with extensive customization options tailored to diverse requirements. In contrast to conventional graph systems that necessitate locking during the indexing process, which can disrupt operations, Vald utilizes a distributed index graph that enables it to continue functioning even while indexing is underway. Furthermore, Vald features a highly adaptable Ingress/Egress filter that integrates seamlessly with the gRPC interface, adding to its versatility. It is also engineered for horizontal scalability concerning both memory and CPU resources, effectively catering to varying workload demands. Importantly, Vald includes automatic backup options utilizing Object Storage or Persistent Volume, ensuring dependable disaster recovery mechanisms for users. This unique combination of sophisticated features and adaptability positions Vald as an exceptional option for developers and organizations seeking robust search solutions, making it an attractive choice in the competitive landscape of search engines.

Media

Media

Integrations Supported

AWS Marketplace
Airbyte
Amazon SageMaker
Amazon Web Services (AWS)
Anthropic
Azure Marketplace
Cleanlab
DocumentDB for VS Code
Firecrawl
Gemini CLI
GitHub
Haystack
Hugging Face
Mistral AI
Parallel
Semantic Kernel
Tadata
Unstructured

Integrations Supported

Docker
Python

API Availability

Has API

API Availability

Pricing Information

Free
Open source (free); free Weaviate Cloud tier; paid Cloud plans from $45/mo.
Free Version

Pricing Information

Free
Open source
Free Version

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Supported Platforms

SaaS

Customer Service / Support

24 Hour Support
Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub
Webinars

Training Options

Documentation Hub

Company Facts

Organization Name

Weaviate

Date Founded

2019

Company Location

The Netherlands

Company Website

weaviate.io

Company Facts

Organization Name

Vald

Company Website

vald.vdaas.org

Categories and Features

AI Memory Layers

Not specified

Context Engineering

Not specified

Vector Databases

Not specified

Categories and Features

Neural Search

Not specified

Vector Databases

Not specified

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