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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 Azure AI Search?
Deliver outstanding results through a sophisticated vector database tailored for advanced retrieval augmented generation (RAG) and modern search techniques. Focus on substantial expansion with an enterprise-class vector database that incorporates robust security protocols, adherence to compliance guidelines, and ethical AI practices. Elevate your applications by utilizing cutting-edge retrieval strategies backed by thorough research and demonstrated client success stories. Seamlessly initiate your generative AI application with easy integrations across multiple platforms and data sources, accommodating various AI models and frameworks. Enable the automatic import of data from a wide range of Azure services and third-party solutions. Refine the management of vector data with integrated workflows for extraction, chunking, enrichment, and vectorization, ensuring a fluid process. Provide support for multivector functionalities, hybrid methodologies, multilingual capabilities, and metadata filtering options. Move beyond simple vector searching by integrating keyword match scoring, reranking features, geospatial search capabilities, and autocomplete functions, thereby creating a more thorough search experience. This comprehensive system not only boosts retrieval effectiveness but also equips users with enhanced tools to extract deeper insights from their data, fostering a more informed decision-making process. Furthermore, the architecture encourages continual innovation, allowing organizations to stay ahead in an increasingly competitive landscape.
Integrations Supported
Azure Marketplace
Azure OpenAI Service
Microsoft Azure
AWS Marketplace
Agno
Amazon Bedrock
Amazon SageMaker
Astro by Astronomer
Claude Code
DeepEval
Integrations Supported
Azure Marketplace
Azure OpenAI Service
Microsoft Azure
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
$0.11 per hour
Free Version
Free Trial Offered?
Supported Platforms
SaaS
Windows
Mac
On-Prem
Linux
Supported Platforms
SaaS
Customer Service / Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Training Options
Documentation Hub
Webinars
On-Site Training
Company Facts
Organization Name
Weaviate
Date Founded
2019
Company Location
The Netherlands
Company Website
weaviate.io
Company Facts
Organization Name
Microsoft
Date Founded
1975
Company Location
United States
Company Website
azure.microsoft.com/en-us/products/ai-services/ai-search/
Categories and Features
Categories and Features
Enterprise Search
Not specified
Reranking Models
Not specified
Retrieval-Augmented Generation (RAG)
Not specified
Vector Databases
Not specified