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Weaviate

  • What it is:Weaviate is an open-source, cloud-native vector database that enables semantic search and AI-powered applications by storing and searching both objects and vectors at scale.
  • Best for:Teams building RAG apps, Enterprises needing hybrid deploy, Knowledge graph use cases
  • Pricing:Free tier available, paid plans from from $25/mo ($0.095 per 1M vector dimensions)
  • Rating:88/100Very Good
  • Expert's conclusion:Weaviate is the best choice for organizations that want to make an investment in sophisticated AI infrastructure that includes vector search along with structured data; particularly when flexibility of deployment and community support are important.
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Reviewed byMaxim Manylov·Web3 Engineer & Serial Founder

What Is Weaviate and What Does It Do?

The Netherlands based Weaviate is a company focused on development and support of an open-source AI native vector database which provides scalable semantic search capabilities to help developers build applications with AI. Weaviate was previously known as SeMi technologies but was rebranded after they had established their cloud native infrastructure for managing unstructured data using machine learning (ML) models. Weaviate supports a world wide developer and enterprise community of developers and businesses creating and deploying generative AI applications such as recommendation systems and chatbots.

Active
📍Amsterdam, Netherlands
📅Founded 2019
🏢Private
TARGET SEGMENTS
DevelopersEnterprisesAI BuildersMLOps Teams

What Are Weaviate's Key Business Metrics?

📊
1.6M+
Downloads
📊
$67.7M
Funding Raised
📊
Global
Countries
👥
Enterprises including Fortune 500
Customers
📊
$200M+
Valuation
Rating by Platforms
4.6/ 5
G2(29 reviews)
Regulated By
SOC 2(Global)HIPAA Compliant(USA)

How Credible and Trustworthy Is Weaviate?

88/100
Excellent

Weaviate has demonstrated significant credibility from substantial funding, excellent user feedback, business grade security features, and a mature open source product being used in production by some of the largest companies in the world.

BREAKDOWN
Product Maturity90/100
Company Stability85/100
Security & Compliance95/100
User Reviews85/100
Transparency90/100
Support Quality88/100
TRUST SIGNALS
Open-source with 1.6M+ downloadsBacked by Index Ventures, Battery VenturesSOC 2 and HIPAA compliant99.9% uptime in cloud offeringsUsed by Fortune 500 enterprises

What is the history of Weaviate and its key milestones?

2019

Company Founded

SeMi Technologies was founded in Amsterdam, The Netherlands by Bob van Luijt, Etienne Dilocker, and Micha Verhagen.

2020

Seed Funding

SeMi received a $1.6 million seed investment and developed its first version of the Weaviate vector database.

2022

Series A Funding

SeMi received a $16.5 million Series A investment and began integrating with popular ML platforms.

2023

Rebrand & Series B

SeMi changed its name to Weaviate and received a $50 million Series B investment from Index Ventures at a $200 million valuation.

2024

Major Partnerships

Weaviate partnered with Snowflake and Amazon Web Services (AWS) to improve the ability to deploy applications for use cases involving AI.

What Are the Key Features of Weaviate?

✨
Hybrid Search
Weaviate combines vector semantic search with keyword BM25 and additional filters to optimize the results of a search query within milliseconds.
✨
Vectorizer Modules
Weaviate includes pre-built integrations with OpenAI, Cohere, Hugging Face, and others to automatically create embeddings upon import.
💬
RAG Support
Weaviate provides out-of-the-box retrieval-augmented generation with generative search and reranking capabilities.
✨
Multi-Tenancy
Weaviate is natively multi-tenant with tenant isolation to provide secure and efficient scalability across teams.
✨
Vector Index Compression
Weaviate reduces the memory footprint for large datasets while maintaining search performance.
✨
Modular Architecture
Weaviate can be extended with modules for custom vectorizers, backup solutions, and workflows for AI agents.
✨
Billion-Scale Performance
Weaviate is capable of performing low latency searches over billion(s) of vectors using a Go-based architecture.

What Technology Stack and Infrastructure Does Weaviate Use?

Infrastructure

Cloud-native with Kubernetes orchestration, deployable on AWS, GCP, self-hosted

Technologies

GoPythonKubernetesGraphQLREST API

Integrations

OpenAICohereHuggingFaceAWSGCPSnowflake

AI/ML Capabilities

AI-native with built-in support for vector embeddings, hybrid search, RAG pipelines, and integrations with major ML model providers like OpenAI and Cohere

Derived from official documentation, GitHub repo, and engineering announcements

What Are the Best Use Cases for Weaviate?

AI Developers
Developers are able to rapidly prototype semantic search and recommendation systems using Weaviate's open-source vector database which includes simple vectorization.
Enterprise Data Teams
Enterprise RAG with hybrid search, multi-tenancy and features related to compliance (e.g., SOC 2 and HIPAA)
MLOps Engineers
Cloud deployments of billion-scale vector indexes, including backups, and integrations with cloud services
Chatbot Builders
Creation of ground-based AI agents through generative search which are used to decrease hallucinations using proprietary data.
NOT FORHigh-Frequency Trading
Not designed for sub-millisecond latency for real-time trading applications.
NOT FORSolo Hobbyists
Overkill for apps that do not require vector indexing or AI/ML capabilities; standard database solutions may be preferable.

How Much Does Weaviate Cost and What Plans Are Available?

Pricing information with service tiers, costs, and details
☐Service$CostℹDetails🔗Source
Free Sandbox$014-day free trial, sandbox cluster with full core features including hybrid search, compression, multi-tenancy, RBAC security, community support via Slack and forumhttps://weaviate.io/pricing
Standardfrom $25/mo ($0.095 per 1M vector dimensions)Email support business hours, monitoring, multi-AZ, phone escalation 1bd S1, 2bd S2https://weaviate.io/deployment/serverless
Professionalfrom $135/mo ($0.145 per 1M vector dimensions)24/7 support, phone escalation 4h S1, 8h S2, 1bd S3, high availability optionalhttps://weaviate.io/deployment/serverless
Business Criticalfrom $450/mo ($0.175 per 1M vector dimensions)24/7 support, phone escalation 1h S1, 4h S2, 8h S3, high availability optionalhttps://weaviate.io/deployment/serverless
EnterpriseCustom quote (starting ~$10k/year)Dedicated VPC deployment, premium support, annual contracts based on AI unitsAWS Marketplace
Free Sandbox$0
14-day free trial, sandbox cluster with full core features including hybrid search, compression, multi-tenancy, RBAC security, community support via Slack and forum
https://weaviate.io/pricing
Standardfrom $25/mo ($0.095 per 1M vector dimensions)
Email support business hours, monitoring, multi-AZ, phone escalation 1bd S1, 2bd S2
https://weaviate.io/deployment/serverless
Professionalfrom $135/mo ($0.145 per 1M vector dimensions)
24/7 support, phone escalation 4h S1, 8h S2, 1bd S3, high availability optional
https://weaviate.io/deployment/serverless
Business Criticalfrom $450/mo ($0.175 per 1M vector dimensions)
24/7 support, phone escalation 1h S1, 4h S2, 8h S3, high availability optional
https://weaviate.io/deployment/serverless
EnterpriseCustom quote (starting ~$10k/year)
Dedicated VPC deployment, premium support, annual contracts based on AI units
AWS Marketplace
💡Pricing Example: Storing 10M vectors (1536 dim, ~15B dimensions)
Weaviate Standard$~85/month
$0.095 x 15B dimensions / 1M
Weaviate Professional$~130/month
$0.145 x 15B dimensions / 1M + base fee
💰Savings:Predictable storage-based pricing vs query-based

How Does Weaviate Compare to Competitors?

FeatureWeaviatePineconeQdrantChroma
Core functionalityHybrid search, knowledge graphsVector similarity searchAdvanced filteringEmbeddings storage
Starting Price$25/mo$70/mo$0 (OSS) $0.014/hr cloud$0 (open source)
Free tier availabilityYes (Sandbox 14 days)YesYes 1GBYes
Enterprise featuresSSO, VPC, SLA, audit logsCompliance, private networkingSOC 2, HIPAA-readyN/A (self-hosted)
API availabilityGraphQL, RESTREST APIREST, gRPCPython API
Integration countModular ML modulesLangChain, LlamaIndexHigh with OSSAI frameworks
Support optionsSlack/email to 24/7 SLAEnterprise supportEnterpriseCommunity
Security certificationsRBAC baselineEncryption keysSOC 2 Type IIN/A
Performance (QPS)Good (791 QPS)Excellent at scaleExcellent (Rust)Suitable small
Core functionality
WeaviateHybrid search, knowledge graphs
PineconeVector similarity search
QdrantAdvanced filtering
ChromaEmbeddings storage
Starting Price
Weaviate$25/mo
Pinecone$70/mo
Qdrant$0 (OSS) $0.014/hr cloud
Chroma$0 (open source)
Free tier availability
WeaviateYes (Sandbox 14 days)
PineconeYes
QdrantYes 1GB
ChromaYes
Enterprise features
WeaviateSSO, VPC, SLA, audit logs
PineconeCompliance, private networking
QdrantSOC 2, HIPAA-ready
ChromaN/A (self-hosted)
API availability
WeaviateGraphQL, REST
PineconeREST API
QdrantREST, gRPC
ChromaPython API
Integration count
WeaviateModular ML modules
PineconeLangChain, LlamaIndex
QdrantHigh with OSS
ChromaAI frameworks
Support options
WeaviateSlack/email to 24/7 SLA
PineconeEnterprise support
QdrantEnterprise
ChromaCommunity
Security certifications
WeaviateRBAC baseline
PineconeEncryption keys
QdrantSOC 2 Type II
ChromaN/A
Performance (QPS)
WeaviateGood (791 QPS)
PineconeExcellent at scale
QdrantExcellent (Rust)
ChromaSuitable small

How Does Weaviate Compare to Competitors?

vs Pinecone

Weaviate is an open-source solution that includes hybrid search with knowledge graphs for complex relationships, focusing on a variety of customizable enterprise solutions; Pinecone provides a fully-managed solution focused on ease of use and scalability for production RAG, with premium pricing, but limited customization.

Better for hybrid search and self-hosted applications is Weaviate; best for hands-off, managed service is Pinecone.

vs Qdrant

Both offer open-source as well as managed; Weaviate has strengths in both semantic and structural data, while Qdrant has strength in metadata filtering and raw speed (Rust); however, Qdrant is less expensive, while Weaviate is stronger in hybrid search, but possibly more operationally complex.

Best for cost-sensitive applications with high filter workloads is Qdrant; best for knowledge-graph applications is Weaviate.

vs Chroma

Chroma is an open-source solution that is designed for prototyping and AI workflows, and does not support large-scale or SLA-based enterprise environments; Weaviate, by contrast, provides managed options, hybrid features, and is better suited for production environments but can be more operationally complex.

Best for quick prototype development is Chroma; best for scalable enterprise deployment is Weaviate.

What are the strengths and limitations of Weaviate?

Pros

  • Fastest time to spin-up and push data into the platform for rapid prototyping.
  • Provides hybrid search functionality, allowing vector search results to be filtered against structured queries.
  • Offers open-source flexibility, providing both self-hosted and managed deployment options.
  • Provides knowledge graph capabilities to handle data relationships beyond pure vector relationships.
  • Is modularly architected, supporting multiple index types and backend technologies.
  • Has strong community documentation and AI-assisted assistance to make navigation and learning faster and easier.
  • Uses several compression techniques (PQ/BQ), to provide cost-effective storage for indexed data.

Cons

  • Issues with scaling performance — latency of unpredictable nature at peak load levels
  • Operational complexity – more administration compared to rivals that are fully managed
  • Pricing per dimension – may be higher cost for applications that perform many queries
  • Configuration required for optimal performance at moderate loads
  • Steep learning curve – requires time to learn and apply advanced hybrid capabilities
  • Not as battle tested as incumbents – ultra-large-scale
  • Tiers of support – Community only included in free/basic plans

Who Is Weaviate Best For?

Best For

  • Teams building RAG apps — Easy and fast to develop with hybrid search and LLM integration
  • Enterprises needing hybrid deploy — Can be either cloud-based or self-hosted with options for VPC and SLA’s provide flexibility and convenience
  • Knowledge graph use cases — Effectively combines structured relationships with vectors
  • Prototyping AI apps — Provides a free sandbox and quick-start to reduce infra headaches
  • Mid-size orgs (100-1000) — Scales on a pay-as-you-go basis with usage and provides no lock-in

Not Suitable For

  • Ultra-high QPS real-time apps — Latency issues at scale; Consider using Qdrant or Pinecone
  • Budget startups minimal budget — Costs add up when deploying to cloud; Self-hosted Chroma will be less expensive
  • Hands-off no DevOps teams — Has more configuration than Pinecone; Fully-managed Pinecone is recommended
  • Simple embedding storage only — Too much overhead; Basic open source alternatives like Chroma are sufficient

Are There Usage Limits or Geographic Restrictions for Weaviate?

Free Trial
14 days sandbox cluster
Pricing Basis
Per 1M vector dimensions stored/month
Data Pricing
$0.095-$0.175/1M dims by SLA tier
Cluster Lifetime
Infinite until terminated, hibernation possible
Support SLA
Business hours (Standard) to 1h S1 (Business Critical)
Deployment
Shared/multi-cloud (AWS/Azure/GCP), VPC Enterprise
Security Baseline
RBAC in sandbox, advanced in paid

Is Weaviate Secure and Compliant?

RBAC SecurityRole-based access control baseline in sandbox and all tiers
Multi-AZ RedundancyMultiple availability zones across AWS, Azure, GCP
High AvailabilityOptional in Professional and above tiers
EncryptionCustomer-managed keys in Enterprise, baseline encryption
Compliance SupportSOC 2 in cloud offerings, HIPAA-ready enterprise
Monitoring & AuditBuilt-in monitoring all packages, audit trails Enterprise
VPC DeploymentCustomer-managed VPC in Enterprise plans

What Customer Support Options Does Weaviate Offer?

Channels
24/7 via support@weaviate.io24/7 ticketing system24/7 hotline for escalations (Premium/Premium Dedicated plans only)Private Slack channel (Premium/Premium Dedicated plans only)Community support (all tiers)Comprehensive online library with tutorials and troubleshooting
Hours
24/7 for all support channels
Response Time
Severity 1 (Critical): 1 hour - 1 business day depending on plan; Severity 2 (High): 4-8 hours depending on plan; Severity 3 (Medium): 8 hours - 3 business days depending on plan; Severity 4 (Low): 1-5 business days depending on plan
Specialized
Technical Account Team support available for Premium and Premium Dedicated plans (1-4 hours per month depending on tier)
Business Tier
Business Critical Support offers 1-hour response for critical issues with 24/7 hotline; Premium Dedicated offers 99.9% SLA
Support Limitations
•Free tier limited to community support only via Slack and Forum
•Phone support available for Premium and Premium Dedicated plans only
•Flex plan support limited to business hours (9am-5pm)

What APIs and Integrations Does Weaviate Support?

API Type
REST API, GraphQL API, and gRPC for flexible querying and data retrieval
Authentication
API Key-based authentication for cluster access
Client Libraries
Official SDKs for Python, TypeScript/JavaScript, and Go
Documentation
Comprehensive API documentation at docs.weaviate.io with examples, tutorials, and GraphQL schema reference
Webhook/Integration Support
Supports integration with 20+ ML model providers including OpenAI, Cohere, and Hugging Face for embedding models
Use Cases
Semantic search, retrieval augmented generation (RAG), vector similarity search, hybrid search combining vector and keyword matching, recommendation engines, anomaly detection
Data Format
Supports storage of both vector embeddings and structured object data with relationships
Rate Limits
Scales with subscription tier; pay-as-you-go pricing based on vector dimensions stored and queries executed

What Are Common Questions About Weaviate?

Weaviate is an open-source AI Vector Database designed to store data objects along with their vector embeddings. This allows for semantic search, retrieval-augmented generation (RAG) and agentic AI workloads through the combination of vector similarity with structured data relationships.

Weaviate is designed specifically for AI Applications, providing the ability to combine vector embeddings with object storage and knowledge graphs. Weaviate also allows for semantic search based on meaning rather than keyword matching, which makes it well-suited for AI powered Search, RAG applications and Intelligent Agents.

Weaviate is deployed across several different methods including Weaviate Cloud for Managed Hosting with free trials, self-hosted Open Source Deployment, Local Development Environments and Marketplace Deployments on platforms such as AWS. Select based upon your requirements for scale and regulatory compliance.

Yes, Weaviate is Open-Source and Available via GitHub. You can self-host it, edit the code, and deploy it wherever you want. The Weaviate Cloud option is the managed Service Option for users who prefer to host their own Infrastructure.

Weaviate offers REST API, GraphQL API, and gRPC APIs for querying. Client Libraries have been developed for Python, TypeScript/JavaScript, and Go which will make integrating with your application very simple.

Yes, Weaviate has integrations with over 20 Model Providers including OpenAI, Cohere, and Hugging Face. In Weaviate Cloud you can also leverage our built-in Embedding Services as well as connect your own models for generating embeddings.

Weaviate Cloud includes features such as SOC 2 Compliance, RBAC (Role Based Access Control), Encryption and enterprise grade security controls. Self Hosted Deployments can be customized and deployed securely.

Yes, Weaviate is designed to scale horizontally seamlessly to Billions of entries, we provide Auto Scaling which adjusts dynamically to workload changes and optimizes cost, this makes Weaviate an ideal solution regardless of the application size.

Weaviate Cloud offers a 14 day Free Trial with Pay As You Go Pricing by Vector Dimensions Stored. We offer Flex, Premium (Shared) and Premium (Dedicated) Plans with varying levels of Support and Performance. The Self Hosted Version of Weaviate is completely Free.

Is Weaviate Worth It?

Weaviate is a mature and proven open source Vector Database that brings together Semantic Search, Structured Data Relationships and Knowledge Graphs. With a growing Ecosystem and over 50k developers participating, and flexibility in how you choose to deploy Weaviate, it is an excellent choice to power the AI Applications of all types from RAG Systems to Intelligent Agents. Weaviates Architecture is designed to strike the balance between Ease Of Use and Enterprise Grade Features and Capabilities which makes Weaviate an attractive option to companies seriously looking for reliable AI Infrastructure.

Recommended For

  • Production Teams using Relational Artificial General Intelligence (RAG) and other forms of AI Applications which require both Reliability and Compliances.
  • Companies which need to have their deployment choices flexible (Cloud, Self Hosted or Hybrid).
  • Development Teams which are comfortable with using customizing Open Source Software for their Application Infrastructure.
  • Companies who wish to combine Semantics Search with Structured Data Relationship Requirements.
  • AI builders who want to have an engaged community of developers as well as extensive documentation

!
Use With Caution

  • Teams that require around-the-clock telephone support should go with the premium option (premium will cost additional).
  • Organizations with very low latency expectations should test performance for your use case.
  • Companies with little or no prior knowledge of vector databases may need education/training to be able to maximize value from the product.
  • Highly regulated companies should confirm what regulatory compliance requirements you would need to meet based on your chosen deployment model.

Not Recommended For

  • Teams with budget constraints, but need simple keyword searches — the traditional search products are going to be more cost effective than Weaviate.
  • Organizations wanting a completely managed solution with minimal configuration — Pinecone has a much less hands-on approach.
  • Teams that can afford to put a high level of risk tolerance into mission critical SLA’s on unproven hardware — start by looking at managed options.
Expert's Conclusion

Weaviate is the best choice for organizations that want to make an investment in sophisticated AI infrastructure that includes vector search along with structured data; particularly when flexibility of deployment and community support are important.

Best For
Production Teams using Relational Artificial General Intelligence (RAG) and other forms of AI Applications which require both Reliability and Compliances.Companies which need to have their deployment choices flexible (Cloud, Self Hosted or Hybrid).Development Teams which are comfortable with using customizing Open Source Software for their Application Infrastructure.

What do expert reviews and research say about Weaviate?

Key Findings

Weaviate is a well funded, production ready open source vector database that provides good technical leadership and has a large (50,000+) active developer community. Weaviate also provides knowledge graph capabilities, flexible deployment options (cloud/self-hosted) and modular ML model integration. Pricing was recently simplified (October 2025) and now there are clear and easy to understand pay-as-you-go models for each tier with defined support SLA’s.

Data Quality

Excellent - comprehensive public information from official website, documentation, GitHub, support terms, and pricing pages. Company maintains transparent support documentation and regular updates. Some enterprise-specific pricing requires sales contact.

Risk Factors

!
Open source software continues to evolve over time which means it can contain “breaking” changes.
!
There are many competitive offerings (Pinecone, Qdrant, Milvus) from other vendors that are also well funded.
!
Self hosted deployment requires some operational knowledge to ensure that the solution runs reliably in production.
!
Optimizing performance of Weaviate depends on the correct configuration of indexes and compression.
Last updated: January 2026

What Additional Information Is Available for Weaviate?

Community & Ecosystem

Weaviate has a thriving community of 50,000+ AI builders with active Discord, Slack, and forum channels. Weaviate Academy provides structured learning paths including courses like 'A Quick Tour of Weaviate' for developers new to vector databases. Regular community events and office hours foster knowledge sharing and provide direct access to the product team.

Open Source Foundation

Weaviate is fully open-source and available on GitHub, allowing community contributions and customization. The Apache 2.0 license provides commercial flexibility. Active development with regular releases ensures continuous improvements and feature additions driven by community needs.

Enterprise Capabilities

Enterprise deployments include SOC 2 Type II compliance, HIPAA readiness, RBAC, data replication for high-availability, zero-downtime updates, and dedicated technical account teams. Premium Dedicated plans offer 99.9% SLA and 1-hour critical response times for mission-critical applications.

Product Integration

Weaviate Agents enable pre-built agentic services for Weaviate Cloud users, reducing manual workflow development. Built-in embedding service eliminates dependency on external providers. Native integrations with popular ML frameworks and model providers (OpenAI, Cohere, Hugging Face) streamline AI application development.

Deployment Flexibility

Deploy Weaviate in Weaviate Cloud (managed), self-hosted on your infrastructure, via AWS Marketplace, or in Kubernetes environments. Local development containers enable experimentation before production deployment. Multi-deployment support accommodates teams with varying infrastructure preferences and compliance requirements.

Competitive Positioning

Compared to Pinecone (managed simplicity), Weaviate offers greater deployment flexibility and open-source transparency. Versus Milvus (high-speed searches), Weaviate excels in structured data relationships. Against Qdrant, Weaviate provides stronger knowledge graph capabilities. Each serves different organizational priorities.

Industry Recognition

Weaviate is recognized as a leading vector database solution by industry analysts and technical communities. Featured in major tech publications and used by organizations across various industries for RAG systems, semantic search, and intelligent agent applications.

What Are the Best Alternatives to Weaviate?

  • •
    Pinecone: A managed Vector Database that emphasizes both performance and simplicity, along with minimal management requirements due to the hosted infrastructure model and minimal operational overhead. Suitable for Teams who require an easy-to-use experience and managed services offering; Competitive Pricing Model; Fast Query Performance - however less flexibility in regards to deployment options and higher cost per query than Weaviate when scaled up. Suitable for Teams that desire a "turn-key" Vector Search solution with no need to manage their own Infrastructure. (pinecone.io)
  • •
    Milvus: An Open-Source Vector Database recognized for its ability to perform fast searches and to process high volumes of queries with sub-millisecond latency. Ideal for Analytics Workloads and Use Cases where Velocity of Search is the most important aspect. Has lower emphasis on Structured Data Relationships compared to Weaviate but provides the fastest raw Search Speed of the Solutions listed here. (milvus.io)
  • •
    Qdrant: An Open-Source Vector Database that places heavy emphasis on Production Readiness and Filtering Capabilities. Provides a Hybrid Search Solution that combines Vectors with Metadata Filtering. Faster Startup Time compared to Weaviate and lighter-weight solution. Lower Focus on Knowledge Graph Centric than Weaviate but specializes in Pure Vector Search. Ideal for Teams seeking Vector Similarity with Complex Filtering. (qdrant.tech)
  • •
    PostgreSQL with pgvector: Adds Vector Capabilities to PostgreSQL, providing the ability to store and search relational data and embeddings in a single database. Utilizes Familiar SQL and PostgreSQL Ecosystem. Smaller footprint for Teams already utilizing Postgres; However, Optimized for Large-Scale Vector Workloads compared to Dedicated Vector Databases. Ideal for Small-Medium Scale Applications or existing Postgres Users. (postgresql.org)
  • •
    Elasticsearch with Vector Search: (89.) Elasticsearch adds a layer of vector search allowing for semantic search in addition to powerful full-text search and analytics. This is ideal for companies using Elasticsearch for search and analytics that wish to incorporate some level of artificial intelligence into their existing solution. It may not be as effective for pure vector or other types of searches however it will be highly effective for any combination of both text and vector searches. For example, an organization wishing to find all content related to "hotel room" from a vector search would also be able to see all results that contain the word "room". (elastic.co)
  • •
    MongoDB Atlas Vector Search: (90.) Vector search has been added to MongoDB’s document database so you can do AI-powered searching on your documents. Ideal for companies that are currently using MongoDB and need to add vector search functionality but don’t want to have to implement another tool. Similar to Weaviate, less effective at pure vector searches compared to some of the more specialized vector search tools; however very effective for those that require hybrid search functionality. Also a great option for those that are already heavily invested in the MongoDB ecosystem and looking for a native way to perform semantic search on their document collections. (mongodb.com)

What Are Weaviate's Vector Db Performance?

1,900+ QPS
Queries Per Second
2.80ms
Mean Latency
4.43ms
P99 Latency
97.24%
Recall@10
6.1 GB
Memory Efficiency

What Is Weaviate's Vector Db Scalability?

Max Vectors
Billions of vectors
Horizontal Scaling
Distributed architecture with automatic clustering and multi-node support
Sharding Support
Automatic partitioning and dynamic index switching from flat to HNSW at 10,000+ objects
Replication
Multi-replica support with configurable consistency options and zero-downtime updates

What Vector Db Index Types Does Weaviate Support?

HNSW (Hierarchical Navigable Small World)Flat IndexDynamic Index

HNSW is the default index type with logarithmic query complexity; dynamic index automatically switches from flat to HNSW as data grows

What Vector Db Features Does Weaviate Offer?

Hybrid Search

(91.) Combines keyword (BM25) search with vector similarity search to allow users to choose how much weight they place on each.

Metadata Filtering

(92.) Allows users to filter the results using SQL-like where clauses to get the desired results.

Semantic Search

(93.) Performs vector-based similarity searches across large amounts of unstructured data.

Real-time Indexing

(94.) Supports full CRUD operations including the ability to update data incrementally.

Multi-Tenancy

(95.) Allows for isolated collections to create multiple tenant solutions.

RAG Support

(96.) Provides native support for Retrieval Augmented Generation (RAG) workflows.

Agentic Workflows

(97.) Includes database agents to automate querying and aggregation operations.

What Is Weaviate's Vector Db Deployment?

Cloud Managed
Weaviate Cloud with shared and dedicated infrastructure, sandbox evaluation tier available
Self-Hosted
Docker Compose, standalone binary, and local containerized deployment
Kubernetes
Helm charts and Kubernetes operators with multi-node cluster support, local Kubernetes via minikube
Serverless
Weaviate Cloud Serverless with pay-per-query pricing model

What Vector Db Distance Metrics Does Weaviate Support?

CosineDot ProductSquared Euclidean (L2-Squared)Manhattan (L1)Hamming

What Vector Db Integrations Does Weaviate Offer?

LangChain

(98.) Provides native integration with Large Language Model (LLM) application development.

LlamaIndex

(99.) Offers supported vector store for data management and orchestration.

Python SDK

(100.) Provides a fully functional python client library.

REST API

(101.) Offers language-agnostic HTTP endpoints.

GraphQL API

(102.) Allows for flexible querying of the system via API interfaces to build complex searches.

TypeScript/JavaScript SDK

(103.) Provides client libraries for Node.js and browser environments.

Go SDK

(104.) Provides native support for Go applications.

Weaviate Embeddings

(105.) Offers managed embedding inference service through Weaviate Cloud.

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