AI visibility report
Weaviate ranks #6 in Search & Vector Databases AI search.
Outside the top three on 15 of the 25 prompts buyers actually ask.
Meilisearch is cited on 6 of those losses.
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Track Weaviate across these prompts daily.
Start free trial#6 among 11 vendors · still absent from 91.3% of tracked prompt responses
Top-3 citations across 150 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
Grok
Google AI Mode
ChatGPT
Gemini Search
Perplexity
Bing CopilotVisible, but narrative can improve. Weaviate ranks #6 on presence but #9 on sentiment. The brand appears relatively often, but competitors may be getting more favorable language when they appear.
Where Weaviate is losing
Prompts where competitors are visible and Weaviate is not.
These prompt-level losses are the first prompts to track and repair.
Where Weaviate is winning4
Which search engines handle synonyms, typo tolerance, and stop words across multiple languages without duplicating index configuration?
Avg # 1.0 · 1 platform
Which vector databases use the best ANN algorithms for recall at scale — how do the implementations differ across the major platforms?
Avg # 1.0 · 1 platform
Which search platforms support multimodal search combining text queries with image embeddings — what are the best options for this use case?
Avg # 2.0 · 1 platform
Which vector databases make it easiest to swap out the embedding model later without rebuilding the entire index — what should I evaluate for model portability?
Avg # 12.0 · 1 platform
Where Weaviate is losing5
Which hosted search platforms have the easiest relevance ranking tuning for a product catalog use case — what's the learning curve like?
Competitors on 3 platforms
Track this promptWhich search platforms best support geo-search and faceted filtering combined with full-text relevance for a marketplace application?
Competitors on 2 platforms
Track this promptWhich search platforms make it easiest to migrate from SQL LIKE-query search without taking the app offline during the transition?
Competitors on 2 platforms
Track this promptWhich search platform SDKs handle index schema migrations best when adding new fields without a full index rebuild?
Competitors on 2 platforms
Track this promptWhat are the best managed search services versus self-hosted options in terms of operational overhead and reliability at scale?
Competitors on 2 platforms
Track this prompt
Track Weaviate daily before the next report refresh.
Track these gapsResearch dossierCapabilities, use cases, sources, reviews, pricing, and FAQ
Overview
Weaviate is an open-source, AI-native vector database founded in 2019 and headquartered in Amsterdam, Netherlands. Built in Go, it stores both data objects and their vector embeddings, enabling semantic search, hybrid keyword-plus-vector search, retrieval-augmented generation (RAG), and agentic AI workflows in a single platform. Weaviate supports multiple deployment models—self-hosted via Docker or Kubernetes, Shared Cloud, Dedicated Cloud, and Bring Your Own Cloud on AWS, GCP, and Azure—making it suitable for use cases ranging from developer prototypes to billion-scale enterprise production systems. With over 13 million open-source downloads and 15,700+ GitHub stars, it has one of the largest communities in the vector database market. The company is backed by Index Ventures, Battery Ventures, and NEA, having raised approximately $67.7M in confirmed funding.
Weaviate is an open-source, cloud-native vector database designed for AI engineers building production-grade search, RAG, and agentic AI applications. It combines dense vector search, BM25 keyword search, and hybrid fusion in one system, with built-in support for multimodal data types, pluggable vectorizer modules, multi-tenancy, horizontal scaling, and enterprise compliance. A managed Weaviate Cloud service eliminates operational overhead, while the open-source version enables full self-hosted control. In 2024–2025, Weaviate expanded beyond a pure vector database into an AI application platform with the introduction of Weaviate Agents (Query, Transformation, and Personalization agents) and a native embedding service.
Key Facts
- Founded
- 2019
- HQ
- Amsterdam, Netherlands
- Founders
- Bob van Luijt, Etienne Dilocker, Micha Verhagen
- Employees
- 81-99
- Funding
- $67.7M
- Status
- Private
Target users
Key Capabilities10
- Hybrid search combining dense vector (HNSW) and keyword (BM25/BM25F) with configurable fusion strategies
- Multi-tenancy with full data isolation and hot/warm/cold storage tiering
- Built-in generative search and RAG without additional tooling
- Multimodal support for text, image, audio, and video in a single query interface
- Modular vectorizer architecture supporting OpenAI, Cohere, HuggingFace, Google, and custom models
- Vector compression (quantization) for cost-efficient billion-scale deployments
- Horizontal scaling and replication with RAFT-based consensus
- Enterprise security: RBAC, SOC 2, HIPAA, SSO/SAML, PrivateLink, customer-managed encryption keys
- Weaviate Agents (Query Agent, Transformation Agent, Personalization Agent) for agentic AI workflows
- Flexible deployment: open-source self-hosted, Shared Cloud, Dedicated Cloud, and BYOC on AWS/GCP/Azure
Key Use Cases7
- Retrieval-augmented generation (RAG) for enterprise knowledge bases and chatbots
- Semantic and hybrid search across unstructured data at scale
- Agentic AI workflows with memory and tool-use capabilities
- Multimodal search across images, audio, video, and text
- AI-powered recommendation systems
- Content classification and similarity-based data clustering
- Developer prototyping to production AI application pipelines
Weaviate customer outcomes
3x increase in user engagement; 60% reduction in trainer analysis time
MetaBuddy integrated Weaviate's vector database and Query Agent to unify fragmented health and fitness data, enabling natural-language AI coaching and proactive threshold-based insights. Trainers shifted from manual data analysis to high-value coaching interactions.
Recent Trend
How AI describes Weaviate3
...on OpenSearch Service (with Bedrock), Azure AI Search , and specialized vector databases like Milvus and Weaviate . [https://cloud.google.com/blog/products/ai-machine-learning/combine-text-image-power-with-vertex-ai](https://cloud.g...
Which search platforms offer the best developer experience for combining keyword search with semantic vector search in a single query?
Weaviate : An AI-native database that uses an asynchronous indexing process.
What are the best vector databases for a RAG application when you're just starting out with embeddings — which ones have the simplest setup path?
Based on 2026 industry analysis, Milvus , Qdrant , Weaviate , and Pgvector are top choices for monitoring and analyzing query performance.
What tools help keep a search index in sync with a primary relational database without building a custom ETL pipeline — what do teams typically use?
Most cited sources8
7Hybrid Search Explained | Weaviate
weaviate.io·Blog Post
- 5
Hybrid search | Weaviate
weaviate.io·Documentation
- 5
Rethinking Vector Search at Scale: Weaviate's Native, Efficient and Optimized Multi-Tenancy | Weaviate
weaviate.io·Blog Post
- D2
Monitoring | Weaviate Documentation
docs.weaviate.io·Documentation
- 1
Vector Search Explained | Weaviate
weaviate.io·Blog Post
- D1
Multimodal Embeddings | Weaviate Documentation
docs.weaviate.io·Documentation
Alternatives in Search & Vector Databases6
Weaviate positions itself as the leading open-source, AI-native vector database for production AI applications, differentiating on three axes: (1) open-source flexibility with an optional fully managed cloud (unlike Pinecone's proprietary managed-only model), (2) mature hybrid search combining dense vector and BM25 keyword search with configurable fusion in a single query (versus Chroma's simpler embedded focus or Qdrant's pure-performance orientation), and (3) a full-stack AI application platform with built-in RAG, multi-modality, Weaviate Agents, and a broad ecosystem of LLM/framework integrations.
- The European founding origin and GDPR-native design are cited as differentiators for EU enterprise buyers.
- Its HNSW-based Go architecture trades some raw query throughput versus Qdrant for a richer feature surface area and enterprise compliance posture (SOC 2, HIPAA).
Reviews
Praised
- Easy onboarding and developer-friendly setup
- Powerful hybrid search (vector + keyword) capabilities
- Active Slack community and responsive team
- Strong documentation and learning resources
- Multi-tenancy and API-first design
- Broad LLM and framework integrations
- Flexible deployment (self-hosted or managed)
Criticized
- High memory and compute resource consumption
- Complex, hard-to-estimate pricing model
- Not a general-purpose operational database; requires a second data store
- Higher query latency vs. Qdrant in benchmarks
- Steeper learning curve vs. pure managed services like Pinecone
- Cloud console feature gaps relative to API/CLI
- Enterprise support responsiveness inconsistencies
Weaviate receives broadly positive developer feedback, particularly praised for its hybrid search capabilities, ease of onboarding, active Slack community, and the breadth of its LLM and framework integrations. Gartner Peer Insights reviewers highlight its AI-native design and suitability for RAG and GenAI workloads. Critical feedback centers on resource intensity, the complexity of cost estimation, and the fact that it cannot replace a general-purpose operational database—requiring teams to manage an additional data store. Some users report less responsive enterprise support under certain plans. Third-party performance benchmarks generally show Weaviate trailing Qdrant on raw throughput.
Pricing
Open-source self-hosting is free (BSD-3-Clause license). Weaviate Cloud offers: (1) Free Trial — 14-day sandbox with full core database features; (2) Flex — starts at $45/month, pay-as-you-go on shared cloud, with vector dimensions priced from $0.01668/1M and storage from $0.255/GiB; (3) Premium — starts at $400/month on prepaid contract, with access to dedicated cloud, 99.95% SLA, SSO/SAML, HIPAA, PrivateLink, and as-low-as 1-hour Severity 1 support. BYOC and Enterprise Cloud are contact-sales. Add-ons include Weaviate Embeddings (from $0.025/1M tokens) and Query Agent ($30/month for 4,000 requests). Available on AWS, GCP, and Azure Marketplaces.
Limitations
- Third-party benchmarks (e.g., Qdrant's own benchmarks) show Weaviate with higher query latency and lower throughput than Qdrant in head-to-head vector search scenarios.
- Gartner reviewers note that Weaviate is not a general-purpose operational data store, requiring teams to operate an additional database for non-vector workloads.
- Self-hosting demands Kubernetes expertise and ongoing maintenance.
- The managed cloud pricing model based on vector dimensions is frequently cited as complex and difficult to estimate upfront.
- Memory usage is notably higher under uncompressed vector configurations.
- Cloud console feature set has been noted as limited relative to the CLI/API experience.
Frequently asked questions
Topic coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
| Prompt | Gemini Search | Bing Copilot | Perplexity | ChatGPT | Google AI Mode | Grok |
|---|---|---|---|---|---|---|
Capability3/5 cited (60%) | ||||||
Which search platforms best support geo-search and faceted filtering combined with full-text relevance for a marketplace application? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which vector databases handle filtered similarity search efficiently — which ones support nearest neighbor search scoped to a specific user's namespace? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited |
What are the tradeoffs between dense vector search and sparse keyword search, and which platforms offer the best hybrid search implementations? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | A competitor was cited | Your brand and a competitor were cited |
Which search platforms support multimodal search combining text queries with image embeddings — what are the best options for this use case? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which hosted vector databases scale best to billions of high-dimensional embeddings — what are the real limitations teams hit at that scale? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Developer Experience2/5 cited (40%) | ||||||
Which search engines handle synonyms, typo tolerance, and stop words across multiple languages without duplicating index configuration? | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | A competitor was cited |
Which search engines have the best dashboard and query explorer tools for non-engineers to understand why certain results rank higher? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which search platforms offer the best developer experience for combining keyword search with semantic vector search in a single query? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited |
Which search platform SDKs handle index schema migrations best when adding new fields without a full index rebuild? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
Which hosted search platforms have the easiest relevance ranking tuning for a product catalog use case — what's the learning curve like? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
Integrations & Ecosystem2/5 cited (40%) | ||||||
Which search platforms have native integrations with popular LLM orchestration frameworks for building RAG pipelines with minimal boilerplate? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
Which search platforms work best as the retrieval layer for an AI agent that needs to query across multiple data sources and indexes? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which vector databases make it easiest to swap out the embedding model later without rebuilding the entire index — what should I evaluate for model portability? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand was cited |
Which vector databases integrate best with standard observability stacks — which ones make it easy to monitor and analyze query performance? | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited |
What tools help keep a search index in sync with a primary relational database without building a custom ETL pipeline — what do teams typically use? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Performance & Reliability2/5 cited (40%) | ||||||
Which search platforms scale horizontally best when index size grows past what fits on a single node — what are the options? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which vector databases use the best ANN algorithms for recall at scale — how do the implementations differ across the major platforms? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand was cited | A competitor was cited |
What are the best managed search services versus self-hosted options in terms of operational overhead and reliability at scale? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
Which vector databases handle real-time index updates without degrading query performance during high write loads? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
Which hosted vector search services offer the best p99 query latency when searching 50 million vectors — what should I realistically expect? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited |
Setup & First Run1/5 cited (20%) | ||||||
Which search platforms make it easiest to migrate from SQL LIKE-query search without taking the app offline during the transition? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
What are the best vector databases for a RAG application when you're just starting out with embeddings — which ones have the simplest setup path? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
What's the fastest way to add full-text search to a Next.js app without setting up a dedicated search cluster — which services are worth looking at? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which hosted search platforms deliver good out-of-the-box relevance with minimal tuning before results feel useful to end users? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
What are the best search engines for indexing an existing relational database without needing a full data pipeline from day one? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Your brand was cited | A competitor was cited |
Turn this matrix into daily prompt monitoring.
Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Meilisearch | 18.7% | 23.6% | 10.7% | 12.7% | 33.3% | #25.8 | +0.26 |
| 2 | Elastic | 13.3% | 11.1% | 4.0% | 1.3% | 28.7% | #20.9 | +0.28 |
| 3 | Algolia | 11.3% | 14.8% | 6.0% | 7.3% | 34.0% | #30.4 | +0.37 |
| 4 | Typesense | 11.3% | 14.8% | 7.3% | 0.0% | 28.7% | #33.4 | +0.33 |
| 5 | Qdrant | 9.3% | 10.5% | 4.0% | 2.0% | 47.3% | #43.5 | +0.23 |
| 6 | Weaviate | 8.7% | 6.8% | 0.7% | 5.3% | 46.0% | #33.2 | +0.22 |
| 7 | Pinecone | 8.0% | 7.1% | 0.7% | 3.3% | 49.3% | #45.8 | +0.31 |
| 8 | Zilliz | 6.7% | 6.0% | 0.7% | 4.0% | 16.0% | #35.1 | +0.26 |
| 9 | Vespa | 4.0% | 4.6% | 2.0% | 2.0% | 2.7% | #38.3 | -0.02 |
| 10 | Chroma | 1.3% | 0.6% | 0.7% | 0.0% | 12.7% | #45.0 | +0.25 |
| 11 | Trieve | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
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