# Qdrant AI visibility in Search & Vector Databases

Canonical: https://devtune.ai/verticals/search-vector-databases/qdrant

[Website](https://qdrant.tech/)

Updated: 2026-10-16T17:49:38.014886+00:00
Prompts: 25
Runs: 6


## Platforms

- bing-copilot-search
- google-ai-mode
- perplexity
- chatgpt-search
- google-ai
- xai-search

Rank: 3
Total brands: 11
Measured responses: 150
Presence percent: 21.333333333333336
Share of voice percent: 14.864864864864865
Average position: 21.420454545454547
Docs presence percent: 17.333333333333336
Blog presence percent: 2
Brand mention percent: 48.66666666666667


## Profile

Overview: Qdrant (pronounced 'quadrant') is an open-source vector database and similarity search engine designed for production-grade AI applications. Founded in 2021 and headquartered in Berlin, Germany, it is built entirely in Rust for high throughput, low latency, and memory safety. Qdrant supports dense and sparse vector storage with advanced JSON payload filtering, native hybrid search, multi-vector representations, and multiple quantization methods that reduce memory usage by up to 64x. It is available as a self-hosted open-source binary (Apache 2.0), a fully managed cloud service on AWS, GCP, and Azure, a hybrid cloud offering on customer-owned Kubernetes, and an edge deployment option. With over 28,000 GitHub stars, 60,000+ community members, and $87.8M in total funding, Qdrant serves use cases spanning RAG, semantic search, recommendation systems, and AI agent memory.
Product summary: Qdrant is a high-performance, open-source vector database and search engine written in Rust, offering native hybrid (dense + sparse) retrieval, one-stage filtered HNSW indexing, multi-vector support, and advanced quantization. It serves as the retrieval backbone for RAG pipelines, AI agents, semantic search, and recommendation systems, and is deployable as open-source, fully managed cloud, hybrid cloud, private cloud, or edge.


### Key capabilities

- High-performance HNSW vector search built entirely in Rust with SIMD acceleration
- Native hybrid search combining dense and sparse vectors (BM25, SPLADE++, miniCOIL) in a single query
- One-stage filtered HNSW: filters applied during index traversal, not pre/post, preserving recall
- Advanced quantization (scalar, binary, asymmetric) reducing RAM usage by up to 64x
- Multi-vector per object support for multimodal and late-interaction retrieval (e.g., ColBERT)
- Real-time indexing without full index rebuilds
- Distributed deployment with automatic sharding, replication, and zero-downtime rolling upgrades
- Flexible deployment: OSS, managed cloud (AWS/GCP/Azure), hybrid cloud (BYO Kubernetes), private/air-gapped, and edge (beta)
- Full-spectrum reranking with score boosting, MMR, and late-interaction models
- Enterprise compliance: SOC 2, HIPAA, GDPR-aligned, RBAC, SSO (SAML/OIDC), private VPC links



### Target users

- AI/ML engineers building RAG and LLM-powered applications
- Backend and platform engineers deploying production vector search infrastructure
- Data scientists building recommendation and semantic search systems
- Enterprise development teams requiring SOC 2 / HIPAA compliant AI retrieval
- Startups and indie developers prototyping with open-source vector search
- DevOps and infrastructure teams managing self-hosted or hybrid AI data stacks



### Key use cases

- Retrieval-Augmented Generation (RAG) and GenAI knowledge bases
- AI agent persistent memory and context retrieval
- Semantic and hybrid search for enterprise applications
- Personalized recommendation systems
- Multimodal search across text, image, and video
- Anomaly detection and data analysis over high-dimensional embeddings
- E-commerce product discovery and visual search
- Legal and patent document intelligence retrieval

Integrations ecosystem: Qdrant integrates natively with the major LLM orchestration frameworks: LangChain (official langchain-qdrant package), LlamaIndex, Microsoft Semantic Kernel, Haystack, and DocArray. Embedding providers supported include OpenAI, Cohere, and HuggingFace. The cloud service is available on AWS, Google Cloud, and Azure, including AWS Marketplace. Qdrant Cloud Inference enables native embedding generation without a separate pipeline. Official client SDKs cover Python, JavaScript/TypeScript, Go, Rust, .NET/C#, and Java, with community clients in Elixir, PHP, and Ruby. In 2025, 35 new integrations were added including an official n8n node. Infrastructure automation is supported via Terraform, Pulumi, and CLI. Monitoring integrations include Prometheus, Grafana, and Datadog. Partners include Airbyte for data pipelines.
Pricing summary: Free Tier: permanently free with a single-node cluster (0.5 vCPU, 1 GB RAM, 4 GB disk) and free Cloud Inference for selected models. Standard Tier: usage-based pricing billed hourly per vCPU, memory (GB), disk (GB), and inference tokens consumed; dedicated clusters with 99.5% SLA on AWS, GCP, or Azure. Premium Tier: minimum spend required; adds SSO, private VPC links, 99.9% SLA, and 24×7 support. Hybrid Cloud and Private Cloud: custom pricing negotiated with the sales team. OSS self-hosting is free. The open-source core is licensed Apache 2.0.
Review summary: Developer reviewers consistently highlight Qdrant's speed, ease of self-hosting via Docker, and thorough documentation as standout strengths. Users evaluating it against Pinecone and other alternatives frequently choose Qdrant for higher embedding search accuracy and more straightforward setup. The primary complaint across multiple reviews is the absence of built-in visualization tooling, requiring users to write code for bulk collection management tasks that other databases expose through a GUI. Enterprise adopters note strong performance stability under concurrent production loads.
Competitive positioning: Qdrant positions itself as the highest-performance, production-grade, open-source vector database purpose-built for production AI workloads. Its primary differentiators are its Rust-based engine (no wrappers or bolt-ons), one-stage HNSW filtering with payload indexes, composable/modular search configuration, and the widest deployment flexibility in the market (OSS, managed cloud on AWS/GCP/Azure, hybrid cloud on customer Kubernetes, private cloud, and edge). Against cloud-only rivals like Pinecone it stresses no vendor lock-in and self-hosting. Against broader platforms like Elastic it emphasizes purpose-built AI retrieval performance. Against Weaviate and Chroma it cites benchmark leadership at scale and advanced quantization. Its open-source Apache 2.0 license and enterprise-grade compliance (SOC 2, HIPAA, GDPR) let it compete for both developer-led adoption and top-down enterprise deals.
Limitations: Reviewers consistently note the absence of built-in data visualization tools, making it difficult to explore search results without writing code. The web UI has limited support for bulk or pattern-based collection management operations without custom queries. Self-hosted deployments require Kubernetes expertise and ongoing operational overhead. At very high recall requirements, tail latency can increase depending on cluster configuration. Pricing transparency for Standard and Premium tiers requires use of a calculator rather than published list prices.


### Source urls

- https://qdrant.tech/
- https://qdrant.tech/about-us/
- https://qdrant.tech/pricing/
- https://qdrant.tech/customers/
- https://github.com/qdrant/qdrant
- https://techcrunch.com/2024/01/23/qdrant-open-source-vector-database/
- https://siliconangle.com/2026/03/12/qdrant-raises-50m-bring-flexible-vector-search-production-ai-systems/
- https://www.techtarget.com/searchdatamanagement/news/366640132/Qdrant-raises-50M-in-funding-to-fuel-vector-database-growth
- https://qdrant.tech/blog/case-study-tripadvisor/
- https://qdrant.tech/blog/case-study-lyzr/
- https://qdrant.tech/blog/2025-recap/
- https://aws.amazon.com/marketplace/reviews/reviews-list/prodview-rtphb42tydtzg
- https://tracxn.com/d/companies/qdrant/__8lnYFqbFwJLMs8h9W87yDPITqoMXrEUlt5zZ1Rcsb-4

Reviewed at: 2026-04-28T23:13:44.524+00:00


### Customer outcomes

| Customer | Summary | Metric |
| --- | --- | --- |
| Tripadvisor | Qdrant powers Tripadvisor's AI Trip Planner, enabling multimodal vector search across over one billion user reviews and images. Users engaging with the generative AI experience show materially higher monetization than those on traditional interfaces. | 2–3x revenue uplift from GenAI-engaged users |
| Lyzr | After migrating from Weaviate to Qdrant, Lyzr reduced query latency from 300–500 ms to 20–50 ms (P99), improved indexing speed, and cut infrastructure costs across their AI agent platform supporting 100+ concurrent agents. | >90% query latency reduction; 2x faster indexing; ~30% infrastructure cost reduction |
| Deutsche Telekom | Deutsche Telekom built a multi-agent enterprise PaaS on Qdrant powering AI-driven conversations across 10 countries, dramatically reducing the time required to develop new agents. | Agent development time cut from 15 days to 2 days; 2M+ AI-driven conversations served |
| Dust | Dust uses Qdrant as the vector search backbone for its AI agents platform, scaling retrieval across a large and diverse set of enterprise data sources. | 5,000+ data sources indexed and searched |



### Reviews breakdown





### Review themes



#### Praised

- High query speed and low latency at scale
- Easy self-hosting with Docker
- Comprehensive and up-to-date documentation
- Open-source with no vendor lock-in
- Multi-cloud managed service (AWS, GCP, Azure)
- Accurate embedding search vs. competitors
- Real-time indexing without downtime
- Strong developer community and active roadmap



#### Criticized

- No built-in data visualization tools
- Limited rich UI operations without writing code/queries
- Slower search times reported in some configurations
- Self-hosted Kubernetes operations require expertise
- Standard and Premium tier pricing requires a calculator, not published list prices




### Company facts

Founded year: 2021
Hq: Berlin, Germany


#### Founders

- André Zayarni
- Andrey Vasnetsov

Employees range: 100-200
Total funding: $87.8M
Valuation: Not available
Arr: Not available
Customer count: Not available
Status: Private


Readiness: Not available


## Ranking

| Display name | Pair count | Total pairs | Presence percent | Avg position |
| --- | --- | --- | --- | --- |
| Elastic | 47 | 150 | 31.333333333333336 | 11.63265306122449 |
| Typesense | 36 | 150 | 24 | 20.880434782608695 |
| Qdrant | 32 | 150 | 21.333333333333336 | 21.420454545454547 |
| Meilisearch | 32 | 150 | 21.333333333333336 | 24.627906976744185 |
| Weaviate | 24 | 150 | 16 | 16.523076923076925 |
| Pinecone | 23 | 150 | 15.333333333333332 | 25.72 |
| Algolia | 23 | 150 | 15.333333333333332 | 26.704918032786885 |
| Vespa | 10 | 150 | 6.666666666666667 | 24 |
| Zilliz | 7 | 150 | 4.666666666666667 | 42.11764705882353 |
| Chroma | 4 | 150 | 2.666666666666667 | 16.833333333333332 |
| Trieve | 0 | 150 | 0 | Not available |



## Platform breakdown

| Platform | Prompt count | Presence rate |
| --- | --- | --- |
| bing-copilot-search | 0 | 0 |
| google-ai-mode | 5 | 20 |
| perplexity | 7 | 28.000000000000004 |
| chatgpt-search | 11 | 44 |
| google-ai | 1 | 4 |
| xai-search | 8 | 32 |



## Strengths

| Prompt text | Platform count | Avg position |
| --- | --- | --- |
| Which search platforms support multimodal search combining text queries with image embeddings — what are the best options for this use case? | 2 | 1.5 |
| 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? | 3 | 1.6666666666666667 |
| Which vector databases integrate best with standard observability stacks — which ones make it easy to monitor and analyze query performance? | 5 | 3 |
| Which hosted vector search services offer the best p99 query latency when searching 50 million vectors — what should I realistically expect? | 3 | 4 |



## Gaps

| Prompt text | Competitor presence count |
| --- | --- |
| Which search platform SDKs handle index schema migrations best when adding new fields without a full index rebuild? | 5 |
| Which search engines handle synonyms, typo tolerance, and stop words across multiple languages without duplicating index configuration? | 5 |
| Which search platforms best support geo-search and faceted filtering combined with full-text relevance for a marketplace application? | 4 |
| What are the best search engines for indexing an existing relational database without needing a full data pipeline from day one? | 4 |
| Which search platforms work best as the retrieval layer for an AI agent that needs to query across multiple data sources and indexes? | 4 |



## Topic scores

| Topic name | Prompt count | Cited prompt count |
| --- | --- | --- |
| Capability | 5 | 4 |
| Developer Experience | 5 | 1 |
| Integrations & Ecosystem | 5 | 3 |
| Performance & Reliability | 5 | 3 |
| Setup & First Run | 5 | 1 |



## Prompt results

- Prompt text: Which hosted vector databases scale best to billions of high-dimensional embeddings — what are the real limitations teams hit at that scale?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Perplexity: 6
Chatgpt-search: Not available
Google-ai: Not available
Xai-search: 65



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode





##### Perplexity

| Display name | Position |
| --- | --- |
| Pinecone | 1 |
| Zilliz | 4 |
| Qdrant | 6 |
| Weaviate | 7 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Pinecone | 1 |
| Weaviate | 8 |



##### Google-ai





##### Xai-search

| Display name | Position |
| --- | --- |
| Meilisearch | 33 |
| Pinecone | 41 |
| Zilliz | 55 |
| Qdrant | 65 |


- Prompt text: 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?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Perplexity: Not available
Chatgpt-search: 4
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode





##### Perplexity

| Display name | Position |
| --- | --- |
| Chroma | 1 |
| Pinecone | 2 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Chroma | 1 |
| Pinecone | 3 |
| Qdrant | 4 |



##### Google-ai





##### Xai-search




- Prompt text: 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?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Perplexity: Not available
Chatgpt-search: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Elastic | 6 |



##### Perplexity





##### Chatgpt-search





##### Google-ai





##### Xai-search

| Display name | Position |
| --- | --- |
| Elastic | 15 |


- Prompt text: 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?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Perplexity: Not available
Chatgpt-search: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Typesense | 3 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Algolia | 1 |
| Meilisearch | 4 |
| Typesense | 5 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Typesense | 6 |



##### Google-ai





##### Xai-search

| Display name | Position |
| --- | --- |
| Typesense | 15 |
| Meilisearch | 34 |


- Prompt text: Which search platform SDKs handle index schema migrations best when adding new fields without a full index rebuild?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Perplexity: Not available
Chatgpt-search: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Typesense | 2 |
| Elastic | 4 |
| Algolia | 7 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Elastic | 1 |
| Typesense | 3 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Elastic | 1 |
| Typesense | 3 |
| Meilisearch | 7 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Elastic | 1 |
| Algolia | 6 |



##### Xai-search

| Display name | Position |
| --- | --- |
| Elastic | 1 |
| Typesense | 12 |
| Meilisearch | 13 |
| Pinecone | 57 |
| Algolia | 77 |


- Prompt text: Which search platforms best support geo-search and faceted filtering combined with full-text relevance for a marketplace application?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Perplexity: Not available
Chatgpt-search: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Typesense | 3 |
| Meilisearch | 5 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Algolia | 1 |
| Typesense | 2 |
| Elastic | 4 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Algolia | 1 |
| Elastic | 3 |
| Typesense | 8 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Typesense | 7 |



##### Xai-search

| Display name | Position |
| --- | --- |
| Meilisearch | 1 |
| Algolia | 3 |
| Elastic | 25 |
| Typesense | 28 |


- Prompt text: Which hosted vector search services offer the best p99 query latency when searching 50 million vectors — what should I realistically expect?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Perplexity: 2
Chatgpt-search: 1
Google-ai: Not available
Xai-search: 9



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode





##### Perplexity

| Display name | Position |
| --- | --- |
| Qdrant | 2 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Qdrant | 1 |
| Pinecone | 3 |
| Weaviate | 4 |



##### Google-ai





##### Xai-search

| Display name | Position |
| --- | --- |
| Zilliz | 5 |
| Qdrant | 9 |
| Elastic | 26 |
| Pinecone | 37 |
| Weaviate | 67 |


- Prompt text: Which search platforms have native integrations with popular LLM orchestration frameworks for building RAG pipelines with minimal boilerplate?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Perplexity: Not available
Chatgpt-search: 12
Google-ai: Not available
Xai-search: 10



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Elastic | 1 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Elastic | 5 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Pinecone | 10 |
| Qdrant | 12 |
| Weaviate | 14 |
| Elastic | 16 |



##### Google-ai





##### Xai-search

| Display name | Position |
| --- | --- |
| Qdrant | 10 |
| Zilliz | 34 |


- Prompt text: Which search engines have the best dashboard and query explorer tools for non-engineers to understand why certain results rank higher?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Perplexity: Not available
Chatgpt-search: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode





##### Perplexity





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Algolia | 5 |
| Elastic | 10 |



##### Google-ai





##### Xai-search

| Display name | Position |
| --- | --- |
| Meilisearch | 40 |


- Prompt text: Which vector databases handle real-time index updates without degrading query performance during high write loads?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: 3
Perplexity: 2
Chatgpt-search: 1
Google-ai: Not available
Xai-search: 67



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Qdrant | 3 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Qdrant | 2 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Qdrant | 1 |
| Weaviate | 7 |



##### Google-ai





##### Xai-search

| Display name | Position |
| --- | --- |
| Zilliz | 53 |
| Qdrant | 67 |
| Pinecone | 77 |


- Prompt text: Which vector databases integrate best with standard observability stacks — which ones make it easy to monitor and analyze query performance?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: 1
Perplexity: 3
Chatgpt-search: 1
Google-ai: 1
Xai-search: 9



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Qdrant | 1 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Qdrant | 3 |
| Weaviate | 5 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Qdrant | 1 |
| Weaviate | 4 |
| Pinecone | 9 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Qdrant | 1 |
| Weaviate | 6 |



##### Xai-search

| Display name | Position |
| --- | --- |
| Qdrant | 9 |
| Zilliz | 34 |
| Weaviate | 54 |
| Chroma | 75 |
| Pinecone | 86 |


- Prompt text: Which search platforms make it easiest to migrate from SQL LIKE-query search without taking the app offline during the transition?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Perplexity: Not available
Chatgpt-search: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode





##### Perplexity

| Display name | Position |
| --- | --- |
| Meilisearch | 1 |
| Typesense | 2 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Typesense | 1 |
| Meilisearch | 3 |
| Elastic | 5 |



##### Google-ai





##### Xai-search

| Display name | Position |
| --- | --- |
| Elastic | 2 |
| Typesense | 35 |
| Meilisearch | 36 |
| Algolia | 63 |


- Prompt text: What are the best managed search services versus self-hosted options in terms of operational overhead and reliability at scale?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Perplexity: Not available
Chatgpt-search: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode





##### Perplexity

| Display name | Position |
| --- | --- |
| Algolia | 1 |
| Elastic | 5 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Elastic | 1 |
| Typesense | 7 |



##### Google-ai





##### Xai-search

| Display name | Position |
| --- | --- |
| Algolia | 1 |
| Elastic | 4 |
| Meilisearch | 8 |
| Typesense | 20 |


- Prompt text: What are the best search engines for indexing an existing relational database without needing a full data pipeline from day one?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Perplexity: Not available
Chatgpt-search: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Typesense | 2 |
| Meilisearch | 5 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Elastic | 1 |
| Typesense | 3 |
| Meilisearch | 4 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Elastic | 1 |



##### Google-ai





##### Xai-search

| Display name | Position |
| --- | --- |
| Elastic | 1 |
| Meilisearch | 12 |
| Algolia | 15 |
| Typesense | 39 |


- Prompt text: Which search platforms work best as the retrieval layer for an AI agent that needs to query across multiple data sources and indexes?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Perplexity: Not available
Chatgpt-search: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode





##### Perplexity

| Display name | Position |
| --- | --- |
| Elastic | 1 |
| Vespa | 5 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Elastic | 3 |
| Vespa | 5 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Elastic | 1 |



##### Xai-search

| Display name | Position |
| --- | --- |
| Meilisearch | 2 |
| Algolia | 32 |
| Zilliz | 41 |
| Vespa | 44 |


- Prompt text: What are the tradeoffs between dense vector search and sparse keyword search, and which platforms offer the best hybrid search implementations?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Perplexity: Not available
Chatgpt-search: 6
Google-ai: Not available
Xai-search: 30



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode





##### Perplexity

| Display name | Position |
| --- | --- |
| Pinecone | 1 |
| Weaviate | 2 |
| Elastic | 7 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Elastic | 1 |
| Weaviate | 4 |
| Qdrant | 6 |



##### Google-ai





##### Xai-search

| Display name | Position |
| --- | --- |
| Weaviate | 1 |
| Pinecone | 9 |
| Chroma | 15 |
| Qdrant | 30 |
| Elastic | 31 |


- Prompt text: Which search engines handle synonyms, typo tolerance, and stop words across multiple languages without duplicating index configuration?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Perplexity: Not available
Chatgpt-search: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Algolia | 4 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Typesense | 1 |
| Algolia | 5 |
| Elastic | 6 |
| Meilisearch | 9 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Algolia | 1 |
| Meilisearch | 4 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Meilisearch | 1 |
| Typesense | 3 |
| Algolia | 4 |
| Elastic | 7 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Meilisearch | 1 |



##### Xai-search

| Display name | Position |
| --- | --- |
| Typesense | 1 |
| Meilisearch | 2 |
| Algolia | 11 |
| Elastic | 18 |


- Prompt text: Which hosted search platforms have the easiest relevance ranking tuning for a product catalog use case — what's the learning curve like?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Perplexity: Not available
Chatgpt-search: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Algolia | 1 |
| Typesense | 3 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Typesense | 5 |
| Elastic | 6 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Algolia | 1 |
| Typesense | 5 |
| Elastic | 10 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Meilisearch | 1 |



##### Xai-search

| Display name | Position |
| --- | --- |
| Meilisearch | 1 |
| Algolia | 2 |
| Typesense | 3 |
| Elastic | 25 |


- Prompt text: 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?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: 3
Perplexity: 1
Chatgpt-search: 1
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Qdrant | 3 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Qdrant | 1 |
| Pinecone | 3 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Qdrant | 1 |
| Weaviate | 3 |
| Pinecone | 6 |



##### Google-ai





##### Xai-search

| Display name | Position |
| --- | --- |
| Weaviate | 12 |


- Prompt text: Which search platforms scale horizontally best when index size grows past what fits on a single node — what are the options?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Perplexity: Not available
Chatgpt-search: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode





##### Perplexity

| Display name | Position |
| --- | --- |
| Elastic | 1 |
| Vespa | 4 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Vespa | 1 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Elastic | 4 |



##### Xai-search

| Display name | Position |
| --- | --- |
| Meilisearch | 9 |
| Vespa | 17 |
| Elastic | 23 |
| Typesense | 37 |


- Prompt text: Which search platforms support multimodal search combining text queries with image embeddings — what are the best options for this use case?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: 1
Perplexity: Not available
Chatgpt-search: 2
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Qdrant | 1 |
| Elastic | 4 |
| Pinecone | 6 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Elastic | 1 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Elastic | 1 |
| Qdrant | 2 |
| Weaviate | 3 |
| Pinecone | 15 |



##### Google-ai





##### Xai-search

| Display name | Position |
| --- | --- |
| Elastic | 1 |
| Meilisearch | 18 |
| Pinecone | 30 |


- Prompt text: Which vector databases handle filtered similarity search efficiently — which ones support nearest neighbor search scoped to a specific user's namespace?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Perplexity: 3
Chatgpt-search: 1
Google-ai: Not available
Xai-search: 25



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode





##### Perplexity

| Display name | Position |
| --- | --- |
| Qdrant | 3 |
| Weaviate | 5 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Qdrant | 1 |
| Weaviate | 2 |



##### Google-ai





##### Xai-search

| Display name | Position |
| --- | --- |
| Pinecone | 11 |
| Weaviate | 18 |
| Qdrant | 25 |


- Prompt text: Which vector databases use the best ANN algorithms for recall at scale — how do the implementations differ across the major platforms?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Perplexity: 1
Chatgpt-search: 1
Google-ai: Not available
Xai-search: 52



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode





##### Perplexity

| Display name | Position |
| --- | --- |
| Qdrant | 1 |
| Weaviate | 3 |
| Elastic | 6 |
| Vespa | 9 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Qdrant | 1 |
| Vespa | 2 |
| Weaviate | 4 |
| Pinecone | 10 |
| Elastic | 11 |



##### Google-ai





##### Xai-search

| Display name | Position |
| --- | --- |
| Pinecone | 2 |
| Vespa | 35 |
| Meilisearch | 49 |
| Qdrant | 52 |


- Prompt text: Which hosted search platforms deliver good out-of-the-box relevance with minimal tuning before results feel useful to end users?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Perplexity: Not available
Chatgpt-search: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Typesense | 1 |
| Meilisearch | 3 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Typesense | 1 |
| Meilisearch | 3 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Meilisearch | 2 |
| Typesense | 3 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Algolia | 1 |
| Meilisearch | 2 |
| Typesense | 6 |



##### Xai-search

| Display name | Position |
| --- | --- |
| Typesense | 11 |
| Meilisearch | 12 |
| Algolia | 20 |
| Elastic | 32 |


- Prompt text: Which search platforms offer the best developer experience for combining keyword search with semantic vector search in a single query?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: 5
Perplexity: Not available
Chatgpt-search: 6
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Typesense | 1 |
| Meilisearch | 3 |
| Qdrant | 5 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Weaviate | 1 |
| Pinecone | 4 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Weaviate | 1 |
| Elastic | 3 |
| Qdrant | 6 |
| Typesense | 8 |



##### Google-ai





##### Xai-search

| Display name | Position |
| --- | --- |
| Elastic | 1 |
| Weaviate | 8 |
| Vespa | 33 |





## Top sources

| Url | Title | Domain | Logo url | Source vertical | Content type | Citation count | Last30d count |
| --- | --- | --- | --- | --- | --- | --- | --- |
| https://qdrant.tech/documentation/ops-monitoring/monitoring/ | Monitoring & Telemetry - Qdrant | qdrant.tech | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/6b127064-92bf-4295-83ce-71eec7540861/b2db56e79a226655eb0c692e7a46043bcbd6ca80.png | commercial | product_page | 7 | 7 |
| https://qdrant.tech/observability/ | Qdrant Cloud Observability: Metrics, Monitoring, and Alerting | qdrant.tech | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/6b127064-92bf-4295-83ce-71eec7540861/b2db56e79a226655eb0c692e7a46043bcbd6ca80.png | commercial | documentation | 6 | 6 |
| https://qdrant.tech/documentation/manage-data/multitenancy/ | Multitenancy | qdrant.tech | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/6b127064-92bf-4295-83ce-71eec7540861/b2db56e79a226655eb0c692e7a46043bcbd6ca80.png | commercial | documentation | 5 | 5 |
| https://qdrant.tech/documentation/tutorials-operations/embedding-model-migration/ | Migrate to a New Embedding Model - Qdrant | qdrant.tech | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/6b127064-92bf-4295-83ce-71eec7540861/b2db56e79a226655eb0c692e7a46043bcbd6ca80.png | commercial | article | 5 | 5 |
| https://qdrant.tech/documentation/search/hybrid-queries/ | Hybrid Queries - Qdrant | qdrant.tech | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/6b127064-92bf-4295-83ce-71eec7540861/b2db56e79a226655eb0c692e7a46043bcbd6ca80.png | commercial | documentation | 4 | 4 |
| https://qdrant.tech/documentation/search/text-search/hybrid-search/ | Hybrid Search | qdrant.tech | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/6b127064-92bf-4295-83ce-71eec7540861/b2db56e79a226655eb0c692e7a46043bcbd6ca80.png | commercial | documentation | 4 | 4 |
| https://qdrant.tech/documentation/manage-data/vectors/ | Vectors - Qdrant | qdrant.tech | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/6b127064-92bf-4295-83ce-71eec7540861/b2db56e79a226655eb0c692e7a46043bcbd6ca80.png | commercial | documentation | 4 | 4 |
| https://qdrant.tech/documentation/multimodal-search/ | Multimodal Search | qdrant.tech | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/6b127064-92bf-4295-83ce-71eec7540861/b2db56e79a226655eb0c692e7a46043bcbd6ca80.png | commercial | landing_page | 4 | 4 |



## Response excerpts

| Prompt text | Platform | Excerpt |
| --- | --- | --- |
| Which search platforms offer the best developer experience for combining keyword search with semantic vector search in a single query? | chatgpt-search | My shortlist would be Weaviate, Elasticsearch, Qdrant, Azure AI Search, and Pinecone. |
| Which hosted vector databases scale best to billions of high-dimensional embeddings — what are the real limitations teams hit at that scale? | chatgpt-search | ...loud \| Very good \| Rich retrieval/filtering + multi-tenancy \| HNSW memory pressure; compression becomes important \| \| Qdrant Cloud \| Good–very good \| Efficient ANN, filtering, operational simplicity \| Billion-scale deployments require careful s... |
| 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? | chatgpt-search | Setup: easy · Cost: cloud usage-based Official quickstart and docs ## 3. Qdrant Best for: a beginner-friendly prototype that can grow into a more capable retrieval system. |



## Competitor excerpts

| Platform | Competitor name | Excerpt |
| --- | --- | --- |
| google-ai-mode | Typesense | ...API](https://docs.opensearch.org/latest/api-reference/index-apis/put-mapping/)Collection schema migrations · Issue #96 · typesense ... - GitHub[How do I add a field to an Algolia index without rebuildi... |
| perplexity | Typesense | If the requirement is adding new fields to an existing index without rebuilding it, Elasticsearch and Typesense are the clearest fits—but they differ in how explicitly they support the change. |
| chatgpt-search | Elastic | Elastic \| \| OpenSearch \| Yes \| Excellent \| `PUT _mapping` explicitly supports evolving an existing index; changing an indexed field's type requires reindexing. |
| chatgpt-search | Typesense | If your priority is adding new indexed fields without rebuilding the entire index, the strongest options are Elasticsearch/OpenSearch and Typesense. |
| google-ai | Elastic | Elastic \+ 1 * Mapping API: You can use the Update Mapping API (e.g., `PUT /index/_mapping` ) via any official SDK (Python, Node.js, Java, Go) to explicitly push new field definitions into an existing index. |
| google-ai-mode | Typesense | Typesense (Best Out-of-the-Box for Unified Multilingual Indices) * * How it handles multiple languages: Typesense lets you specify a `locale` parameter per field in your schema (e.g., `"locale": "ja"`, `"locale": "en"`, `"locale": "ar"`). |
| perplexity | Algolia | Algolia is the clearest match. It supports typo tolerance and language-specific stop-word dictionaries, and lets you set the languages used for query processing. |
| chatgpt-search | Meilisearch | Meilisearch — probably the closest fit. Typo tolerance is built in and automatic, and its language-aware tokenizer/normalization handles multilingual text without requiring a separate analyzer configuration per language. |
| chatgpt-search | Typesense | Typesense — strong built-in typo tolerance and synonyms, with its feature set designed around a single search index. |
| google-ai | Meilisearch | The core challenge with traditional engines (like Elasticsearch, OpenSearch, Meilisearch, or Typesense) is that features like stop words, stemming, and tokenization are inherently language-dependent. |
| google-ai-mode | Typesense | Typesense vs Elasticsearch vs OpenSearch: Self Comparison of Top Search Platforms for Marketplaces Platform\| Full-Text Relevance\| Geo-Search Capability\| F... |
| perplexity | Algolia | The strongest fits are Algolia, Typesense, and Elasticsearch. All three support the combination of geographic search, facets/filters, and text relevance; the best choice depends on how much control and operational complexity you want. |



## Trend

Visibility delta: 4.533333333333333
Avg position delta: 1.7631578947368425
Citation count delta: 41