# Elastic AI visibility in Search & Vector Databases

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

[Website](https://www.elastic.co/)

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: 1
Total brands: 11
Measured responses: 150
Presence percent: 31.333333333333336
Share of voice percent: 16.554054054054053
Average position: 11.63265306122449
Docs presence percent: 20.666666666666668
Blog presence percent: 1.3333333333333335
Brand mention percent: 36.666666666666664


## Profile

Overview: Elastic is a Dutch-American publicly traded software company (NYSE: ESTC), founded in 2012 by Shay Banon, Simon Willnauer, Steven Schuurman, and Uri Boness. It develops the Elasticsearch platform—a distributed, Apache Lucene-based search and analytics engine—alongside the broader Elastic Stack (ELK: Elasticsearch, Logstash, Kibana, Beats). Elastic positions itself as 'The Search AI Company,' offering a unified platform across three solution pillars: Search (application and enterprise search, vector database), Observability (log analytics, APM, infrastructure monitoring), and Security (SIEM, XDR, endpoint protection). Trusted by over 50% of the Fortune 500 and 17,000+ customers globally, Elastic generated $1.48 billion in revenue in fiscal year 2025. Deployment options span Elastic Cloud Serverless, Cloud Hosted (AWS, Azure, GCP), and self-managed on-premises.
Product summary: Elastic builds and operates the Elasticsearch platform—the world's most widely deployed search and analytics engine—along with the Elastic Stack (ELK). The platform provides distributed full-text search, vector database capabilities, hybrid search (BM25 + dense/sparse vectors via ELSER), real-time analytics, log management, observability, and AI-driven security. It is available as a fully managed serverless cloud service, a hosted cloud deployment, or self-managed on any infrastructure.


### Key capabilities

- Full-text search powered by Apache Lucene and BM25 with advanced Query DSL
- Native vector database with dense and sparse (ELSER) vector support for semantic and hybrid search
- Hybrid search with Reciprocal Rank Fusion (RRF) and weighted linear combination relevance blending
- Elastic Learned Sparse EncodeR (ELSER) for domain-specific neural sparse retrieval
- Kibana dashboards for real-time data visualization, alerting, and search analytics
- Elastic Observability: unified logs, metrics, APM traces, and RUM with AIOps anomaly detection
- Elastic Security: next-gen SIEM, XDR, endpoint protection, and AI-driven threat detection
- Distributed, horizontally scalable architecture with shard-based clustering and high availability
- Flexible deployment: Elastic Cloud Serverless (usage-based), Cloud Hosted (resource-based), and self-managed
- Agentic AI and RAG support via Elastic Agent Builder and context engineering tools



### Target users

- Enterprise software engineers and backend developers building search-powered applications
- DevOps and SRE teams managing log analytics, APM, and infrastructure observability
- Security operations center (SOC) analysts using SIEM and XDR platforms
- AI/ML engineers building RAG pipelines, semantic search, and LLM-powered applications
- Data engineers and architects managing large-scale data ingestion and analytics
- Platform and infrastructure teams at Fortune 500 enterprises requiring multi-cloud or hybrid deployments



### Key use cases

- Enterprise and application search (e-commerce, site search, employee search)
- Log analytics and centralized log management (ELK Stack)
- Observability and application performance monitoring (APM, infrastructure monitoring)
- Security information and event management (SIEM) and threat detection
- Vector database and semantic search for AI/LLM applications
- Retrieval-Augmented Generation (RAG) and AI agent context engineering
- Real-time analytics and business intelligence on large-scale datasets
- Geospatial and time-series data search and analytics

Integrations ecosystem: Elastic offers 350+ pre-built integrations and data connectors via the Elastic Integrations catalog. The platform is natively available on AWS, Azure, and GCP marketplaces. Verified G2 integrations include Apache Kafka, Adobe Experience Manager, AWS Lambda, Azure Pipelines, Cribl Stream, CrowdStrike Falcon, Grafana Labs, GitHub, Google Cloud Storage, Jira, Microsoft Power BI, MongoDB, Redis, ServiceNow ITSM, Slack, and Splunk On-Call. Elastic's AI ecosystem includes Jina AI (acquired, providing embeddings and rerankers), Hugging Face model support, OpenAI compatibility, and LangChain/LlamaIndex integration for RAG pipelines. The platform supports OpenTelemetry natively for observability data collection. Kubernetes-native deployment is supported via Elastic Cloud on Kubernetes (ECK), and FedRAMP High Authorization is available on Elastic Cloud Hosted.
Pricing summary: Elastic offers three deployment pricing models. Elastic Cloud Serverless uses usage-based pricing (pay-as-you-go monthly or prepaid), with no cluster management overhead. Elastic Cloud Hosted is resource-based, starting at $99/month, available on AWS, Azure, GCP, and Alibaba across 60 regions, with four support tiers (Standard, Gold, Platinum, Enterprise) and a 99.95% uptime SLA on Platinum/Enterprise. Self-managed (on-premises or private cloud) uses license-based pricing tied to node count and RAM, with Platinum and Enterprise subscription tiers. A 14-day free trial with no credit card is available for Elastic Cloud. Self-managed Elasticsearch can also be downloaded and run locally for free under open-source licensing.
Review summary: Reviewers consistently praise Elasticsearch for its speed, scalability, and flexibility in handling large volumes of structured and unstructured data with near-real-time performance. The Query DSL's expressiveness and the Elastic Stack's end-to-end integration (Kibana dashboards, Logstash/Beats ingestion) are frequently cited as major strengths. Common criticisms include a steep learning curve, operational complexity in managing clusters at scale, high resource consumption under heavy workloads, and cost at enterprise scale. Vector search capabilities are viewed positively for enterprise AI/RAG use cases, though purpose-built vector databases are acknowledged to outperform Elasticsearch on pure dense vector query speed.
Competitive positioning: Elastic positions itself as 'The Search AI Company,' differentiating through a unified platform that combines full-text (BM25), vector, hybrid, and semantic search within a single engine—eliminating the need to manage separate search and vector database infrastructure. Unlike pure-play vector databases (Pinecone, Qdrant, Weaviate), Elastic extends into observability and security SIEM, making it attractive for enterprises seeking to consolidate tooling. Its ELSER (Elastic Learned Sparse EncodeR) sparse neural model and native hybrid retrieval via Reciprocal Rank Fusion (RRF) offer relevance tuning depth that purpose-built vector DBs typically lack. Trusted by 50%+ of Fortune 500 and listed as a Leader in the 2025 Gartner Magic Quadrant for Observability Platforms and Forrester Wave Security Analytics Q2 2025, Elastic competes on breadth, enterprise maturity, and ecosystem depth rather than vector-only performance.
Limitations: Elastic carries a steep learning curve, particularly for cluster management, Query DSL, shard balancing, and relevance tuning—often requiring dedicated Elasticsearch engineers. Resource consumption (CPU, memory, disk) scales significantly under high-ingest or high-query workloads, contributing to cost concerns at scale. Pure vector search latency lags behind purpose-built vector databases (e.g., Milvus/Zilliz benchmarks show 30x+ performance gaps on dense vector search at 1M vectors). Elasticsearch does not support relational data models natively, limiting certain join-heavy query patterns. Licensing history (2021 shift from Apache 2.0 to SSPL, then re-introduction of AGPL for Elasticsearch 8.x) created ecosystem confusion and led to the AWS OpenSearch fork. Debugging in distributed environments is complex, and downgrades after version upgrades are not straightforward.


### Source urls

- https://www.elastic.co/
- https://www.elastic.co/pricing
- https://www.g2.com/products/elastic-elasticsearch/reviews
- https://www.g2.com/sellers/elastic
- https://www.gartner.com/reviews/market/insight-engines/compare/elastic-vs-yext
- https://www.gartner.com/reviews/market/enterprise-search-engine/vendor/elastic/product/elastic-search
- https://en.wikipedia.org/wiki/Elastic_NV
- https://www.elastic.co/about/leadership
- https://www.elastic.co/customers/pepsico
- https://stockanalysis.com/stocks/estc/revenue/
- https://pitchbook.com/profiles/company/55574-02
- https://dcf-model.com/blogs/history/estc-history-mission-ownership
- https://pureinsights.com/blog/2026/from-vector-hype-to-hybrid-reality-is-elasticsearch-still-the-right-bet/

Reviewed at: 2026-04-28T23:12:42.447+00:00


### Customer outcomes

| Customer | Summary | Metric |
| --- | --- | --- |
| PepsiCo | PepsiCo deployed Elastic Observability as its Full Stack Observability (FSO) platform, consolidating MELT data from 38+ critical applications. The deployment reduced mean time to resolution and hardware costs while achieving high uptime and a 23% automation rate in incident manag | 30% MTTR reduction, 25% hardware cost reduction, 99.9% uptime |
| UOL | UOL deployed Elastic Security to improve threat detection and incident response, dramatically cutting the time required to identify and resolve security incidents across its infrastructure. | 80% reduction in incident resolution time |



### Reviews breakdown

| Platform | Score | Score max | Review count | Url |
| --- | --- | --- | --- | --- |
| G2 | 4.5 | 5 | 290 | https://www.g2.com/products/elastic-elasticsearch/reviews |
| G2 (Elastic Brand Overall) | 4.4 | 5 | 498 | https://www.g2.com/sellers/elastic |
| Gartner Peer Insights | 4.4 | 5 | 325 | https://www.gartner.com/reviews/market/insight-engines/vendor/elastic/product/elastic-search |



### Review themes



#### Praised

- Near real-time search speed at massive scale
- Flexible and powerful Query DSL
- Seamless ELK Stack integration (Kibana, Logstash, Beats)
- Horizontal scalability and distributed architecture
- Strong open-source community and ecosystem
- Versatility across search, observability, and security use cases
- Vector and hybrid search for AI/RAG workloads
- Comprehensive REST APIs and developer documentation



#### Criticized

- Steep learning curve for cluster management and tuning
- High resource consumption (CPU, memory) at scale
- Operational complexity of shard and index lifecycle management
- Expensive at enterprise scale
- Pure vector search performance lags purpose-built vector DBs
- No native relational data model
- Complex troubleshooting in distributed environments
- Difficult to find experienced Elasticsearch engineers




### Company facts

Founded year: 2012
Hq: Amsterdam, Netherlands (operational HQ: San Francisco, CA, USA)


#### Founders

- Shay Banon
- Simon Willnauer
- Steven Schuurman
- Uri Boness

Employees range: 3500-4000
Total funding: ~$162M (pre-IPO)
Valuation: Not available
Arr: Not available
Customer count: 17,000+
Status: Public (NYSE: ESTC)


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 | 11 | 44 |
| chatgpt-search | 14 | 56.00000000000001 |
| google-ai | 3 | 12 |
| xai-search | 14 | 56.00000000000001 |



## Strengths

| Prompt text | Platform count | Avg position |
| --- | --- | --- |
| What are the best search engines for indexing an existing relational database without needing a full data pipeline from day one? | 3 | 1 |
| Which search platform SDKs handle index schema migrations best when adding new fields without a full index rebuild? | 5 | 1.6 |
| Which search platforms work best as the retrieval layer for an AI agent that needs to query across multiple data sources and indexes? | 3 | 1.6666666666666667 |
| Which search platforms offer the best developer experience for combining keyword search with semantic vector search in a single query? | 2 | 2 |
| Which search platforms make it easiest to migrate from SQL LIKE-query search without taking the app offline during the transition? | 2 | 3.5 |



## Gaps

| Prompt text | Competitor presence count |
| --- | --- |
| Which search engines handle synonyms, typo tolerance, and stop words across multiple languages without duplicating index configuration? | 5 |
| Which vector databases integrate best with standard observability stacks — which ones make it easy to monitor and analyze query performance? | 4 |
| Which hosted search platforms have the easiest relevance ranking tuning for a product catalog use case — what's the learning curve like? | 4 |
| Which hosted search platforms deliver good out-of-the-box relevance with minimal tuning before results feel useful to end users? | 4 |
| Which vector databases handle real-time index updates without degrading query performance during high write loads? | 3 |



## Topic scores

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



## 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: 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 |
| --- | --- |
| 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: Not available
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: 6
Perplexity: Not available
Chatgpt-search: Not available
Google-ai: Not available
Xai-search: 15



#### 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: 4
Perplexity: 1
Chatgpt-search: 1
Google-ai: 1
Xai-search: 1



#### 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: 4
Chatgpt-search: 3
Google-ai: Not available
Xai-search: 25



#### 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: Not available
Chatgpt-search: Not available
Google-ai: Not available
Xai-search: 26



#### 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: 1
Perplexity: 5
Chatgpt-search: 16
Google-ai: Not available
Xai-search: Not available



#### 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: 10
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: 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 |
| --- | --- |
| 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: 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 |
| --- | --- |
| 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: 5
Google-ai: Not available
Xai-search: 2



#### 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: 5
Chatgpt-search: 1
Google-ai: Not available
Xai-search: 4



#### 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: 1
Chatgpt-search: 1
Google-ai: Not available
Xai-search: 1



#### 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: 1
Chatgpt-search: 3
Google-ai: 1
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: 7
Chatgpt-search: 1
Google-ai: Not available
Xai-search: 31



#### 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: 6
Perplexity: Not available
Chatgpt-search: 7
Google-ai: Not available
Xai-search: 18



#### 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: 6
Chatgpt-search: 10
Google-ai: Not available
Xai-search: 25



#### 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: 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 |
| --- | --- |
| 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: 1
Chatgpt-search: Not available
Google-ai: 4
Xai-search: 23



#### 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: 4
Perplexity: 1
Chatgpt-search: 1
Google-ai: Not available
Xai-search: 1



#### 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: 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 |
| --- | --- |
| 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: 6
Chatgpt-search: 11
Google-ai: Not available
Xai-search: Not available



#### 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: 32



#### 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: Not available
Perplexity: Not available
Chatgpt-search: 3
Google-ai: Not available
Xai-search: 1



#### 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://www.elastic.co/docs/solutions/search/hybrid-search | Hybrid search \| Elastic Docs | elastic.co | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/6b127064-92bf-4295-83ce-71eec7540861/0bb868f9ca2bffbbe9687f63cf342c7fdfb91694.png | commercial | documentation | 7 | 7 |
| https://www.elastic.co/docs/solutions/search/vector | Vector search in Elasticsearch | elastic.co | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/6b127064-92bf-4295-83ce-71eec7540861/0bb868f9ca2bffbbe9687f63cf342c7fdfb91694.png | commercial | documentation | 7 | 7 |
| https://www.elastic.co/docs/solutions/search/multimodal-search | Multimodal search in Elasticsearch \| Elastic Docs | elastic.co | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/6b127064-92bf-4295-83ce-71eec7540861/0bb868f9ca2bffbbe9687f63cf342c7fdfb91694.png | commercial | documentation | 7 | 7 |
| https://www.elastic.co/what-is/hybrid-search | A Comprehensive Hybrid Search Guide \| Elastic | elastic.co | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/6b127064-92bf-4295-83ce-71eec7540861/0bb868f9ca2bffbbe9687f63cf342c7fdfb91694.png | commercial | documentation | 7 | 7 |
| https://www.elastic.co/docs/deploy-manage/distributed-architecture/clusters-nodes-shards | Clusters, nodes, and shards \| Elastic Docs | elastic.co | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/6b127064-92bf-4295-83ce-71eec7540861/0bb868f9ca2bffbbe9687f63cf342c7fdfb91694.png | commercial | documentation | 5 | 5 |
| https://www.elastic.co/search-labs/integrations/llama-index | LlamaIndex for Elasticsearch & RAG | elastic.co | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/6b127064-92bf-4295-83ce-71eec7540861/0bb868f9ca2bffbbe9687f63cf342c7fdfb91694.png | commercial | product_page | 4 | 4 |
| https://www.elastic.co/docs/manage-data/ingest/ingesting-data-from-applications/ingest-data-from-relational-database-into-elasticsearch-service | Ingest data from a relational database \| Elastic Docs | elastic.co | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/6b127064-92bf-4295-83ce-71eec7540861/0bb868f9ca2bffbbe9687f63cf342c7fdfb91694.png | commercial | documentation | 4 | 4 |
| https://www.elastic.co/elasticsearch/rag | RAG platform solutions for enterprise search - Elastic | elastic.co | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/6b127064-92bf-4295-83ce-71eec7540861/0bb868f9ca2bffbbe9687f63cf342c7fdfb91694.png | commercial | landing_page | 4 | 4 |



## Response excerpts

| Prompt text | Platform | Excerpt |
| --- | --- | --- |
| Which hosted search platforms deliver good out-of-the-box relevance with minimal tuning before results feel useful to end users? | chatgpt-search | Website ## 5. Elastic Cloud Elastic Cloud is a powerful managed option, but I wouldn't put it first if minimizing relevance tuning is the main goal. |
| Which search platforms offer the best developer experience for combining keyword search with semantic vector search in a single query? | chatgpt-search | ...search with minimal query code \| \| Elasticsearch \| Best for search control \| Production search, complex relevance, existing Elastic stacks \| \| Qdrant \| Best for retrieval flexibility \| Dense + sparse retrieval, explicit fusion and multi-stage queries \| \|... |
| 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? | chatgpt-search | Load Data to Elastic in Real-time ETL & CDCElasticsearch Materialization Connector Best for: near-real-time sync with less infrastructure to maintain. |



## Competitor excerpts

| Platform | Competitor name | Excerpt |
| --- | --- | --- |
| 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 | Qdrant | Qdrant, Milvus, and Weaviate integrate best with standard observability stacks like Prometheus, Grafana, and OpenTelemetry , providing native metric endpoints and tracing hooks for query performance analysis. |
| perplexity | Qdrant | For a Prometheus/Grafana-based stack, the strongest fits among the databases compared are Milvus, Qdrant, and Weaviate. |
| chatgpt-search | Qdrant | ## My shortlist ## 1. Qdrant Best overall for Prometheus-native monitoring Qdrant exposes Prometheus/OpenMetrics metrics, supports per-collection API metrics, and provides telemetry and a slow-request log. |
| google-ai | Qdrant | Qdrant (Best Overall for Native Stack Integration) Qdrant stands out for production-grade observability because it natively outputs metrics in standard OpenMetrics format . Qdrant * Observability Stack Integration: Seamlessly connects with... |
| google-ai-mode | Algolia | Algolia * * The Paradigm: Configurable "Relevance Formula" (a strict cascading tie-breaker pipeline: Typos → Exact match → Proximity → Attribute → Custom Ranking). |
| google-ai-mode | Typesense | Typesense vs Algolia: Self * 2\. |
| chatgpt-search | Algolia | My starting shortlist would be Algolia, Constructor, Typesense Cloud, and Elasticsearch Serverless. |



## Trend

Visibility delta: 2.400000000000002
Avg position delta: 1.678638059701493
Citation count delta: 35