# Zilliz AI visibility in Search & Vector Databases

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

[Website](https://zilliz.com/)

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: 9
Total brands: 11
Measured responses: 150
Presence percent: 4.666666666666667
Share of voice percent: 2.871621621621622
Average position: 42.11764705882353
Docs presence percent: 1.3333333333333335
Blog presence percent: 2
Brand mention percent: 16.666666666666664


## Profile

Overview: Zilliz is a US-based enterprise software company and the creator of Milvus, the world's most widely adopted open-source vector database with over 44,000 GitHub stars and 100 million+ downloads. Founded in 2017 by Charles Xie, Zilliz offers two primary products: the open-source Milvus for self-managed deployments, and Zilliz Cloud, a fully managed, multi-cloud vector database service built on Milvus. Zilliz Cloud is available on AWS, GCP, and Azure across 30+ regions, with deployment options spanning serverless, dedicated, and Bring Your Own Cloud (BYOC) modes. The platform targets enterprises building AI applications requiring high-performance vector search at scale—from RAG pipelines and recommendation engines to fraud detection and drug discovery. Zilliz is named a Leader in the Forrester Wave Vector Database Providers Q3 2024 and serves 10,000+ enterprise customers.
Product summary: Zilliz provides open-source (Milvus) and fully managed (Zilliz Cloud) vector database technologies purpose-built for AI applications requiring high-performance embedding similarity search at billion-vector scale. Its proprietary Cardinal Search Engine powers Zilliz Cloud's performance advantages, while its AutoIndex feature removes manual tuning. The platform supports serverless, dedicated, and BYOC deployments across major clouds, integrates with leading AI frameworks, and delivers enterprise-grade security, compliance, and observability tooling.


### Key capabilities

- Billion-scale vector similarity search powered by open-source Milvus
- Proprietary Cardinal Search Engine delivering claimed 10x faster retrieval vs. self-hosted Milvus
- Fully managed cloud service with serverless, dedicated, and BYOC deployment modes
- Hybrid search combining dense vector, sparse/keyword, and full-text search
- AI-powered AutoIndex with zero manual tuning
- Built-in embedding pipelines and hosted model inference for automatic vectorization
- Multi-cloud availability on AWS, GCP, and Azure across 30+ regions
- Enterprise security: SOC2 Type II, ISO 27001, RBAC, SSO (SAML 2.0), CMEK, HIPAA-eligible
- Elastic horizontal scaling up to 500 CUs supporting 100B+ vectors
- BYOC (Bring Your Own Cloud) for data-sovereign and regulated deployments



### Target users

- AI/ML engineers building RAG and LLM-powered applications
- Enterprise data infrastructure and platform teams
- Data scientists running large-scale embedding workloads
- Developers prototyping or scaling semantic search products
- MLOps and DevOps teams in regulated industries (healthcare, finance)
- Organizations migrating from self-hosted Milvus to managed infrastructure



### Key use cases

- Retrieval Augmented Generation (RAG) for LLM-powered applications
- Semantic and multimodal similarity search (text, image, video, audio)
- AI agent memory and knowledge grounding
- E-commerce and media recommendation systems
- Fraud detection and anomaly detection
- Molecular and drug discovery search in life sciences
- Autonomous vehicle and multimodal data mining
- Semantic plagiarism detection and document intelligence

Integrations ecosystem: Zilliz Cloud integrates natively with major AI frameworks and model providers including OpenAI, Anthropic, Cohere, LangChain, LlamaIndex, DSPy, vLLM, and Voyage AI. Data pipeline integrations include Airbyte and Fivetran. SDKs are available for Python, Java, Go, and Node.js. The platform is available via the AWS, Google Cloud, and Azure Marketplaces (enabling committed-spend drawdown) and spans 30+ regions across five clouds. Open-source tooling includes Attu (GUI admin), Milvus CLI, GPTCache (LLM response caching), DeepSearcher, and Vector Transport Service for cross-system migration. Zilliz also holds AWS Agentic AI Specialization.
Pricing summary: Zilliz Cloud offers four tiers. Free: $0, 5 GB storage, up to 5 collections. Serverless (Standard): pay-as-you-go based on vCU consumption, starting from $0/month. Dedicated Standard: from $99/GB/month; Dedicated Enterprise: from $155/month with 99.95% SLA, RBAC, SSO, private networking, and 24/7 support. Business Critical: custom pricing with 99.99% SLA, CMEK, HIPAA eligibility, PITR, and priority support. BYOC: custom pricing for self-infrastructure deployments. Cluster pricing by type: Performance-optimized from $65/million vectors/month; Capacity-optimized from $20/million vectors/month; Tiered-storage from $7/million vectors/month. Annual commit plans offer additional credits. Available via AWS, GCP, and Azure Marketplaces for cloud-spend drawdown.
Review summary: Zilliz holds a 4.7/5 rating on G2 from 53 verified reviews (92% 5-star), recognized as both 'Highest Performer' and 'Easiest to Use' in G2's Summer 2025 Vector Database Grid Report. Users consistently praise blazing-fast query performance (sub-100ms for millions of vectors), ease of getting started via SDKs and documentation, seamless integration with LangChain and OpenAI embeddings, and the managed service's ability to eliminate infrastructure overhead. The most common criticism is cost at small or individual-project scale, where self-hosting is materially cheaper. Some users note friction with chunked large-file uploads.
Competitive positioning: Zilliz positions as the enterprise-grade, fully managed vector database built on open-source Milvus, combining maximum retrieval performance (claimed 10x faster than self-hosted Milvus via its proprietary Cardinal Search Engine) with managed simplicity. Named a Leader in the Forrester Wave Vector Database Providers Q3 2024 and the only vendor to simultaneously earn G2's 'Highest Performer' and 'Easiest to Use' in the Summer 2025 Vector Database Grid Report. Zilliz differentiates on open-source credibility (Milvus), multi-cloud reach (AWS, GCP, Azure, 30+ regions), enterprise compliance depth (SOC2 Type II, ISO 27001, HIPAA-eligible, CMEK), and its BYOC deployment model for regulated industries—targeting teams who outgrow self-hosted Milvus or commodity serverless offerings like Pinecone.
Limitations: Managed dedicated clusters can be costly for small projects or individual developers—G2 reviewers note that self-hosting on commodity infrastructure (e.g., Hetzner) can be significantly cheaper at small scale. Large-scale data uploads require manual chunking, which some users find cumbersome. Self-hosted Milvus configuration involves operational complexity that Zilliz Cloud resolves but at added cost. Free tier is limited to 5 GB and 5 collections. Advanced features (CMEK, PITR, continuous data protection) are gated to Business Critical tier requiring custom pricing negotiation.


### Source urls

- https://zilliz.com/
- https://zilliz.com/pricing
- https://zilliz.com/customers
- https://www.g2.com/products/zilliz/reviews
- https://www.crunchbase.com/organization/zilliz
- https://tracxn.com/d/companies/zilliz/__3kmvnTH8wUTuKs-hu5_rNyYWcfxlWLOAW0urecWctVY
- https://www.prnewswire.com/news-releases/zilliz-named-highest-performer-and-easiest-to-use-in-g2s-summer-2025-grid-report-for-vector-databases-302503076.html
- https://siliconangle.com/2025/02/11/zilliz-enhances-ai-cloud-offering-enhanced-bring-cloud-capabilities/
- https://www.unite.ai/charles-xie-founder-ceo-of-zilliz-interview-series/

Reviewed at: 2026-04-28T23:13:04.979+00:00


### Customer outcomes

| Customer | Summary | Metric |
| --- | --- | --- |
| Filevine | Deployed Zilliz Cloud to power legal case management with semantic search across millions of documents, managing 30 billion vectors and enabling concepts to be connected across documents even when exact terminology differs. | 60-80% time saved on consuming, digesting, and identifying relevant data points |
| Bosch | Adopted Milvus to optimize search efficiency and data processing for internal AI applications across engineering and manufacturing workflows. | 80% cost reduction |
| Rexera | Migrated to Zilliz Cloud for its AI agent architecture handling high-traffic real estate workflows, with hybrid search materially improving retrieval accuracy. | 40% accuracy improvement with hybrid search |
| Read AI | Uses Milvus as the central repository powering information retrieval across billions of records for millions of monthly active users, delivering real-time conversational intelligence. | 5× speedup in agentic search; sub 20-50ms retrieval latency |



### Reviews breakdown

| Platform | Score | Score max | Review count | Url |
| --- | --- | --- | --- | --- |
| G2 | 4.7 | 5 | 53 | https://www.g2.com/products/zilliz/reviews |



### Review themes



#### Praised

- Blazing-fast vector retrieval (sub-100ms for millions of vectors)
- Ease of use and quick onboarding via SDKs
- Comprehensive and clear documentation
- Seamless integration with LangChain and OpenAI embeddings
- Managed service eliminates infrastructure and ops burden
- Pay-as-you-go pricing accessible to individual developers
- Scalability from prototype to billions of vectors
- Open-source Milvus foundation reduces vendor lock-in risk



#### Criticized

- High cost for small or individual projects vs. self-hosting
- Manual chunked uploads required for very large datasets
- Some edge-case documentation gaps
- Advanced enterprise features locked to top-tier plans




### Company facts

Founded year: 2017
Hq: Redwood City, CA, USA


#### Founders

- Charles Xie

Employees range: 100-200
Total funding: $113M
Valuation: Not available
Arr: Not available
Customer count: 10,000+ enterprises
Status: Private (Series B)


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 | 0 | 0 |
| perplexity | 1 | 4 |
| chatgpt-search | 0 | 0 |
| google-ai | 0 | 0 |
| xai-search | 6 | 24 |



## Strengths





## 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 |
| Which vector databases integrate best with standard observability stacks — which ones make it easy to monitor and analyze query performance? | 4 |
| What are the best search engines for indexing an existing relational database without needing a full data pipeline from day one? | 4 |



## Topic scores

| Topic name | Prompt count | Cited prompt count |
| --- | --- | --- |
| Capability | 5 | 1 |
| Developer Experience | 5 | 0 |
| Integrations & Ecosystem | 5 | 3 |
| Performance & Reliability | 5 | 2 |
| Setup & First Run | 5 | 0 |



## 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: 4
Chatgpt-search: Not available
Google-ai: Not available
Xai-search: 55



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



#### 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: Not available
Google-ai: Not available
Xai-search: 34



#### 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: Not available
Perplexity: Not available
Chatgpt-search: Not available
Google-ai: Not available
Xai-search: 53



#### 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: 34



#### 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: 41



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



#### 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: 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: 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: 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 |
| 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: 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 | 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: 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 |
| 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://zilliz.com/pricing | Zilliz Cloud Pricing - Fully Managed Vector Database for AI ... | zilliz.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/6b127064-92bf-4295-83ce-71eec7540861/1d81942c34bc04f43d608792b8bbb9c9ac39661a.png | commercial | product_page | 2 | 2 |
| https://docs.zilliz.com/docs/limits | Zilliz Cloud Limits \| Cloud \| Zilliz Cloud Developer Hub | docs.zilliz.com | Not available | commercial | documentation | 2 | 2 |
| https://zilliz.com/ | Zilliz Vector Lakebase for Enterprise AI, Powered by Milvus | zilliz.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/6b127064-92bf-4295-83ce-71eec7540861/1d81942c34bc04f43d608792b8bbb9c9ac39661a.png | commercial | faq | 2 | 2 |
| https://zilliz.com/blog/qdrant-cloud-vs-zilliz | How to Choose a Vector Database: Qdrant Cloud vs. ... | zilliz.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/6b127064-92bf-4295-83ce-71eec7540861/1d81942c34bc04f43d608792b8bbb9c9ac39661a.png | commercial | blog | 2 | 0 |
| https://zilliz.com/what-is-milvus | Milvus \| Open-source Vector Database created by Zilliz | zilliz.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/6b127064-92bf-4295-83ce-71eec7540861/1d81942c34bc04f43d608792b8bbb9c9ac39661a.png | commercial | home | 1 | 0 |
| https://zilliz.com/comparison/milvus-vs-zilliz-cloud | Milvus vs Zilliz Cloud \| Vector Database Comparison | zilliz.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/6b127064-92bf-4295-83ce-71eec7540861/1d81942c34bc04f43d608792b8bbb9c9ac39661a.png | commercial | home | 1 | 0 |
| https://zilliz.com/comparison/weaviate-vs-pinecone | Weaviate vs Pinecone \| Vector Database Comparison | zilliz.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/6b127064-92bf-4295-83ce-71eec7540861/1d81942c34bc04f43d608792b8bbb9c9ac39661a.png | commercial | home | 1 | 0 |
| https://zilliz.com/vdbbench-leaderboard | VectorDBBench: An Open-Source VectorDB Benchmark Tool | zilliz.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/6b127064-92bf-4295-83ce-71eec7540861/1d81942c34bc04f43d608792b8bbb9c9ac39661a.png | commercial | home | 1 | 0 |



## Response excerpts

| Prompt text | Platform | Excerpt |
| --- | --- | --- |
| Which hosted vector databases scale best to billions of high-dimensional embeddings — what are the real limitations teams hit at that scale? | chatgpt-search | I’d put Milvus/Zilliz and Pinecone at the top, with Weaviate viable depending on workload. |
| Which hosted vector search services offer the best p99 query latency when searching 50 million vectors — what should I realistically expect? | chatgpt-search | My shortlist would be Qdrant Cloud, Zilliz Cloud, Pinecone, and Weaviate Cloud. |
| Which hosted vector databases scale best to billions of high-dimensional embeddings — what are the real limitations teams hit at that scale? | bing-copilot-search | At billion-scale, no hosted vector database is “frictionless”: Pinecone, Milvus/Zilliz Cloud, Qdrant Cloud, and Weaviate Cloud all scale to billions of embeddings, but teams consistently hit limits around memory blow‑up, SSD I/O bottlenecks, metadata f... |



## 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: 0.8
Avg position delta: Not available
Citation count delta: 2