AI visibility report
Zilliz ranks #8 in Search & Vector Databases AI search.
Outside the top three on 21 of the 25 prompts buyers actually ask.
Elastic is cited on 11 of those losses.
Free trial. Setup comes pre-filled for Zilliz.
Track Zilliz across these prompts daily.
Start free trial#8 among 11 vendors · still absent from 94% of tracked prompt responses
Top-3 citations across 150 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
Grok
Perplexity
ChatGPT
Google AI Mode
Gemini Search
Bing CopilotVisible, but narrative can improve. Zilliz ranks #8 on presence but #9 on sentiment. The brand appears relatively often, but competitors may be getting more favorable language when they appear.
Where Zilliz is losing
Prompts where competitors are visible and Zilliz is not.
These prompt-level losses are the first prompts to track and repair.
Where Zilliz is winning2
Which vector databases handle filtered similarity search efficiently — which ones support nearest neighbor search scoped to a specific user's namespace?
Avg # 2.0 · 1 platform
Which hosted vector search services offer the best p99 query latency when searching 50 million vectors — what should I realistically expect?
Avg # 5.0 · 1 platform
Where Zilliz is losing5
Which search engines handle synonyms, typo tolerance, and stop words across multiple languages without duplicating index configuration?
Competitors on 5 platforms
Track this promptWhich search platforms best support geo-search and faceted filtering combined with full-text relevance for a marketplace application?
Competitors on 3 platforms
Track this promptWhich search platform SDKs handle index schema migrations best when adding new fields without a full index rebuild?
Competitors on 3 platforms
Track this promptWhich hosted search platforms deliver good out-of-the-box relevance with minimal tuning before results feel useful to end users?
Competitors on 3 platforms
Track this promptWhich vector databases use the best ANN algorithms for recall at scale — how do the implementations differ across the major platforms?
Competitors on 3 platforms
Track this prompt
Track Zilliz daily before the next report refresh.
Track these gapsResearch dossierCapabilities, use cases, sources, reviews, pricing, and FAQ
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.
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 Facts
- Founded
- 2017
- HQ
- Redwood City, CA, USA
- Founders
- Charles Xie
- Employees
- 100-200
- Funding
- $113M
- Customers
- 10,000+ enterprises
- Status
- Private (Series B)
Target users
Key Capabilities10
- 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
Key Use Cases8
- 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
Zilliz customer outcomes
60-80% time saved on consuming, digesting, and identifying relevant data points
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.
80% cost reduction
Adopted Milvus to optimize search efficiency and data processing for internal AI applications across engineering and manufacturing workflows.
40% accuracy improvement with hybrid search
Migrated to Zilliz Cloud for its AI agent architecture handling high-traffic real estate workflows, with hybrid search materially improving retrieval accuracy.
5× speedup in agentic search; sub 20-50ms retrieval latency
Uses Milvus as the central repository powering information retrieval across billions of records for millions of monthly active users, delivering real-time conversational intelligence.
Recent Trend
How AI describes Zilliz3
...vector databases that scale best to billions of embeddings tend to be Pinecone (fully managed, serverless options), Milvus/Zilliz Cloud (billion-scale, strong open-source + managed path), and Qdrant/Weaviate with careful sharding and infrastructure plan...
Which hosted vector databases scale best to billions of high-dimensional embeddings — what are the real limitations teams hit at that scale?
Milvus (and Zilliz cloud) * Strengths: Distributed architecture with horizontal scaling, shards, and asynchronous background merging; supports real-time updates with near real-time visibility.
Which vector databases handle real-time index updates without degrading query performance during high write loads?
...ith managed ops | Distributed HNSW, disk tiers | Memory-heavy; search-then-filter patterns break multi-tenancy | | Milvus/Zilliz Cloud | GPU-accelerated ANN, multimedia embeddings | IVF-PQ + GPU indexing | Complex ops; quantization reduces accuracy f...
Which hosted vector databases scale best to billions of high-dimensional embeddings — what are the real limitations teams hit at that scale?
Most cited sources8
5Top 5 Open Source Vector Databases in 2025 - Zilliz blog
zilliz.com·Blog Post
- 3
For a given application requiring real-time updates (inserting new ...
zilliz.com·Faq
- 3
How Multimodal Retrieval Transforms Image Search - Zilliz blog
zilliz.com·Blog Post
- 2
Zilliz: Vector Database built for enterprise-grade AI applications
zilliz.com·Faq
- 2
How to Choose a Vector Database: Qdrant Cloud vs. ...
zilliz.com·Faq
- 1
Milvus | Open-source Vector Database created by Zilliz
zilliz.com·Comparison
Alternatives in Search & Vector Databases6
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.
Reviews
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
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.
Pricing
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.
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.
Frequently asked questions
Topic coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
| Prompt | ChatGPT | Google AI Mode | Perplexity | Gemini Search | Bing Copilot | Grok |
|---|---|---|---|---|---|---|
Capability3/5 cited (60%) | ||||||
Which vector databases handle filtered similarity search efficiently — which ones support nearest neighbor search scoped to a specific user's namespace? | A competitor was cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which search platforms best support geo-search and faceted filtering combined with full-text relevance for a marketplace application? | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which hosted vector databases scale best to billions of high-dimensional embeddings — what are the real limitations teams hit at that scale? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited |
Which search platforms support multimodal search combining text queries with image embeddings — what are the best options for this use case? | A competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
What are the tradeoffs between dense vector search and sparse keyword search, and which platforms offer the best hybrid search implementations? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Developer Experience0/5 cited (0%) | ||||||
Which search platforms offer the best developer experience for combining keyword search with semantic vector search in a single query? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which search platform SDKs handle index schema migrations best when adding new fields without a full index rebuild? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which search engines have the best dashboard and query explorer tools for non-engineers to understand why certain results rank higher? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which search engines handle synonyms, typo tolerance, and stop words across multiple languages without duplicating index configuration? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
Which hosted search platforms have the easiest relevance ranking tuning for a product catalog use case — what's the learning curve like? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Integrations & Ecosystem3/5 cited (60%) | ||||||
What tools help keep a search index in sync with a primary relational database without building a custom ETL pipeline — what do teams typically use? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which vector databases integrate best with standard observability stacks — which ones make it easy to monitor and analyze query performance? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited |
Which search platforms work best as the retrieval layer for an AI agent that needs to query across multiple data sources and indexes? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited |
Which search platforms have native integrations with popular LLM orchestration frameworks for building RAG pipelines with minimal boilerplate? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited |
Which vector databases make it easiest to swap out the embedding model later without rebuilding the entire index — what should I evaluate for model portability? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Performance & Reliability2/5 cited (40%) | ||||||
Which vector databases use the best ANN algorithms for recall at scale — how do the implementations differ across the major platforms? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which search platforms scale horizontally best when index size grows past what fits on a single node — what are the options? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
What are the best managed search services versus self-hosted options in terms of operational overhead and reliability at scale? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which vector databases handle real-time index updates without degrading query performance during high write loads? | Neither your brand nor a competitor was cited | A competitor was cited | Your brand was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited |
Which hosted vector search services offer the best p99 query latency when searching 50 million vectors — what should I realistically expect? | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited |
Setup & First Run0/5 cited (0%) | ||||||
Which hosted search platforms deliver good out-of-the-box relevance with minimal tuning before results feel useful to end users? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which search platforms make it easiest to migrate from SQL LIKE-query search without taking the app offline during the transition? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
What are the best vector databases for a RAG application when you're just starting out with embeddings — which ones have the simplest setup path? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What's the fastest way to add full-text search to a Next.js app without setting up a dedicated search cluster — which services are worth looking at? | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
What are the best search engines for indexing an existing relational database without needing a full data pipeline from day one? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Turn this matrix into daily prompt monitoring.
Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Meilisearch | 20.0% | 23.0% | 8.7% | 14.7% | 33.3% | #24.9 | +0.33 |
| 2 | Elastic | 16.7% | 11.4% | 4.7% | 1.3% | 32.7% | #19.1 | +0.33 |
| 3 | Pinecone | 14.0% | 10.0% | 4.0% | 3.3% | 50.7% | #32.1 | +0.37 |
| 4 | Algolia | 12.7% | 15.1% | 8.0% | 7.3% | 31.3% | #28.5 | +0.38 |
| 5 | Typesense | 11.3% | 15.1% | 9.3% | 0.0% | 26.7% | #31.0 | +0.37 |
| 6 | Weaviate | 8.0% | 6.8% | 1.3% | 4.7% | 50.7% | #32.0 | +0.38 |
| 7 | Qdrant | 8.0% | 9.5% | 4.0% | 2.0% | 46.7% | #45.8 | +0.33 |
| 8 | Zilliz | 6.0% | 4.9% | 0.7% | 3.3% | 20.0% | #40.4 | +0.14 |
| 9 | Vespa | 2.7% | 3.8% | 1.3% | 2.0% | 2.7% | #42.9 | +0.00 |
| 10 | Chroma | 1.3% | 0.5% | 0.7% | 0.0% | 12.0% | #45.0 | +0.25 |
| 11 | Trieve | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
Turn this into your team dashboard
Sign up to unlock project-level analytics, daily tracking, actionable insights, custom prompt configurations, adoption tracking, AI traffic analytics and more.
Free trial. Setup comes pre-filled from this report.