AI visibility report
Meilisearch ranks #1 in Search & Vector Databases AI search.
Outside the top three on 13 of the 25 prompts buyers actually ask.
Elastic is cited on 5 of those losses.
Free trial. Setup comes pre-filled for Meilisearch.
Track Meilisearch across these prompts daily.
Start free trialBest among 11 vendors · still absent from 81.3% of tracked prompt responses
Top-3 citations across 150 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
Grok
Gemini Search
Perplexity
ChatGPT
Google AI Mode
Bing CopilotLeader, with room to expand. Meilisearch leads this category on presence and share of voice, but appears in only 18.7% of tracked prompt responses. The priority is defending current wins while expanding absolute coverage.
Where Meilisearch is losing
Prompts where competitors are visible and Meilisearch is not.
These prompt-level losses are the first prompts to track and repair.
Where Meilisearch is winning4
Which search platforms work best as the retrieval layer for an AI agent that needs to query across multiple data sources and indexes?
Avg # 2.0 · 1 platform
Which search platforms scale horizontally best when index size grows past what fits on a single node — what are the options?
Avg # 5.0 · 2 platforms
Which hosted search platforms deliver good out-of-the-box relevance with minimal tuning before results feel useful to end users?
Avg # 6.5 · 2 platforms
Which hosted vector databases scale best to billions of high-dimensional embeddings — what are the real limitations teams hit at that scale?
Avg # 33.0 · 1 platform
Where Meilisearch is losing5
Which search platforms support multimodal search combining text queries with image embeddings — what are the best options for this use case?
Competitors on 3 platforms
Track this promptWhich search platforms offer the best developer experience for combining keyword search with semantic vector search in a single query?
Competitors on 2 platforms
Track this promptWhich search platform SDKs handle index schema migrations best when adding new fields without a full index rebuild?
Competitors on 2 platforms
Track this promptWhat are the tradeoffs between dense vector search and sparse keyword search, and which platforms offer the best hybrid search implementations?
Competitors on 2 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 2 platforms
Track this prompt
Track Meilisearch daily before the next report refresh.
Track these gapsResearch dossierCapabilities, use cases, sources, reviews, pricing, and FAQ
Overview
Meilisearch is an open-source, developer-focused search and AI retrieval platform founded in Paris, France in 2018. Written in Rust, it delivers sub-50ms search-as-you-type results with built-in typo tolerance, relevancy tuning, and hybrid search that blends full-text and semantic (vector) retrieval. The platform is available as a free self-hosted Community Edition (MIT license), a fully-managed Meilisearch Cloud (starting at $23–$30/month), and an Enterprise tier with advanced features such as sharding and S3 snapshots. With more than 56,000 GitHub stars and SDKs across ten languages, Meilisearch targets developers and product teams seeking a simple, cost-effective alternative to Algolia or Elasticsearch. It increasingly serves RAG and AI retrieval workloads via its vector storage and LangChain integration.
Meilisearch is a unified search and AI retrieval platform offering an open-source search engine API and a managed cloud service. It provides hybrid full-text and semantic search, vector storage, typo tolerance, geosearch, faceted filtering, and multi-tenancy out of the box — with near-zero configuration required to get started.
Key Facts
- Founded
- 2018
- HQ
- Paris, France
- Founders
- Quentin de Quelen, Clément Renault, Thomas Payet
- Employees
- 11-50
- Funding
- ~$22M
- Status
- Private
Target users
Key Capabilities10
- Sub-50ms search-as-you-type with typo tolerance
- Hybrid search combining full-text and semantic/vector retrieval
- Vector storage for similarity queries and RAG pipelines
- Filtering, faceting, and multi-attribute sorting
- Geosearch with location-based filtering and ranking
- Multi-language support with optimized tokenization
- Multi-tenancy via scoped API keys and tenant tokens
- Federated search across multiple indexes (Cloud)
- Search analytics and monitoring dashboard (Cloud)
- Open-source self-hosted (MIT) and fully-managed cloud deployments
Key Use Cases8
- E-commerce product search and catalog discovery
- SaaS and web application in-app search
- Documentation and knowledge-base search
- RAG/AI retrieval pipelines and vector lookup
- Enterprise internal document and asset search
- B2B marketplace search at scale
- Media and content discovery platforms
- Multi-tenant customer-facing search in CRM or support tools
Meilisearch customer outcomes
43% increase in conversion rates
The online bookstore adopted Meilisearch to improve search result quality and relevancy across its catalog.
5x growth in search volume
The baby-registry platform migrated to Meilisearch from Algolia for higher relevancy and lower maintenance, observing significant growth in search activity.
The global B2B wholesale platform migrated from Algolia to Meilisearch Cloud, improving developer experience and achieving substantial cost savings on search infrastructure.
300,000+ models, datasets, and demos indexed
The AI model hub uses Meilisearch to power search across its catalog, combining semantic search with dynamic filters.
Recent Trend
How AI describes Meilisearch3
Meilisearch : A developer-friendly, open-source search engine that supports multiple languages.
Which hosted search platforms have the easiest relevance ranking tuning for a product catalog use case — what's the learning curve like?
Typesense / Meilisearch : Known for being lighter weight and easier to self-host than Elasticsearch while still offering good performance for smaller-to-medium scale applications.
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?
...fields) without a full rebuild include Elasticsearch/OpenSearch (using dynamic mapping or index templates) and Typesense/Meilisearch (using schema-first or dynamic approaches) . [https://www.bytebase.com/blog/top-database-schema-change-tool-evolution/...
Which search platforms best support geo-search and faceted filtering combined with full-text relevance for a marketplace application?
Most cited sources8
25Algolia vs Typesense vs Meilisearch: Which Search Engine? | Meilisearch
meilisearch.com·Comparison
- 18
Elasticsearch vs Typesense: A definitive comparison [July 2025]
meilisearch.com·Comparison
- 13
Comparison to alternatives - Meilisearch Documentation
meilisearch.com·Comparison
- 12
Marketplace search engine: How to make one, top tools, & more
meilisearch.com·Blog Post
- 7
Typo tolerance vs fuzzy search: how Meilisearch handles misspellings - Meilisearch Documentation
meilisearch.com·Documentation
- 7
Multimodal Search
meilisearch.com·Product Page
Alternatives in Search & Vector Databases6
Meilisearch positions itself as the developer-first, open-source alternative to Algolia and Elasticsearch: a plug-and-play search engine written in Rust that delivers sub-50ms hybrid (full-text + semantic) search with zero mandatory configuration.
- Its key differentiators are ease of setup, transparent pricing (starting at $30/month managed or free self-hosted), an MIT-licensed open-source core, and a strong GitHub-driven community (56k+ stars).
- Against Algolia it competes on cost and openness; against Elasticsearch on simplicity and speed-to-production.
- It is expanding into vector storage and RAG retrieval to compete with pure-play vector databases.
Reviews
Praised
- Fast setup and onboarding (under 10 minutes to first search)
- Excellent out-of-the-box relevancy and typo tolerance
- Clear, comprehensive documentation
- Seamless framework integrations (Laravel, Rails, Symfony)
- Responsive and helpful support team
- Strong performance with large datasets
- Cost-effectiveness vs. Algolia
- Active open-source community
Criticized
- Admin dashboard lacks sophistication and index management depth
- Per-search cloud pricing expensive at high traffic volumes
- Recent pricing model changes disadvantage large-index, lower-query workloads
- Some advanced features (federated search, RAG) are Cloud-only
- Limited built-in suggestion/autocomplete features
Meilisearch earns strong praise from developers for its fast setup, clear documentation, and out-of-the-box search relevancy. G2 reviewers highlight seamless framework integrations (particularly Laravel and Rails), responsive customer support, and reliable performance even with large indexes. Common criticisms focus on the Cloud admin dashboard lacking sophistication, per-search pricing becoming costly at high traffic volumes, and the pricing model changes that disadvantage large-index/lower-query users. Overall sentiment is highly positive among developer-led teams, particularly those migrating from PostgreSQL or evaluating Algolia alternatives.
Pricing
Four tiers: (1) Open Source — free, self-hosted, MIT-licensed Community Edition; (2) Cloud usage-based — starting at $30/month with pre-set search and document limits, billed for overages, 14-day free trial, no credit card required; (3) Cloud resource-based — starting at $23/month, paying for dedicated CPU/RAM/storage, suited for high-traffic or vector workloads; (4) Enterprise — custom quote, includes enterprise features (sharding, S3 snapshots), self-hosting option, premier support, and premium SLA. Annual plans available at a discount. Standard cloud support hours: 8 AM–11 PM CET, Monday–Friday.
Limitations
- The admin dashboard is considered basic by reviewers; index management tooling lacks depth compared to dedicated dashboards.
- Cloud per-search pricing on usage-based plans can become expensive at high query volumes, though self-hosting remains an option.
- Several advanced features (federated search, multi-modal search, conversational search/RAG) are Cloud-only and not available in the self-hosted Community Edition.
- The Enterprise Edition (sharding, S3 snapshots) requires a commercial agreement and cannot be used in production under the open-source license alone.
- No publicly confirmed funding since October 2022.
Frequently asked questions
Topic coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
| Prompt | Gemini Search | Bing Copilot | Perplexity | ChatGPT | Google AI Mode | Grok |
|---|---|---|---|---|---|---|
Capability4/5 cited (80%) | ||||||
Which search platforms best support geo-search and faceted filtering combined with full-text relevance for a marketplace application? | Your brand and a competitor were cited | Neither your brand nor 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 vector databases handle filtered similarity search efficiently — which ones support nearest neighbor search scoped to a specific user's namespace? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | 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? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
Which search platforms support multimodal search combining text queries with image embeddings — what are the best options for this use case? | Your brand and a competitor were 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 | Your brand and a competitor were cited |
Which hosted vector databases scale best to billions of high-dimensional embeddings — what are the real limitations teams hit at that scale? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited |
Developer Experience4/5 cited (80%) | ||||||
Which search engines handle synonyms, typo tolerance, and stop words across multiple languages without duplicating index configuration? | Your brand was cited | A competitor was cited | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | A competitor was cited | Your brand and a competitor were cited |
Which search engines have the best dashboard and query explorer tools for non-engineers to understand why certain results rank higher? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | Your brand was cited |
Which search platforms offer the best developer experience for combining keyword search with semantic vector search in a single query? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | 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? | 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 | Your brand and a competitor were cited |
Which hosted search platforms have the easiest relevance ranking tuning for a product catalog use case — what's the learning curve like? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | A competitor was cited | Your brand and a competitor were cited |
Integrations & Ecosystem1/5 cited (20%) | ||||||
Which search platforms have native integrations with popular LLM orchestration frameworks for building RAG pipelines with minimal boilerplate? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
Which search platforms work best as the retrieval layer for an AI agent that needs to query across multiple data sources and indexes? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | 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 |
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 | 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 tools help keep a search index in sync with a primary relational database without building a custom ETL pipeline — what do teams typically use? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Performance & Reliability3/5 cited (60%) | ||||||
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 | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited |
Which vector databases use the best ANN algorithms for recall at scale — how do the implementations differ across the major platforms? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Your brand and a competitor were cited |
What are the best managed search services versus self-hosted options in terms of operational overhead and reliability at scale? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Your brand and a competitor were cited |
Which vector databases handle real-time index updates without degrading query performance during high write loads? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
Which hosted vector search services offer the best p99 query latency when searching 50 million vectors — what should I realistically expect? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Setup & First Run4/5 cited (80%) | ||||||
Which search platforms make it easiest to migrate from SQL LIKE-query search without taking the app offline during the transition? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand was cited | Your brand and a competitor were cited |
What are the best vector databases for a RAG application when you're just starting out with embeddings — which ones have the simplest setup path? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
What's the fastest way to add full-text search to a Next.js app without setting up a dedicated search cluster — which services are worth looking at? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited |
Which hosted search platforms deliver good out-of-the-box relevance with minimal tuning before results feel useful to end users? | Your brand and a competitor were 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 |
What are the best search engines for indexing an existing relational database without needing a full data pipeline from day one? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | A competitor was cited | Your brand and a competitor were cited |
Turn this matrix into daily prompt monitoring.
Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Meilisearch | 18.7% | 23.6% | 10.7% | 12.7% | 33.3% | #25.8 | +0.26 |
| 2 | Elastic | 13.3% | 11.1% | 4.0% | 1.3% | 28.7% | #20.9 | +0.28 |
| 3 | Algolia | 11.3% | 14.8% | 6.0% | 7.3% | 34.0% | #30.4 | +0.37 |
| 4 | Typesense | 11.3% | 14.8% | 7.3% | 0.0% | 28.7% | #33.4 | +0.33 |
| 5 | Qdrant | 9.3% | 10.5% | 4.0% | 2.0% | 47.3% | #43.5 | +0.23 |
| 6 | Weaviate | 8.7% | 6.8% | 0.7% | 5.3% | 46.0% | #33.2 | +0.22 |
| 7 | Pinecone | 8.0% | 7.1% | 0.7% | 3.3% | 49.3% | #45.8 | +0.31 |
| 8 | Zilliz | 6.7% | 6.0% | 0.7% | 4.0% | 16.0% | #35.1 | +0.26 |
| 9 | Vespa | 4.0% | 4.6% | 2.0% | 2.0% | 2.7% | #38.3 | -0.02 |
| 10 | Chroma | 1.3% | 0.6% | 0.7% | 0.0% | 12.7% | #45.0 | +0.25 |
| 11 | Trieve | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
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.