For a web platform where fulltext search is needed with various similarity matching, stemming, attribute filters, autocomplete and so on, what would be the best choice?

GPT-5.4

Vote: ElasticSearch

ElasticSearch is the strongest default choice for this requirement set. It is built for full-text search and natively supports relevance tuning, fuzzy/similarity matching, stemming, analyzers, autocomplete, faceting, and rich attribute filtering at scale. Compared with MySQL full-text search, it is far more capable for search-specific needs. MySQL can handle basic text search, but it is not the best tool when you need advanced ranking, typo tolerance, linguistic analysis, and flexible query composition. Solr is also a serious contender and can absolutely do this well. But ElasticSearch is generally the more common pick for modern web platforms because of its ecosystem, API ergonomics, documentation, and broad adoption. Meilisearch is attractive for simplicity and fast setup, especially for smaller teams. However, for the broadest and most mature feature set across complex similarity matching and advanced search behavior, ElasticSearch is the safer best choice.

Grok 4.1 Fast

Vote: ElasticSearch

ElasticSearch is the gold standard for complex full-text search needs on web platforms. It natively supports advanced stemming via analyzers, fuzzy and similarity matching (e.g., BM25, Levenshtein), faceted attribute filters, and robust autocomplete with completion suggesters. Solr is capable but lags in ecosystem (no Kibana equivalent), scalability ease, and modern real-time features. MySQL full-text is rudimentary, lacking true stemming, advanced similarity, and autocomplete without extensions. Meilisearch is lightweight and fast for basics but falls short on deep customization for 'various' advanced matching and filters. ES powers giants like Netflix, LinkedIn for these exact use cases, offering unmatched flexibility, horizontal scaling, and plugin ecosystem without sacrificing performance.

DeepSeek V3.2

Vote: ElasticSearch

ElasticSearch is the most comprehensive solution for a web platform requiring advanced full-text search capabilities. It excels in similarity matching with multiple scoring models (BM25, custom similarities), robust stemming via configurable analyzers, and powerful attribute filtering through aggregations and facets. Autocomplete is natively supported via suggesters, including completion and term suggestions. Its distributed architecture ensures scalability, while its extensive ecosystem and documentation simplify integration and maintenance. While Solr offers similar Lucene-based features, ElasticSearch's modern tooling and wider adoption in web platforms make it the best choice. MySQL full-text search is too limited for advanced requirements, and Meilisearch, though user-friendly, lacks the maturity and depth of features needed for complex similarity matching and filtering.

Gemini 3.1 Pro

Vote: ElasticSearch

Elasticsearch is widely recognized as the industry standard for implementing advanced search features in modern web platforms. Built on top of Apache Lucene, it provides exceptional, scalable full-text search capabilities out of the box. It fully satisfies all the requirements mentioned in the prompt: it offers robust text analysis (including extensive language stemming), advanced similarity scoring algorithms (like BM25), complex attribute filtering via structured queries, and powerful completion suggesters (edge n-grams) for rapid autocomplete. While Solr shares the same Lucene backbone, Elasticsearch's JSON-based REST API, massive ecosystem, and superior developer experience make it the better fit for web platforms. MySQL's full-text search is far too basic for these advanced needs, and Meilisearch, though user-friendly, lacks the deep configurability and maturity required for complex similarity matching and extensive text analysis.

Claude Opus 4.6

Vote: ElasticSearch

ElasticSearch is the best choice for a web platform requiring fulltext search with similarity matching, stemming, attribute filters, and autocomplete. Here's why: **Comprehensive Feature Set:** ElasticSearch natively supports all the features mentioned — fuzzy matching for similarity, built-in stemming analyzers for multiple languages, powerful filtering via structured queries, and completion suggesters for autocomplete. No other option covers all of these requirements as thoroughly out of the box. **Scalability and Performance:** ElasticSearch is built on Apache Lucene and designed for distributed, horizontally scalable search. For a web platform that may need to handle growing data and query volumes, this is critical. MySQL full-text search (C) simply cannot compete at scale for search-specific workloads. **Ecosystem and Community:** ElasticSearch has the largest ecosystem among dedicated search engines, with extensive documentation, client libraries for every major language, and a massive community. This translates to faster development, easier debugging, and better long-term support compared to alternatives. **Why not the others?** Solr (B) is also built on Lucene and is capable, but ElasticSearch has surpassed it in adoption, developer experience, and ease of deployment — particularly for web platforms. MySQL full-text search (C) is rudimentary and lacks similarity matching, proper stemming configuration, and autocomplete capabilities. Meilisearch (D) is excellent for simple, typo-tolerant search and quick setup, but it lacks the depth of query DSL, advanced similarity tuning, and enterprise-grade scalability that ElasticSearch provides for a full-featured web platform. For a platform that needs *all* of these search capabilities — not just basic search — ElasticSearch is the most complete and battle-tested solution.

Final Summary

The AI Roundtable reached a swift and rare unanimous verdict, with all five models identifying ElasticSearch as the definitive choice for advanced web search. From GPT-5.4's initial assessment of its feature depth to Claude Opus 4.6's exhaustive breakdown of its scalability, the group dismissed the alternatives as either too rudimentary, like MySQL, or too niche, like Meilisearch.

All 5 models agreed on "ElasticSearch" after discussion

Strongest Arguments

• ElasticSearch: ElasticSearch is the only solution that natively provides a comprehensive, horizontally scalable ecosystem covering everything from fuzzy matching and linguistic stemming to complex attribute aggregations out of the box.