Search Platform Selector

Search crosses business models. A D2C brand on Shopify and a B2B distributor running SAP can both need Algolia. Adjust the filters to your catalogue size, search volume, team capability, and AI requirements - and see what actually fits.

28 platforms match your situation

  • Plug and play
  • Shopify apparel and fashion

Boost Commerce

Shopify filter and search tool focused on collection filtering and smart search. Strong for apparel and fashion stores with complex variant filtering.

  • Plug and play

Doofinder

Hosted AI search you install in a day. Designed for SMB ecommerce teams who need better search without touching code.

  • Plug and play

ExpertRec

Affordable hosted search for SMB and content sites. Transparent pricing without the usage-based cost anxiety of Algolia.

  • Plug and play

Searchanise

Shopify-first search plugin with a fast setup and a feature set built for stores that have outgrown the default search.

  • Mid-market

Clerk.io

Search, recommendations, and email personalisation in one platform. European mid-market tool with strong product recommendation capability alongside search.

  • Mid-market
  • German-speaking European ecommerce

Findologic

European search and navigation platform with a strong focus on merchandising control and zero-result handling. Particularly well-established in German-speaking markets.

  • Mid-market

Hawksearch

Mid-market search and merchandising with strong B2B catalogue support. One of the few managed SaaS search platforms with genuine part number and technical attribute search.

  • Mid-market

Klevu / Athos Commerce

AI search and discovery platform for retail ecommerce. Originally Shopify and Magento focused; now part of the Athos Commerce group alongside Searchspring and Intelligent Reach.

  • Mid-market

Luigi's Box

European AI search and product discovery platform. Strong analytics and A/B testing. Accessible pricing relative to comparable mid-market tools.

  • Enterprise SaaS

Algolia

The developer-first search API. Fast, flexible, and the most widely integrated search infrastructure in the market. Genuinely excellent until the bill arrives.

  • Enterprise SaaS

Bloomreach

The enterprise discovery platform that combines search, recommendations, content, and marketing automation. Strong if you need all of those. Expensive if you only need search.

  • Enterprise SaaS

Constructor

Enterprise ecommerce search and product discovery platform built specifically for conversion. The only platform in this tier that optimises for revenue, not just relevance.

  • Enterprise SaaS

Coveo

Enterprise AI search across multiple use cases - ecommerce, service portals, intranet, and knowledge management. The right choice if you need search that crosses content types and systems.

  • Enterprise SaaS
  • declining

Fredhopper (Crownpeak)

Enterprise merchandising and search platform with deep catalogue control. Long history in European fashion and home retail. Acquired by Crownpeak in 2022. Still in production use at large European fashion retailers but less AI-native than Constructor or Bloomreach.

  • Enterprise SaaS

Netcore Unbxd

Mid-to-large retail search built into the Netcore Cloud stack. Worth evaluating if you are already buying Netcore for email or engagement. As a standalone search purchase, you are competing against better-known platforms.

  • Cloud infra

AWS OpenSearch Service

AWS-managed version of the Elasticsearch fork. The right choice for teams already invested in AWS who need managed search infrastructure without Elastic's licensing constraints.

  • Cloud infra

Azure AI Search

Microsoft's managed search infrastructure. Native integration with the Azure and Microsoft 365 ecosystem. The right path for organisations already on Azure.

  • Cloud infra

Elastic Cloud (Elasticsearch)

The original enterprise search infrastructure. Unmatched depth for log analytics and multi-use search. Complex to operate at scale and not a plug-and-play solution.

  • Cloud infra

Google AI Commerce Search

Google's managed retail search product, now called AI Commerce Search (part of Gemini Enterprise for Customer Experience). Pay-per-query pricing built on Google's AI infrastructure. The naming has changed repeatedly - verify you are looking at the right product before evaluating.

  • Open source
  • declining

Apache Solr

The original enterprise open-source search engine. Java-based, deeply capable, and showing its age. Still in production at large organisations but rarely the first choice for new builds.

  • Open source

Elasticsearch (self-managed)

Self-hosted Elasticsearch on your own infrastructure. Free licence (with limitations since 2021). Maximum flexibility, maximum operational responsibility.

  • Open source

Meilisearch

The simplest serious open-source search engine. Fastest time-to-working-search for developers who want a clean API and predictable pricing without Elasticsearch's complexity.

  • Open source

OpenSearch (self-managed)

AWS's open-source fork of Elasticsearch. Same capability, true Apache 2.0 licence. The correct choice when Elastic's licensing is a concern and you want to self-host.

  • Open source

Typesense

Modern open-source search engine built for developer experience and speed. The right Algolia alternative for teams that want control without the complexity of Elasticsearch.

  • AI / vector
  • Ecommerce visual and multimodal search

Marqo

Multimodal vector search - text and image. Open-source with a managed cloud option. Purpose-built for ecommerce visual search and hybrid text-image retrieval.

  • AI / vector

Pinecone

Managed vector database. No self-hosting, no infrastructure management. Pay-per-use cloud service for semantic search and RAG applications.

  • AI / vector

Qdrant

Open-source vector database built for high-performance semantic search. The right choice when you are building semantic search or RAG applications and need to manage your own embeddings.

  • AI / vector

Weaviate

Open-source vector database with built-in vectoriser integration. More opinionated than Qdrant about how you bring embedding models in, which reduces some of the build complexity.