See the difference
Layers vs Algolia
Better E-commerce focused results for a better price.
Compare
platform
| Feature | Layers | Algolia |
|---|---|---|
| Native ingestion of Shopify taxonomy | Yes | No |
| Support for Shopify combined listings and bundles | Yes | No |
| Native support for Shopify Markets: multi-region catalogs, currencies, languages | Yes | No |
| Multi-lingual catalog & storefront: translations of product content and search UI | Yes | No |
| Out-of-the-box storefront components/apps optimized for Shopify theme architecture | Yes | No |
search
| Feature | Layers | Algolia |
|---|---|---|
| No synonym lists needed, semantic embeddings understand meaning automatically | Yes | No |
| Multilingual model supports 100+ languages natively in one index | Yes | No |
| Detects user intent (e.g., "red shoes under $100") and auto-applies filters and context | Yes | No |
| Keyword search with prefix match, fuzzy matching, typo tolerance | Yes | Yes |
| Behavior-based learning at query level (clicks, adds, purchases) | Yes | No |
| Zero ongoing tuning - self-optimizes from behavior data | Yes | No |
| Semantic search/intent recognition (natural language queries, context understanding) | Yes | No |
| Acronym & abbreviation expansion (e.g., "4K TV" -> "Ultra HD television") | Yes | No |
| Multilingual query processing and catalog mapping | Yes | No |
| Autocomplete / type-ahead suggestions with personalization | Yes | Yes |
| Camera/image-based search | Yes | No |
| Price detection in query and auto-filtering | Yes | No |
| Dynamic facet/filter generation based on search context | Yes | No |
| Faceting & filtering beyond Shopify standard filters | Yes | Yes |
| Query-result re-ranking via ML/AI | Yes | No |
| Handling of incomplete or ambiguous queries | Yes | No |
merchandising
| Feature | Layers | Algolia |
|---|---|---|
| Supports multiple custom orders per collection (not limited to one) | Yes | No |
| Rules engine for boosting/demoting products based on attributes, events, and performance | Yes | Yes |
| Drag-and-drop merchandising UI for manual ordering (pinning, sequencing) | Yes | Yes |
| Product sequences/lookbooks | Yes | No |
| Ability to define custom metric formulas for ranking | Yes | No |
| Contextual boosting by segment | Yes | Yes |
| Scheduled merchandising: time-based rules | Yes | Yes |
| A/B testing of sort configurations | Yes | Yes |
| Variation of sort logic by Shopify Market or contextual data | Yes | No |
| Visual preview of search or collection page results for merchandising review | Yes | No |
| Integration of business performance metrics into ranking | Yes | No |
| Support for pinning or excluding products dynamically | Yes | Yes |
company
| Feature | Layers | Algolia |
|---|---|---|
| 1-week total onboarding time | Yes | No |
| All onboarding & training included | Yes | No |
| Training, documentation, and support resources are readily available | Yes | No |
| 1:1 migration catalog sync from Shopify | Yes | No |
| No additional cost for refinement searches | Yes | No |
”Layers doesn't just merchandise, it also finds the strategy and provides unparalleled control, all while saving me hours I didn't know I was losing!
~
Brittany Csik, eCommerce Manager, Negative Underwear
”Layers was the first time we were able to create the kind of sort orders we were used to having in Salesforce. We saw a pretty immediate impact on conversion rate.
~
David Cost, VP of eCommerce, Rainbow Shops
”Layers doesn't just merchandise, it also finds the strategy and provides unparalleled control, all while saving me hours I didn't know I was losing!
~
Brittany Csik, eCommerce Manager, Negative Underwear
”Layers was the first time we were able to create the kind of sort orders we were used to having in Salesforce. We saw a pretty immediate impact on conversion rate.
~
David Cost, VP of eCommerce, Rainbow Shops
The difference is clear
Save up to $200,000 annually
Switch to Layers and save ~50% compared to other enterprise platforms, without losing a dollar in revenue.
Cost savings based on real-world onboarding data. Results vary by volume and features utilized.
