How does Sajari differ to other search solutions like Algolia, Swiftype and Elastic?

Some core features are shared across a lot of search solutions, like search suggestions and synonyms, but there are many differences between Sajari and other solutions. This originates from the different goals we set out to achieve when we built the Sajari engine.

Some unique Sajari features include:

  • Custom match score creation, on-the-fly. With Sajari you can use a wide variety of your business data along with keywords in your relevance scoring. For instance, in ecommerce search, you can take into account your stock levels, profit margins and conversion rate when ranking your search results, or in content driven sites you can use data like traffic and social shares to adjust results. With many other solutions you can use only one of these factors to order results, or use only one at a time, but Sajari allows you to blend factors in different weightings to 'score' your results. Most importantly, as scoring is done on-the-fly at query time, ranking factors can be easily adjusted without rebuilding your index.
  • Dynamic query boosting. Run multiple algorithms (or 'pipelines' as we call them) over the same data set at speed e.g. one algorithm can be used for searching your site, another can produce dynamic content blocks targeted at different customer segments. This principle can be applied in any search or matching context, not just site search. Sajari is designed to allow multiple algorithms to be run on your data without affecting performance and without caching.
  • Use records as queries. Queries don't have to be limited to keywords or phrases. You can use an entire document, user profile, or product as a query to search for, or match to, other similar items.
  • Real-time reinforcement machine learning. Over and above tuning your search algorithm, we employ reinforcement machine learning by default for all customers. Within two to four weeks our machine learning system will develop a clear understanding about which pages/records are most relevant to each of your users' queries. Further, you can see how the system is being trained in the Learning section of your Console.
  • Matching is as easy as searching. We see matching and searching as two similar applications of our underlying technology. If you have rich data it can be easily searched and/or matched. e.g. a dating application can let a user search based on parameters they have selected (gender, location, interests), or it can use the same information (gender, location, interests) to create a match with other members of the app, or you can blend both approaches.
  • Speed. We've spent a lot of time on making our engine as fast and efficient as possible. When bench-marked against Elastic search we've recorded about 10x faster indexing and about 100x faster searching.

We're always happy to chat about our features. Drop us a line if you have any questions at

Successful organizations use Sajari