Algolia is a popular hosted search engine known for its speed and ease of use. But it has its critics, too, particularly around the complexity of managing rules and its high indexing costs. In this article, we'll walk through why companies choose Algolia and share out some newer search alternatives.
Algolia is a SaaS site search engine built from the ground up. Originally, Algolia was developed for mobile search use cases, but has since been extended to more traditional search projects.
While Algolia enjoys popularity due to its ease of use and fast retrieval speed, it has its critics too, particularly around complexity for managing custom rules and pricing. For example, anytime Algolia needs to re-index your content — such as for A/B testing — it can double or quadruple your usage, which can quickly exceed monthly quotas.
Algolia has flourished as a new type of search engine relying on a pre-sorted Radix Trie index. By choosing the ranking (sorting) logic upfront and storing the information in the ideal sort order, the index is very fast. This has some downsides but it is incredibly cheap and very simple for people to understand.
Under the hood this approach operates much like Microsoft Excel where you pick a series of tie-breaking sort choices and the index is created with the data stored in this order via an NGINX plugin. This is very fast and works amazingly well to get to good results.
The downside to this approach is the same as its strength: simplicity. It does exactly what the sort says it will do, and nothing more. This is fine for 80% of cases, but when that last 20% is worth millions of dollars the strength becomes a weakness.
The sorting operation is defined by the person who designed it and doesn’t offer any additional intelligent sorting. Yes you can run A/B tests, but aside from that it is only as good as the rules and sort logic that someone wrote. For search this is typically a bad idea, as one person’s opinion is notoriously bad at representing a broad group of customers.
Recently, Algolia acquired Morphl, a machine learning platform, to bolster it’s AI capabilities. The challenge most search platforms have when they layer machine learning on top of their query engines is keeping their search engine performant. Traditionally you can have intelligent AI-driven search that is either slow and/or very expensive to run, or you can have fast instant search based on human written rules. This is the tradeoff that many search projects have made.
While Algolia is a good general purpose search engine, some customers are struggling with its method of ranking and their indexing costs.
We’ve gathered together seven of the best alternatives to Algolia. We’ve divided them into two buckets:
Sajari is a SaaS site search platform built from the ground up that offers tremendous flexibility and ease of configuration built on top of a cloud-native architecture for elastic scale.
While Sajari is also a new search engine, it approaches search very differently from Algolia and other legacy search engines. Sajari treats search more like a database, which offers some advantages in near real-time read/write speed and data synchronization while avoiding costly re-indexing fees.
It also has built-in machine learning — and more specifically, reinforcement learning — for continuous improvement of search performance. Unlike traditional search engines that have layered AI on top of their search algorithms, Sajari has included reinforcement learning as part of its core ranking algorithm. (For Algolia, layering AI on top is slightly problematic as the simplicity of the tie-breaking sort logic is totally nullified)
Additionally, Sajari has taken a different approach to configuration with a feature called pipelines. Pipelines define a series of steps which are executed sequentially when indexing a record (record pipeline) or performing a query (query pipeline) to generate more relevant results.
With pipelines, you can configure the search algorithm to improve search relevance, set rules for how to prioritize certain results, or even A/B test different algorithms to determine which one provides the best search experience. The main advantage here is that AB tests are run at query time, so no re-indexing is required, and thus there aren’t any hidden costs against quota.
Case Study: Switching from Algolia to Sajari at Catch.com.au.
Sajari features include:
Best use cases:
Acquired by Elasticsearch in 2017, Swiftype is a simple, but powerful SaaS search solution. Swiftype was built on top of Elasticsearch for indexing and storing content. (Elasticsearch itself is built on top of Lucene which has certain limitations particularly as search indexes change.)
Unlike Elasticsearch which is built for engineering teams to create full-text search and log analytics solutions, Swiftype has a very good end-to-end site search user experience. Swiftype is a drop-in search solution. It can index a website either through a crawler or API, offers out-of-the-box support for cross domain indexing, spell checking, instant search, search overlay, autocomplete, and more.
Some reviewers have noted a lack of good PDF and document indexing options and have mentioned concerns around high pricing and hitting quota limits sooner than expected.
Swiftype features include:
Best use cases:
Coveo has built its own enterprise search technology that allows people to build secure, enterprise search applications and knowledge bases. With their tight coupling to SiteCore, Salesforce, ServiceNow, and other B2B enterprise applications and SQL and NoSQL databases, Coveo offers fast search across datastores for internal KBs and other enterprise use cases.
Coveo is not as general-purpose a search platform as Sajari or Swiftype. It’s a platform to ingest and transform different data types into searchable, accessible content with Coveo’s proprietary search, machine learning, and recommendations engines built on top. Reviewers have mentioned that initial indexing can be slow, that updates are equally slow and cumbersome, and the user experience and UI is average, but once your content is integrated into Coveo it can be a very powerful tool.
More recently, the company has acquired AI and e-commerce technology to extend its footprint into e-commerce use cases.
Coveo features include:
Best use cases:
Lucidworks is an enterprise SaaS custom search solution built on top of Apache Solr, an open source search engine that’s powering thousands of enterprise search solutions. Effectively, customers get the power of Solr with a custom UI, APIs, and modules from Lucidworks.
Basic search features such as autocomplete and spell checking (what Lucidworks calls “query rewrites”) are premium features available for higher tier customers. If you’re looking for a professional services partner to help build a custom, powerful search solution, Lucidworks is worth a look.
Lucidworks features include:
Best use cases:
Headquartered in Paris, France, MeiliSearch is a young open source project that calls itself the “next generation search API.” MeiliSearch is a very early stage company that currently offers self-hosting only — in other words, you will need to download and install the free open source search engine in your own preferred hosting environment.
MeiliSearch offers an identical approach to Algolia with its search engine and search algorithms. It provides similar features and offers similar search relevance and speed. The biggest difference is that MeiliSearch is fully open sourced and written in Rust.
MeiliSearch is a new platform and reviews are mixed with indexing being slow but results get returned very quickly. With more than 11,000 stars on GitHub, MeiliSearch shows a great deal or promise as it matures into a full-scale search solution.
MeiliSearch features include:
Best use cases:
Typesense is another DIY open source Algolia lookalike that provides extremely fast search results. First released in 2017, Typesense has bloomed into a full-featured search engine. The company also offers a cloud hosting option with optional highly available configurations.
Like MeiliSearch, Typesense is written in Rust which is great for processing large amounts of data and CPU-intensive search. It has been built to be lightweight by taking up a minimal in-memory footprint. The project offers a clean API and smart defaults such as typo tolerance and autocomplete to make standing up a new search project fast and easy.
Typesense features include:
Best use cases:
The major cloud providers now offer many alternatives to Algolia including Microsoft’s Azure Cognitive Search, Amazon Cloudsearch, and Google Cloud Search. If you go the cloud provider route, you’re going to select the one your company is already working with.
Cloud providers offer both private and public hosted search solutions. If your app is hosted in one of these providers, then it might be worth considering them for your search service as well. Co-locating your search service with your app makes a lot of sense for reducing latency.
The pros and cons of each cloud service provider and software vary a lot. But they have some similarities:
Unlike Algolia and several of the alternatives mentioned above, cloud search solutions are not drop-in replacements. They require considerable configuration and are typically only managed by engineering teams. The reason we’re including them on this list is because they can offer tremendous performance when co-located with your site.
We hope this article provides you with some good ideas for what solution to select for your use case. For more ideas, have a look at our Site Search Buyer’s Guide or blog on Best Practices for Site Search.
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