Algolia offers AI-powered solutions to improve online search and discovery experiences, with tools for business teams and APIs for developers that help to improve user engagement and conversions across websites, apps, and e-commerce platforms.
$0
per month Up to 10,000 search requests + 1 Million records
Glean
Score 9.7 out of 10
N/A
Glean is an AI-powered workplace search tool that supports search across all of a company's apps, centralizing company knowledge and helping employees to more quickly find what they need, with 100+ connectors.
$12
per month
Pricing
Algolia
Glean
Editions & Modules
Build
$0
per month Up to 10,000 search requests + 1 Million records
Grow
$0.50
per month per 1,000 search requests
Algolia Recommend
$0.60
per month per 1,000 Recommend requests
Premium
Custom
per month Customized pricing
Elevate
custom
per year
Glean for Individuals - Monthly
$12
per month
Glean for Individuals - 1 Year
$129
one-time fee
Glean for Individuals - 2 Year
$245
one-time fee
Glean for Individuals - 3 Year
$348
one-time fee
Glean for Individuals - 4 Year
$439
one-time fee
Offerings
Pricing Offerings
Algolia
Glean
Free Trial
Yes
Yes
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
Optional
No setup fee
Additional Details
Pay as you go, scale instantly, or upgrade anytime for advanced features and capabilities.
There are many open source search products available. Prior to Algolia, we used an in-house search system adopted from an open-source system. While this was nice in that we could modify it in any way we wanted, it also required dedicated engineering and setting up many …
Mostly for instant search capability, then because SFCC option can be easier to use, but front end capabilities where not nice at the time we implemented it. Elasticsearch is more similar with database and index management, but was more expensive at the time + Algolia cartridge …
Algolia easier to implement, whereas Amazon Cloudsearch is more technical to setup. Algolia is faster and has less latency than Amazon Cloudsearch, Algolia seemed to have more features than Amazon Cloudsearch
Offloading search logic to Algolia saved dev time and allowed our engineers to focus on higher-impact features instead of maintaining complex queries or custom search infra.
Before switching to Algolia, we were using SearchSpring. In our experience, the support was slow, the tech felt outdated, and things just didn’t work consistently. The widgets were clunky and limited, and overall it didn’t give us the flexibility or performance we needed. …
Algolia works out of the box, you don't need to setup a lot to see how it works for you. Its also pretty flexible and customizable if you need to. With elasticsearch you have to think about deployment strategies, where to host it, how to send data for it and build custom …
Even though CloudSearch is fully integrated into the AWS ecosystem, it is ideal for companies already using AWS services.. Algolia is much faster and focused on high-performance search experiences, with an easier-to-use API interface and better customization capabilities. …
We initially attempted an in-house search solution, which, though tailored to our preferences, demanded significant resources for upkeep. While our internally built search system allowed a deep understanding of rankings, Algolia emerged as a more efficient alternative. …
Algolia prioritizes simplicity and quick setup, excelling in user-friendly search experiences. Elasticsearch offers versatility and complexity, suitable for intricate scenarios, while Amazon CloudSearch provides essential features and seamless integration within the AWS …
While AWS's offering is a typically cheaper solution, it requires a lot of work to gain any of the core features of Algolia. The cost of dev time and long-term maintenance would be more than the costs incurred with Algolia, which is why it made the most sense financially. On …
There were few alternatives when we started by using Algolia and it was the better rated in terms of price & performance. Now there are more alternatives, but we keep algolia as is isolated from the rest of our stack so that we can have better performance & control.
Algolia has focused solely on site search for 10+ years, while Google has previously abandoned similar products. Algolia offers end-to-end AI processing, personalization, and automatic query categorization at scale. Deployment and usability is easier with Algolia compared to …
Algolia provides the best user experience, ease of integration and implementation, extremely high performance on large catalogs. The features offered are powerful and complete, with machine learning systems to improve result personalization. The service management can be done …
Algolia got us up and running faster and more easily than if we'd managed elastic search and it's configuration by ourselves. Upfront and ongoing costs and complications/ custom implementations were removed from the equation by choosing Algolia out of the gate.
We have choose Algolia, because is a more robust and scalable solution from a consolidated company in the market. A good differential is the time requrest to update information.
SLI- We used SLI for about 8 years prior to Algolia and Aloglia is far more sophisticated in terms of Typo Tolerance, Synonyms and AI capabilities. It also allows for much easier global rule setting so that we can easily promote our Proprietary Brands.
Amazon is great for huge companies that have a team to support this feature in particular but if you are a small to medium business, Algolia is more manageable.
Well-suited Scenarios: - Fast Car Browsing with Filters: Algolia shines when a user is browsing thousands of cars using filters like price, mileage, year, brand, and location. It returns instant, ranked results even with complex combinations. - Mobile Search with Typos: When users type “Camary” or “Toyta” on mobile, Algolia still returns accurate matches thanks to its typo tolerance and synonyms—improving UX and reducing zero-result queries. - Featured Car Prioritization: We can use custom ranking to boost certain listings (e.g., newly added, better margins, location-specific promos) without affecting the user’s search experience.
Less Appropriate Scenarios: - Complex Rule-Based Inventory Logic: If we want to show different results based on time of day, inventory pressure, or dynamic business rules, Algolia falls short. This logic needs to be applied before indexing. - Global Search Across Entities: Searching across cars, articles, FAQs, and service centers in one go requires heavy frontend orchestration due to lack of native multi-index blending. - Real-Time Updates at Scale: For highly dynamic data (e.g., car availability or pricing updates every few minutes), frequent indexing can be costly and requires batching, making it less real-time than needed
I think Glean can help any organization. It creates an internal search engine that is specific to your organization, and brings everything into one place. This allows you to access content you may not have otherwise found, especially because other systems do not have effective search capabilities
Algolia is brain-dead simple to set up. I've implemented search with Algolia in a dozen different ways now, and it never took me longer than a few minutes to get the functionality I want. With Algolia, the only challenge is designing your search UI -- if you don't want to use their baked in UI solutions.
Results come back incredibly fast. I'm not sure how Algolia does it, but every keystroke I make in a search field returns new results instantly. It's hard to believe that I'm searching large datasets on a remote server when it works so fast.
Very little customization is needed for 99% of use-cases. Algolia's out of the box setup works great, and it takes no prior knowledge to set up.
Algolia can be a bit complex -- for smaller companies or companies without many tech resources, it may be difficult to implement and use without the help of a third party
Manually manipulating search results (for specific queries having listings show up first) is a bit difficult to do without custom developing that functionality
Algolia is a great tool, we didn't have to build a custom search platform (using Elasticsearch for example) for a while. It has great flexibility and the set of libraries and SDKs make using it really easy. However, there are two major blockers for our future: - Their pricing it's still a bit hard to predict (when you are used to other kind of metrics for usage) so I really recommend to take a look at it first. - Integrating it within a CI/CD pipeline is difficult to replicate staging/development environments based on Production.
Algolia has a good interface and they have done some improvements. However, some non technical users have a challenging time in the use for the first days of learning. But once the main aspects are learned is a straight forward operation
Performance is always a major concern when integrating services with our client's websites. Our tests and real-world experience show that Algolia is highly performant. We have more extremely satisfied with the speed of both the search service APIs and the backend administrative and analytic interface.
It’s non existent. No tech support and no customer service… my application was blocked and is currently inactive causing huge business disruption, and I’m still waiting days later for a response to an issue which could be resolved very very quickly if only they would respond. Very poor from a company of that size
There are many open source search products available. Prior to Algolia, we used an in-house search system adopted from an open-source system. While this was nice in that we could modify it in any way we wanted, it also required dedicated engineering and setting up many analytics tools and monitoring systems to ensure it stayed performant/could adapt to our ever evolving needs. Algolia takes a load off our plate and frees our engineers to work on bigger problems vs minute search changes or monitoring. It also empowers our product teams to directly use the AI to make basic changes and see analytics in one easy place. We chose Algolia to increase development velocity and reduce the hidden costs of maintaining and operating open-source code/search tools.
Overall is a scalable tool as the environment and the backend functions are the same and many things are done directly on the tool so without the need of further specific developments. However some things could be improved such as documentation for integration that could help in doing whitelabel solutions
Users who had abandoned our product (attributing slow search speeds as the reason) returned to us thanks to Algolia
We used Algolia as our product's backbone to relaunch it, making it the center of all search on our platform which paid off massively.
Considering we relaunched our product, with Aloglia functioning as its engine, we got a lot of press coverage for our highly improved search speeds.
One negative would be how important it is to read the fine print when it comes to the technical documentation. As pricing is done on the basis of records and indexes, it is not made apparent that there is a size limit for your records or how quickly these numbers can increase for any particular use case. Be very wary of these as they can quite easily exceed your allotted budget for the product.
