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
An intelligent search solution that makes it easy to find the information employees and customers want to know without migrating from one place to another and without wasting time with just a few clicks, Amazon Kendra connects relevant data sources through fully functional and customizable search while minimizing risks increasing workflow by obtaining detailed results.
Recent pricing model changes made Algolia considerably more expensive. I understand that companies change their models all the time, but my plan almost doubled in price overnight. They let me keep my legacy plan for as long as I wanted, but I had already outgrown it, so a small increase in demand caused big price spikes. It's still cheap for what it is though.
The documentation is generally good, but sometimes hard to navigate. I was trying to find examples of how to combine geo-queries with normal ones, and I couldn't find an example, but it wasn't actually hard to figure out.
Some of the advanced features can be hard to understand at first. This isn't really a con, as it just means Algolia is loaded with features, but I was a bit overwhelmed the first time I tried to customize an index.
Amazon Kendra is an intelligent search service that facilitates the way we obtain information by making it easier to obtain accurate documents without wasting time, it is a software that is designed to work in an optimized way, providing a business quality service with flexible features and innumerable benefits. , it is effective and fresh.
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
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 the engineering side, we could give our stakeholders access to Algolia to adjust the indices themselves, which would allow us to focus on other work.
Amazon Kendra offers an intelligent search experience, it is a tool that facilitates the way we work by providing secure and immediate results, saves time, increases productivity and brings progress. It is the best way to get accurate answers using natural language. It is a software that works in an extraordinary way, being easy to implement, safe and effective, the experience is unique.
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.
