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 10k search requests + 100k records
Elasticsearch
Score 8.6 out of 10
N/A
Elasticsearch is an enterprise search tool from Elastic in Mountain View, California.
$16
per month
Pricing
Algolia
Elasticsearch
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
Standard
$16.00
per month
Gold
$19.00
per month
Platinum
$22.00
per month
Enterprise
Contact Sales
Offerings
Pricing Offerings
Algolia
Elasticsearch
Free Trial
Yes
No
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
Yes
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.
By far Elasticsearch is the prime competitor that comes into the picture when thinking about Algolia. Where Algolia surpasses Elasticsearch by miles is in performance. Algolia is search on steroids. However, Elasticsearch supersedes Algolia in terms of flexibility and cost. Elas…
Elasticsearch is its direct competitor, but I would say that it is more focused on performance and less focused on aesthetics and recommendations. If you are thinking of getting Algolia, I would recommend that you also compare it at least to Elasticsearch. Either one is a good …
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 …
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 …
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 …
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 …
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.
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 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.
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.
At the time we did a TD on a number of different solutions, and Algolia was selected for its features and functionality. Why we couldn’t account for at the time was the lack of customer service and tech support, or problems that would regularly and repeatedly occur within their …
Algolia works well in tandem with Magento and provides a large number of tools and features that provide greater control and adaptability as compared to other solutions we reviewed. Algolia has demonstrated its commitment to continual innovation, providing access to next-gen …
Algolia does not require your own setup so we could get going fast. Algolia is known for being fast and highly available. It requires less domain knowledge. It is a lot more expensive though. Another plus is that is it very easy to sync data to it. You can backfill millions of …
Algolia at first seemed and proved to be the fastest compared to the other search engines. It is very easy to implement. Also, it had a 24x7 support which proved to be very useful. It is also useful for all types of clients weather it be organizations or individuals. It can …
Algolia was by far and away the easiest of the three to implement. PostgreSQL has many search modules that can be used on top of your usual database, however, none are particularly efficient and can quickly become overwhelmed at scale. Equally, they do not handle business …
I'd definitely recommend Algolia for a business operating in the ecommerce space and looking to integrate search quickly. Using the tool is easy to set up and works well. Out of the box you can have search, autocomplete, and recommendations that are all integrated. If you integrate directly, Algolia works well for also using their internal A/B test system. Areas where Algolia can be harder to use are in user-generated marketplaces. On a site where you control / own / produce all your inventory of results, you are solely optimizing for revenue/conversion. When you have a platform where there are different creators for each item, you need to balance revenue optimization with trying to support the business goals of the sellers on your marketplace platform. Algolia offers less tooling there which can be difficult and will require you build additional tooling/monitoring for that. Given that need, you likely cannot use all of Algolia's UI tools like A/B testing.
Elasticsearch is a really scalable solution that can fit a lot of needs, but the bigger and/or those needs become, the more understanding & infrastructure you will need for your instance to be running correctly. Elasticsearch is not problem-free - you can get yourself in a lot of trouble if you are not following good practices and/or if are not managing the cluster correctly. Licensing is a big decision point here as Elasticsearch is a middleware component - be sure to read the licensing agreement of the version you want to try before you commit to it. Same goes for long-term support - be sure to keep yourself in the know for this aspect you may end up stuck with an unpatched version for years.
Users get instant feedback as they type, even with complex filters like brand, model, price range, and financing eligibility. This speed significantly improves engagement and reduces bounce.
A user searching for “Camry 2020” or even “Camary 20” still sees relevant Toyota Camry listings from 2020. This reduces friction, especially on mobile where spelling errors are common.
Algolia handles multi-faceted filters efficiently. For example, a user can filter by location, transmission type, color, or inspection status without any lag.
We fine-tune the ranking of search results based on what matters to our business—like prioritizing cars with higher margins or better availability in key cities.
We can experiment with different ranking formulas or UI variations to improve KPIs like lead conversion or time-to-first-interaction.
As I mentioned before, Elasticsearch's flexible data model is unparalleled. You can nest fields as deeply as you want, have as many fields as you want, but whatever you want in those fields (as long as it stays the same type), and all of it will be searchable and you don't need to even declare a schema beforehand!
Elastic, the company behind Elasticsearch, is super strong financially and they have a great team of devs and product managers working on Elasticsearch. When I first started using ES 3 years ago, I was 90% impressed and knew it would be a good fit. 3 years later, I am 200% impressed and blown away by how far it has come and gotten even better. If there are features that are missing or you don't think it's fast enough right now, I bet it'll be suitable next year because the team behind it is so dang fast!
Elasticsearch is really, really stable. It takes a lot to bring down a cluster. It's self-balancing algorithms, leader-election system, self-healing properties are state of the art. We've never seen network failures or hard-drive corruption or CPU bugs bring down an ES cluster.
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.
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
To get started with Elasticsearch, you don't have to get very involved in configuring what really is an incredibly complex system under the hood. You simply install the package, run the service, and you're immediately able to begin using it. You don't need to learn any sort of query language to add data to Elasticsearch or perform some basic searching. If you're used to any sort of RESTful API, getting started with Elasticsearch is a breeze. If you've never interacted with a RESTful API directly, the journey may be a little more bumpy. Overall, though, it's incredibly simple to use for what it's doing under the covers.
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
We've only used it as an opensource tooling. We did not purchase any additional support to roll out the elasticsearch software. When rolling out the application on our platform we've used the documentation which was available online. During our test phases we did not experience any bugs or issues so we did not rely on support at all.
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.
As far as we are concerned, Elasticsearch is the gold standard and we have barely evaluated any alternatives. You could consider it an alternative to a relational or NoSQL database, so in cases where those suffice, you don't need Elasticsearch. But if you want powerful text-based search capabilities across large data sets, Elasticsearch is the way to go.
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.
We have had great luck with implementing Elasticsearch for our search and analytics use cases.
While the operational burden is not minimal, operating a cluster of servers, using a custom query language, writing Elasticsearch-specific bulk insert code, the performance and the relative operational ease of Elasticsearch are unparalleled.
We've easily saved hundreds of thousands of dollars implementing Elasticsearch vs. RDBMS vs. other no-SQL solutions for our specific set of problems.
