I used Algolia to implement search functionalities. When I had too many products and wanted to improve search functionality, I built custom webhooks in the backend using ExpressJS and NodeJS. The product information was passed to Algolia, which I used only for search functionalities.

In my site, conversion rate or customer satisfaction progressed 100%. I rate it 100% for customer satisfaction because when users search for a product, they don't need to press the enter button.

If they type ABC, the ABC type of product is automatically filtered and shown at the bottom of the screen. Algolia gives us that functionality, immediately showing filtering products without any bugs. That's why, in my site, it's 100% customer satisfaction. And from my side, it's okay.

In my experience, I used Algolia because it made searching and filtering products much easier. Algolia provides some cool functionalities like filtering, indexing, and searching.

If I implemented such functionality without Algolia, I would have to create the filtering functionality customly. But with Algolia, it's much easier because they already have these features built-in.

I implement their inbuilt hooks or components, like InstantSearch, which is a React UI component provided by Algolia. It helps me with searching and filtering the products. I used Algolia for basic purposes because I haven't worked on any advanced projects with it yet.

The documentation is not beginner-friendly. When I first used it, I sometimes couldn't understand its components and how they worked.

At one point, the product was automatically deleted from the backend for unknown reasons. But, I haven't had any other problems so far.

I started working with Algolia last year in December at my ex-company. I worked as a front-end technical lead and used different technologies, including Algolia. However, I only used the basic features, not the advanced ones, because it was already set up.

I didn't see any bugs. It's developer-friendly and user-friendly. I have not faced any bugs so far.

When I worked at my previous company, they had a limited developer team of just ten or twelve. So, five or six developers used Algolia in my current team.

My company decided to use Algolia. It's a very big and complicated web application. I asked them why we use Algolia, and they told me that implementing such functionality or filtering functionality customly would take too much time. But if we use Algolia, it helps us save time on maintenance, and we can use it frequently.

If someone reads the documentation well, then anyone can use Algolia. But for beginners, it's hard because the documentation is not beginner-friendly, in my opinion.

Deployment model: I used Algolia in the cloud for search recommendations to make the site's searching faster.

First of all, I want to know why you would ask me this. If you have a complicated project, then I definitely recommend Algolia. But if the project is simple and not too complicated, then you can use custom filtering or functionality.

There are two types of beginners:

1. Those who want to learn just from watching YouTube videos.
2. Those who are willing to read the documentation and research the features.

For the latter, it will be okay, but for beginners who don't want to read the documentation and just want to watch YouTube videos, it will be hard for them to learn to use Aloglia.

Overall, I would rate it a nine out of ten. When I use this product, it reduces my time and saves me from time-consuming tasks because everything is automated. Every back end, Express, Scrapy, and others, they already told us that if we use Algolia, they already provide us with webhooks.

If we implement the webhooks, it only takes two to five minutes maximum. Then we work on the front end just for designing. If I created the functionality without Algolia, it would take too much time, like 30 minutes or an hour.

But when you use Algolia and webhooks, it takes only five minutes, and Algolia has its own component, InstantSearch, for filtering. We just implemented the component, and it's okay. That's why, from my side, I rate Algolia as nine out of ten.

I have used the solution in my company since we started building a product, and we have worked on it in terms of building a search engine and searching some of the microblogs we built.

I really like the recommend feature. Almost all the features and the search feature are pretty seamless. I think what really worked for me is the recommend feature and the InstantSearch.js feature through which you can just build a page, and you have some of the filters on the left, and you can just select from it.You don't have to worry about building a UI or UX out of it as it is already built-in. The tool is great in terms of integration.

I had a team of two junior engineers, and I really wanted to equip them in terms of knowledge of Algolia, so it is not something specific to development or knowledge. Algolia provides a certification, which is pretty basic, and I think it can be improved in terms of a bit more detail and more elaborative content.

I have been using Algolia for a year. I am a customer of the tool.

I have not faced any issues with bugs or breakdowns. Stability-wise, I rate the solution a ten out of ten.

It is a scalable solution. The tool's ay-as-you-go feature is quite expensive. It works well if you are an initial user, a small-scale user, or even a medium-scale user of your applications.

I have experience with OpenSearch from AWS. I started using Algolia since it had almost the same price as OpenSearch but with a few extra features. In Algolia, you could really store big objects and records. The tool also has the recommend feature, which we need. With OpenSearch, we had to build a sidelined solution for it, so we had to put extra effort into it, but it was already built in Algolia.

The deployment is pretty seamless.

The tool is worth the money, and I have seen an ROI.

The product is cheap.

In terms of integrating the tool with the e-commerce platform, I would say that I have a Python server, so I am leveraging its APIs to sort of migrate the data and build an index out of that.

A developer can use it easily.

I did not face any challenges when integrating the product with other tools.

Within the recommend feature, the tool was pretty similar where we didn't really have to do anything extra. AI did enhance the recommended feature in the tool.

The tool is very easy, and you don't have to spend a lot of time doing anything extra. Integration is also seamless.

I rate the tool a nine out of ten.

We use Algolia as one of the core features for our product. We help our customers search within our product. We have millions of companies and tens of millions of people, with different data points or dimensions. We've built a feature like Google, where they can search with different filters or keywords.

The one thing I like best is the documentation.

The challenge is that Algolia doesn't support joining between multiple indexes, similar to how you would join tables in SQL. This limitation makes it difficult to manage and search across large datasets with different types of entities.

I have been using it for six to eight months now.

If you don't read the documentation, you will see all those kinds of issues. However, I have spent enough time doing some proof of concepts and reading their documentation. I know what the constraints are, what they can do, and what they can't do.

It's scalable. It can be scaled massively, but the only thing is that it indirectly impacts the cost because they charge on the API hits. Everything runs on their cloud because it's a SaaS product.

So, the more you scale, the more cost it adds to your project. It's just like adding a couple of hundred thousand or a million records if you want to scale up. It really doesn't matter to them; they're usually able to scale fast. We have millions of records, and they are able to search within a second or 900 milliseconds or less than that, based on the size of the data.

Again, there are some techniques you can follow in the documentation, like sharding. So, I would probably rate the scalability an eight out of ten.

I hardly need to talk to them because they have written very good documentation. The support is good. I had some support from the Australia team. I met that person personally. I haven't faced many issues.

I usually find things in their community webinars or documentation. In the event I don't find them, I might reach out to their developer community or integration team, but I have rarely done that so far.

I used OpenSearch and Typesense, who are both kind of like Algolia in that they're open source and trying to do similar things. There's a developer community, but I like the way Algolia was doing things, and they have a lot of features in place like AI search.

We used OpenSearch instead of Algolia earlier. The one thing I didn't like about OpenSearch was the cost. There were a lot of expenses, but with Algolia you can replace that cost with effort. They have well-built documentation for implementing anything. So, the one thing I like best is the documentation.

Setting up Algolia and making it run on their cloud is quite fast. However, if you want to run things and see them in your front-end development, it will probably take a day or so.

Algolia also has UI demos, which means as long as you have the data uploaded, you just have to click a few buttons and see a demo within a minute. So, if you have the data in place, it's quick.

However, you need an expert to optimize the dataset because there's a limit on the record size. Every record cannot go beyond 100 KB. You have to figure out what matters to the customer and push those points, which can help you get a better result/ROI.

It's only available on the cloud. It's a SaaS product, not open source. You have to trust their infrastructure and deploy it there.

Challenges with integration:

I wanted to have different indexes, similar to SQL tables where you can join things. I wanted multiple indexes to run them isolated. But joining indexes is not possible.

For example, I have a million companies with a hundred data points each, and ten million people with ten data points each. The constraint from the development team and the community is that you have to use one single index, which makes things complicated on the back-end.

To overcome that, you need to work on integrating into one single pipeline and optimizing the record size limit. You have to come up with better ways of searching for the data that really matters. If you have something that doesn't matter for searching, just take it off.

Joining is quite complex. I raised a request last year to support joining between indexes, just like an SQL table, but I'm not sure if they have made it live.

It's worth the money if the search is the core feature of your product. If you just want to impress your customers, it might not be that great. But if you really want to run the business with search as the core feature, this would be a great fit.

You can invest a bit of money into the search, which will give you a good output because you don't have to spend a lot of time setting up infrastructure. They have quite a lot of SDK packages that will help you move the build very quickly.

Overall, I would rate the product an eight out of ten. I would recommend it to people if search is a core part of their business. Algolia can do the heavy lifting and take care of that instead of reinventing the wheel.

But I wouldn't recommend it to people who have lots of data but can't afford the cost. However, if people have less than 20,000 records and want to give their customers a better experience, then it's a good fit for them.