Algolia, the best option to start with your search engine needs
Overall Satisfaction with Algolia
We use Algolia for our search engine needs, this comprises our end users and also our internal users (Sales agents, operations teams, and so forth). It provides an easy way to iterate on search-related features with low development time and without needing a DevOps Engineer (at the time we started using we didn't even have a DevOps team)
Pros
- The way you can easily setup Algolia, populate an index and start doing mockups with instant search (their frontend library)
- A lot of SDKs allow choosing the approach that better suits your use case.
- Not needing to spend a lot of time and money in infrastructure or software architecture.
Cons
- Analytics are a tricky thing to do and don't work right out of the box.
- Developer documentation isn't 100% complete and synced across sources so some things have to be inferred
- Their pricing had some changes which are trickier to estimate (and can be expensive)
- Easy to manage (creating/updating indexes is really easy)
- Easy to setup (almost anyone learns quickly on how to use it)
- Good amount of SDKs for different languages/frameworks
- Queries speed using their frontend library
- For the first couple of years, we saved a lot of money by not having to manage the infrastructure related to a search engine
- We have saved time using it across different features
- It has provided an easy way to deliver an important feature to our end users
Easiness to set up and maintain, our team at the moment we compared them was small and we were in an exploratory phase in the search engine feature. We needed to deploy something to our users quickly to start learning and avoid spending time in infrastructure configurations (like security) and Algolia provided all of this out of the box.
Do you think Algolia delivers good value for the price?
Not sure
Are you happy with Algolia's feature set?
Yes
Did Algolia live up to sales and marketing promises?
Yes
Did implementation of Algolia go as expected?
Yes
Would you buy Algolia again?
Yes
Using Algolia
15 - They are mostly developers from different teams:
- Our supply team makes ingests our inventory, runs validations and checks against them before saving them into our database.
- Our backoffice team, uses the inventory in our database and creates an Algolia index aimed to our internal users. Things like format and information quantity are modified according to their needs.
- Our SEO team, builds indexes aimed to our Search Result Pages, which our end users normally use for their search.
- Our supply team makes ingests our inventory, runs validations and checks against them before saving them into our database.
- Our backoffice team, uses the inventory in our database and creates an Algolia index aimed to our internal users. Things like format and information quantity are modified according to their needs.
- Our SEO team, builds indexes aimed to our Search Result Pages, which our end users normally use for their search.
There are mostly two types of skill needed to support Algolia:
- Technical:
- Backend Developers who know how Algolia works and how to create, modify current and new indexes. Also knowing how facets, ordering, etc works helps a lot.
- Frontend Developers who know how to use Algolia's libraries, SDKs, and how to correctly use them for SEO best practices for example.
- Business:
- A Tech Lead or Product Manager who can be aware of definition of formats, most important information, etc. In order to not end up with a giant index
- Technical:
- Backend Developers who know how Algolia works and how to create, modify current and new indexes. Also knowing how facets, ordering, etc works helps a lot.
- Frontend Developers who know how to use Algolia's libraries, SDKs, and how to correctly use them for SEO best practices for example.
- Business:
- A Tech Lead or Product Manager who can be aware of definition of formats, most important information, etc. In order to not end up with a giant index
- Search for end users
- Search for internal users
- Frontend search with high speed
- Avoiding to use an Elastic Search instance
- Concatenate different entities into a same search index
- Fast prototyping of landing pages
- Internal tools with query capabilities (and amazing speed)
- Small search prototypes
- Building an internal knowledge base based on support tickets
- Building a Marketing tool for inventory
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