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
for 10k search requests + 100k records per month
Azure AI Search
Score 8.8 out of 10
N/A
Azure AI Search (formerly Azure Cognitive Search) is enterprise search as a service, from Microsoft.
$0.10
Per Hour
Pricing
Algolia
Azure AI Search
Editions & Modules
Build
Free
Up to 10,000 search requests + 1 Million records
Grow Plus
Free to start, then pay-as-you-go
10,000 search requests/month and 100,000 records included; $1.75 per additional 1K search requests and $0.40 per additional 1K records
Grow
Free to start, then pay-as-you-go
10,000 search requests/month and 100,000 records included; $0.50 per additional 1K search requests and $0.40 per additional 1K records
Elevate
custom
per year
Elevate
Custom
Custom Custom search requests and records; volume-based discounts available
Basic
$0.101
Per Hour
Standard S1
$0.336
Per Hour
Standard S2
$1.344
Per Hour
Standard S3
$2.688
Per Hour
Offerings
Pricing Offerings
Algolia
Azure AI Search
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.
Algolia is both well-suited to replace Shopify's out-of-the-box search and to very large sites with millions of products in their catalog. Algolia provides a specialized solution that benefits from very large R&D budgets and ongoing investment. Algolia offers a more retail- and open-design solution than competitors such as Amazon or Google search, which offer fewer options and fewer features.
It's very useful when used with large file systems, once the models index the files good enough, the suggestions are very impressive and produce grounded answers. Since it can natively work with blob storage the requirement for pre-processing the data is eliminated i.e. the data can be searched in its raw form, this makes Azure AI Search a very powerful tool when used with Azure Stack.
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.
Better integration of features (ex. synonyms feature is great but isn't respected by their re-ranking product)
Tooling to reduce spam search queries being triaged by system/logged to analytics panels
More automated summaries of analytics (ie. recommend synonyms to add, trends noticed in search volume in specific areas of site, easier ways to leverage API vs using website UI)
Like virtually all Azure services, it has first-class treatment for .Net as the developer platform of choice, but largely ignores other options. While there is a first-party Python SDK, there are only community packages for other languages like Ruby and Node. Might be a game of roulette for those to be kept up-to-date. This might make it a non-starter for some teams that don't want to do the work to integrate with the REST API directly.
In my opinion, partitions inside of Azure Search don't count as data segregation for customers in a multi-tenant app, so any application where you have many customers with high-security concerns, Azure Search is probably a non-starter.
To elaborate on the multi-tenant issue: Azure Search's approach to pricing is pretty steep. While there is a free tier for small applications (50MB of content or less) the first paid tier is about 14x more expensive than the first SQL Database tier that supports full-text search. For many applications, it makes a lot more economic sense to just run some LIKE or CONTAINS queries on columns in a table rather than going with Azure Search.
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 is very intuitive to use, especially the Merchandising Studio. The application provides a virtually seamless view of how product will appear on the frontend and making adjustments is fluid and reflects immediately online. Some slow-down occurs when you have a lot of rules enabled or are pinning / boosting a lot of product. But overall it functions very solidly.
I give 10 rating because by using this endpoint and api key only we able to build that chatbot product in a timeline given by our client and also creating the endpoint and keys from the portal is also very easy for Azure AI Search and it doesn't take much time and also scalability is good.
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
Algolia gives way more control for a non-developer than AWS Elasticsearch Service. Previously we'd have to have our developers make adjustments to site search relevancy, typos, prioritizing certain attributes over others, etc. but now the marketing and website team can do that themselves in the Algolia dashboard
It is good for me, and I want to rate this product 9/10. I hope they continue to improve and also offer a free plan with more benefits to learn Azure AI Search.
Algolia has been a consistent product that works flawless with very few errors or downtime. With the plan options it’s very easy for us to scale especially with this usage pricing. We have 100% gotten our ROI on this product.
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.
When integrated with our existing file system the Azure AI Search helped users tremendously by reducing search times and improve efficacy of intended result.
Since Azure AI Search is a PaaS solution, we had very short ideation to go-live timespan, which ended up reflecting in our product performance.
A rare but not negligible occurrence was correctness of search being questionable when new data was added to the system. The search returns false positive results.
