Algolia vs. Azure Machine Learning vs. IBM Watson Discovery

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.

Incentivized

For [a] data scientist require[d] to build a machine learning model, so he/she didn't worry about infrastructure to maintain it.

All kind of feature[s] such as train, build, deploy and monitor the machine learning model available in a single suite.

If someone has [their] own environment for ML studio, so there [it would] not [be] useful for them.

Overall, IBM Watson Discovery is an amazing technology that we use with our clients to address various business problems, but the biggest challenge has always been about ingesting, analyzing, enriching, and searching huge collections of documents and allowing our end users and SMEs to be able to search for what they need to reduce the time and efforts spent daily on a manual search through various collections of documents. We have successfully managed to reduce manual work by over 80%, and now our SMEs are being used for the skills they have to gather insights rather than do manual work.

Incentivized

• 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.

Incentivized

• User friendliness: This is by far the most user friendly tool I've seen in analytics. You don't need to know how to code at all! Just create a few blocks, connect a few lines and you are capable of running a boosted decision tree with a very high R squared!
• Speed: Azure ML is a cloud based tool, so processing is not made with your computer, making the reliability and speed top notch!
• Cost: If you don't know how to code, this is by far the cheapest machine learning tool out there. I believe it costs less than $15/month. If you know how to code, then R is free.
• Connectivity: It is super easy to embed R or Python codes on Azure ML. So if you want to do more advanced stuff, or use a model that is not yet available on Azure ML, you can simply paste the code on R or Python there!
• Microsoft environment: Many many companies rely on the Microsoft suite. And Azure ML connects perfectly with Excel, CSV and Access files.

Incentivized

• It is an excellently fast platform with documents and the answers to queries.
• With automation learning beneficial as it saves time.
• When searching for a document, everything stays located and easy to find.
• Acceptance of various documents.
• It has a quite comfortable Technical support, always available when required.

Incentivized

• 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)

Incentivized

• It would be great to have text tips that could ease new users to the platform, especially if an error shows up
• Scenario-based documentation
• Pre-processing of modules that had been previously run. Sometimes they need to be re-run for no apparent reason

Incentivized

• I believe AI should be more flexible about providing data. However, it's understandable that you need to provide the details you need in a more specific and detailed way.
• The interface could use more tweaking. Being new to the program, it was kind of hard to navigate.
• Luckily, there was a customized feature of the dashboard that I could set up, and having something that you know where you are placed always feels familiar and comfortable.

Incentivized

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.

Incentivized

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.

Easy and fastest way to develop, test, deploy and monitor the machine learning model.

- Easy to load the data set

-Drag and drop the process of the Machine learning life cycle.

IBM Watson Discovery has the best user capabilities and easily transform business decision-making portfolio. The automation system saves time used in data analysis as opposed to manual research that consumes a lot of time. The visualization across the dashboard enables my team to interpret complex data and use it to make reliable marketing decisions.

Incentivized

100% uptime for as much as we were aware :P

Incentivized

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.

Incentivized

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

Support is nonexistent. It's very frustrating to try and find someone to actually talk to. The robot chatbots are just not well trained.

Incentivized

Similar to all IBM Watson and Salesforce product solutions, the overall support would be a 10/10. Their provided FAQ's help with frequently experienced issues and if still unable to figure something out, their customer service representatives are always super responsive. With instant chat functions available, it is easy to ask a quick question rather than sitting on hold.

Incentivized

Not sure

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

Incentivized

It is easier to learn, it has a very cost effective license for use, it has native build and created for Azure cloud services, and that makes it perfect when compared against the alternatives. As a Microsoft tool, it has been built to contain many visual features and improved usability even for non-specialist users.

Incentivized

Discovery differs from its competitors due to the better ease of implementation and the high level of natural language recognition, it is equal in integration resources such as API and workflow or process pipeline, but it loses in the price for a high volume of documents and/or research. If you own or plan to use other services from the IBM Watson family, there is no doubt that Watson discovery is your best option. Another important point is if you plan to use a cloud or on-premise service (local server or private cloud).

Incentivized

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.

Incentivized

• 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.

Incentivized

• Productivity: Instead of coding and recoding, Azure ML helped my organization to get to meaningful results faster;
• Cost: Azure ML can save hundreds (or even thousands) of dollars for an organization, since the license costs around $15/month per seat.
• Focus on insights and not on statistics: Since running a model is so easy, analysts can focus more on recommendations and insights, rather than statistical details

Incentivized

• We find its Enterprise plan expensive for a country of LATAM. For US or Europe based businesses, looks great.
• A Big Data and massive queries based company would find the service expensive. Maybe a flat price plan would be helpful.
• Have you thought in making a cheaper plan where you take the learning from your customer's data to enrich your AI tool?

Incentivized