Azure AI Search (formerly Azure Cognitive Search) is enterprise search as a service, from Microsoft.
$0.10
Per Hour
IBM Watson Discovery
Score 9.0 out of 10
N/A
IBM offers Watson Discovery, a natural language processing (NLP) application with options to measure sentiment, detect entities, semantic roles, and other concepts.
IBM Watson Discovery resulted more robust and performant, also the insights were much more interesting than just an AI search from Microsoft or a prompt for ChatGPT.
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 …
Incredibly robust software for an enterprise organization to plug into their application. If you have a full development resource team at your disposal, this is great software and I highly recommend it. Largely, however, you won't be able to use this prior to the enterprise level. It's just too complicated and cumbersome of a product.
Companies with a strong IT foundation looking for data-driven insights and efficient data analysis tools should give IBM Watson Discovery top consideration. It is a great instrument for making decisions, combining several data sources and offering insights motivated by artificial intelligence. Also, its personalizing tools let us maximize models for our specific study requirements.
Azure Search provides a fully-managed service for loading, indexing, and querying content.
Azure Search has an easy C# SDK that allows you to implement loading and retrieving data from the service very easy. Any developer with some Microsoft experience should feel immediate familiarity.
Azure Search has a robust set of abilities around slicing and presenting the data during a search, such as narrowing by geospatial data and providing an auto-complete capabilities via "Suggesters".
Azure Search has one-of-a-kind "Cognitive Search" capabilities that enable running AI algorithms over data to enrich it before it is stored into the service. For example, one could automatically do a sentiment analysis when ingesting the data and store that as one of the searchable fields on the content.
Searching through big collections of documents has never been easier thanks to Discovery's ability to identify, classify, profile, tag, and even split documents to allow for optimized search results.
IBM Watson Discovery is by far the go-to tool for mining data collections and getting deeper insights, thanks to the recent release of the Data Miner feature and its amazing graphic interface.
Another thing Discovery does well is the use of Natural Language Processing (NLP), and Smart Document Understanding (SDU) features to train and learn the structures of documents which makes it super easy to find and highlight answers to complex questions.
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.
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.
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.
Azure Search is a competitor against Google's own AI autosuggest a feature. We went with Azure because our network security folks found it to be more robust from a security standpoint, which is incredibly important when you have proprietary manufacturing information. Additionally, we're a Microsoft shop so it plugged into our cloud hosting package and client facing OS.
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).
Azure Search enabled us to stand up a robust search capability with very few developer hours.
The fully-managed service of Azure Search means we get low cost of management (EG, DevOps) going into the future, even though the cost of the service itself definitely reflects the time saved.
Azure Search counts as a "Cognitive Service" for Microsoft Azure consumption and aligns our products with Microsoft's interests of driving an AI-first approach in the enterprise. Microsoft Partners, service and product companies alike, should be looking to align with this AI vision as it means favorable treatment from the Microsoft sales teams.
