Azure AI Search vs. HP Autonomy Intelligent Universal Search (discontinued)

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.

Incentivized

It does a decent job at its core functions (that other free software does just as well or better).

• Incredibly robust back-end infrastructure.
• Streamlined integration into Microsoft's Azure Cloud.
• From a user standpoint, it lets the customer easily access their data and provide useful search tips.

Incentivized

• It adheres to traditional Microsoft standards such as: fact-dump documentation with no coherent story or 'best practices' information, inability to automate common tasks, intentional obfuscation of its basic operations.
• It provides OK search results. Not great, but OK.

• 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.

Incentivized

• There are about a dozen different config files to maintain, and the most important one is dynamically modified by Autonomy itself while it runs. Which means that it is impossible to automate the configuration or keep the configs in versioned source control. Even `cp *.cfg ~/cfgbak/` won't help you roll back a change, because it is never safe to restore a previous config. You'll be using `diff new.cfg old.cfg` a lot.
• The Linux port is poorly thought out. The binaries are named *.exe. The StartService.sh scripts contain both `echo 'Are you sure you want to start the service? Hit ctrl-C to cancel''; read dummy` and, I kid you not, a `chmod a+x /path/to/my/binary.exe`.
• Many features are poorly documented, leading to lots of back and forth with the support department just to answer basic questions like "what does this error code in my logs signify?"
• It seems to reinvent the wheel, poorly, everywhere. E.g. the scheduled backup feature rolls through a user-defined finite list of directories in which to store backups. On day 0 it uses directory 0, on day 1 it uses directory 1, and after day N it rolls back and overwrites directory 0. Why would this be preferable to using a single directory and naming zip files based on the current timestamp?

Management wants to see ROI on the (hefty) cost of purchasing this software, and has mandated that we continue using it. We would prefer to switch immediately.

I want to improve their product and also want to learn Azure AI Search like a professional and use it with full feature but their price is too high, so now I use the free plan as of now, but it takes a very large amount of data, type is few minutes, and give result that I want.

Incentivized

Product enhancement and recent updates, Azure AI Search has become more cost-effective, especially for large-scale generative AI applications2.

Incentivized

• 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.

Incentivized

• I have learned to tack a zero onto the end of any estimate I make for how long an Autonomy change will take in both planning and implementation.