My primary use case is powering up the search in the product.

It is quite easy, and I think it was really great the way they offered the services. I can just click provision the instance into AWS and then I'm good to play with the fast API.

The analytics is the way I have set up, using the fast API as a wrapper on top of Pulse Elasticsearch and OpenSearch and then exposing these as APIs to our front-end systems. All of the logs go to log files, and I ship those logs to our database and then create analytics on top of it.

I think the way they support the ingesting service for Pulse Elasticsearch and OpenSearch is not really great. If I got to attach a few more records, they don't support it. It's all rewrite every time; basically, I truncate the index and then deploy a new index and do a blueprint deployment. I think if they can support ingesting for a few records at any time, that would be great. It will just go ahead and attach to the existing instance.

I think instead of supporting the machine learning services, I'm not sure how many people are using their machine learning service. Rather, if they can support the LLM querying inside Pulse Elasticsearch and OpenSearch, that would be great.

They should support the LLM integration inside Pulse Elasticsearch and OpenSearch.

I have been working with Elasticsearch for probably two or three years now.

To be honest, Elastic didn't solve the problem for us, and I don't recall why I moved to Pulse Elasticsearch and OpenSearch, but I think Pulse Elasticsearch and OpenSearch is what we are on now completely.

I would not really rate my experience great in Elastic, but in Pulse Elasticsearch and OpenSearch. It's the same thing any which way, but we never scaled up in Elastic.

The alerting feature? I think we have it. We have an alert coming to Slack for all of the 500 errors, but we never added the PagerDuty or any other alerting system into it.

I tried Algolia, then moved to Elastic, and then moved to Pulse Elasticsearch and OpenSearch.

I think we provisioned an instance inside AWS for Elasticsearch by Elastic.

I have not used the SQL query support feature.

I did assess the RESTful API support.

I have not utilized Pulse Elasticsearch and OpenSearch machine learning features, as there was no use case for me. But I think I did add the LLM outside that, the machine learning that is provided by Pulse Elasticsearch and OpenSearch. I did add the LLM querying outside the product. So if at all I need, I just call the generate services and do a little bit of research and development there.

I would rate this product an eight overall.

Pulse Elasticsearch and OpenSearch has been utilized for around three years. The main use case is using it as a search engine for customers. Depending on the company, insurance companies search for PDF documents and claims, while medical insurance companies search for medical notes and other medical-related information.

The best features that Pulse Elasticsearch and OpenSearch offer include the scalability, so no matter how many indexes are stored and searched, it scales very well.

Scalability has helped the organization in many situations. For example, when disk space runs out, it automatically increases the disk space to serve customer needs.

In addition to scalability, the speed of Pulse Elasticsearch is greatly appreciated, as it is very fast compared to other resources used for that purpose, which makes customers happy.

Three and a half years have been spent working in the current field.

Pulse Elasticsearch and OpenSearch is stable.

The scalability of Pulse Elasticsearch and OpenSearch is working very well, and that is one of the key metrics of the service.

Pulse Elasticsearch and OpenSearch customer support has been interacted with many times, and it was always extremely helpful. A rating of 10 would be given to customer support.

Positive

A different solution was previously used, and the only reason for switching was the speed, as the old solution was very slow on searches.

Since using Pulse Elasticsearch and OpenSearch, migration from other search services has occurred without experiencing any issues so far, unlike the other systems where a lot of latency was faced and caches had to be warmed up before searching. With Elasticsearch, everything is much easier.

A return on investment has been seen in terms of time saved, as dealing with Pulse Elasticsearch and OpenSearch through its blue-green deployment means only scheduling and monitoring its completion is required without spending a lot of time on upgrades.

The decision to use Pulse Elasticsearch and OpenSearch was made by a higher level without evaluating other options.

Customer satisfaction has significantly improved since switching to Pulse Elasticsearch and OpenSearch from old legacy services, as many insights from customers indicate that latency has changed dramatically and the speed is significantly better now.

For others looking into using Pulse Elasticsearch and OpenSearch, it is definitely recommended to give it a try, especially if old legacy systems that are slow are currently being used. Lately, OpenSearch has come up with a new version that greatly enhances speed, so trying it is encouraged and it will be loved. A review rating of 9 reflects the overall satisfaction with Pulse Elasticsearch and OpenSearch.