It is quite scalable.

I would rate the scalability a seven out of ten. 

It is a scalable tool, but it's not impressive. The challenge arises when scaling out becomes prohibitively expensive. Instead of offering end-users the flexibility to specify the number of instances, there's a tendency to provide preconfigured packages. This approach may not be ideal, particularly for those seeking smaller scale-ups.

In our organization, it's not so much about the number of people as it is about the number of products utilizing it. Currently, we use Elasticsearch in more than 12 products. 

It's become essential for any component that requires text-based functionality. Besides that, it's also used for logging to analyze application performance, peak times, etc. Elasticsearch is a basic component of the architecture for each of these products.

It's scalable in the sense of pods or quantity or numbers of requests, yet not so scalable when considering persistence. We can't handle too much long-term data.

We have at least 500 people using the solution right now.

As a bank, we have some constraints around using and adding new tools. It's very difficult to change stacks. Therefore, we have no plans to stop using the solution anytime soon. 

It is scalable. It's not a problem if you need to expand it. 

We have about 20 people using the solution right now. We're using it in a test environment right now. Once we deploy to production, 300 to 400 people will use it. 

Elastic Enterprise Search is scalable. On a scale of one to 10, with one being not scalable and 10 being very scalable, I give Elastic Enterprise Search a 10. 

The solution is definitely scalable and that is one of the reasons we moved from Grafana. We use Spring Boot but the Spring Actuator's micrometer does not scale properly and is very slow. The solution can scale and manage all our monitoring needs in one place. 

It is a scalable solution. 70 people are working with this solution in the project, 35 on the development team and 20 backend people. We are working on the development, but it's part of the service that the Indian company has to provide. There are about 50 people on their development team who deal with all the development, infrastructure implementation, architecture definition and implementation of the software stack. We are the counterpart of that company.

Everything is managed by Amazon AWS, making Elastic Enterprise Search highly scalable.

We have approximately eight engineers using this solution in my company.

It's a scalable solution. We can expand it if needed. We have 50 to 60 users on the solution right now. We do not have plans to increase usage at this time. 

The scalability of Elastic Enterprise Search is good.

I am using Elastic Enterprise Search on-premise and it cannot scale. However, they do have a cloud option.

We have approximately 100 people using this solution in my organization. We use it on a daily basis.

It is a product that can scale well. It's not a problem. 

We have maybe 200 people on the product right now. 

The tool's scalability is tied to your infrastructure. You need to have the money and resources to scale your infrastructure. To scale up, you need faster disks and more servers.  My company has 15 users for the product. 

The scalability is very good.

We encountered issues with scalability.

The solution provides powerful scalability. I rate the scalability a ten out of ten. Our clients are medium-sized businesses.

More than 50 users are using the solution in our organization.

We're in the very early stages of usage. We only have maybe 20 people on the solution currently. We are increasing this, however. There will be more.

The solution is easy to scale. You can add new Elasticsearch clusters. It should be noted that you have to separate the different roles from Elasticsearch to other devices, so you need a little bit more knowledge to do it right.

Elasticsearch by itself is 100% scalable as scalability is built into the design like any Big Data system. We just have to add more nodes, and it scales horizontally and then redistributes the data into the new nodes, and the cluster becomes faster and agile automatically. Cross-cluster replication comes with a Platinum license. But this feature is highly exceptional and not a common need.

The solution has great scalability. We started with one node, then went to three nodes, as recommended by Elastic. We then found ourselves with seven nodes, and eventually 11 nodes. Then we said, "Wait a minute, this is not going well because we keep adding data and running out of storage." That's when we decided to start dropping data after three months. 

It is a very scalable soluton. It is very easy and I would recommend it to anyone. In terms of users it's all tiered. Most things are from tier zero at egress point of any major large-scale network all the way down to the customer. We have roughly 200 users. And those would include analysts and real time threat analysts. 

It is a highly scalable application. We have 15 users in our management team. I rate the scalability an eight out of ten.

As far as what we could accomplish, it was scalable, but we didn't have a lot of data that needed to be processed. We had 10,000 records and it was scalable.

The solution is easily scalable.

For a one-node installation, it is easy. You can do it and retrieve information fast, but when you are trying to scale up, everything becomes complicated. If you want to deal with several terabytes of data, you should read whitepapers or case studies or get proper consultancy from Elasticsearch. Otherwise, you will lose data. I know many customers who lost their data and could not recover it. It is not like you store everything and search for everything, and it is just instant. It is not like that. You should do your homework very intensively. It looks easy, but when you scale up, it gets complicated.

The product is scalable, and I rate it a ten out of ten. My company has three users. We use it regularly. 

If you use a cloud platform or a cloud environment, it is easy to scale. 

For on-premises, we are using OpenShift. We are using a cluster on OpenShift, and we are facing some issues, but they are not related to Elastic. They are related to our infrastructure of OpenShift because OpenShift is deployed on VMware, and the storage of VMware doesn't allow us to take backup snapshots in a secure way. We are thinking of migrating this cluster of OpenShift to another platform.

Currently, we have a few users of this product because we have been using it only for one year, and we are the first ones in our company. In the future, we will have more people involved with the product.

The scalability of the product is good. It's our key system that generates alerts and does surveillance on a security level. This product is extensively used in our organization.

We have people of course, from the server team that makes sure that the logs get collected. And then we have the people that actually deal with the configuration of the ELK as well. That is a team of five or six people that we use now. Then, of course, we have all the teams that follow up on the alerts, and there, I would say, we have two or three different teams, which is between 10 and 20 people. That's just part of the people that work with the solution.

Elastic Enterprise Search is scalable. In our environment, we deploy it in a containerized environment. For us, we've experienced the scalability of the solution because as we grow and expand, we spin up more containers that are interconnected. I don't see any issues with Elastic Enterprise Search from a scalability perspective. 

I rate Elastic Search's scalability a ten out of ten. 

The product is highly scalable, especially on the database side (elasticserach).

The solution is quite scalable and this is one of its advantages. We are trying to add or plug on to Elasticsearch at present.

It's scalable, but there are some limitations.

If you are scaling a bit too quickly, you tend to break the applications into different indexes. 

The limitations come in when getting the correlation between the applications or the logs.

It is difficult to get the correlations once the indexes have been split.

The scalability is fine.

This is a scalable solution, we have eight to 10 users. We had initially planned to expand use of ELK because of its cheap price and the services that are included, but given the difficulty with implementation we've decided to go with Nagios instead. 

ELK Elasticsearch is a scalable product

This solution is used by five to ten people in our organization.

ELK Elasticsearch is used on a daily basis.

Elasticsearch is very scalable. This was one of the most important points of our decision. We use Elasticsearch with about 1000 users. Now have two sets of solutions:

• One set works with the dashboard from the project team's site.
• A second set works with the enterprise department involved with two roles. 

As for the staff, we have an infrastructure team dedicated to Search. I can't answer quickly how many people we use for that. For maintenance, we employ about three to five experts.

We are planning to increase our Elasticsearch usage. We have the plan because some Elasticsearch systems integrate with other products and we must grow.

Not really any scalability issues, but we have set up a cron job to delete old logs so that we don’t hit the disk space issues. It would be helpful if such a feature were added to the UI, where old logs could be deleted automatically. (Don’t know if this feature is already there).

We have approximately five people in my organization using ELK Elasticsearch.

Elasticsearch is a very easy to scale product, compared to other similar technologies.

It's a scalable solution. For now, we have about 10 users and we plan to increase that number. We use it regularly. 

The stability is good but we have only done vertical scaling and not horizontal at this time. We collection approximately 1,000 EPS and have three people using the solution in my organization.

We have not had any issues with the scalability.

The scalability is very impressive. We can do a lot of things with the product and have not explored all the possibilities as it is something we use somewhat lightly compared to its potential.

This solution is scalable.

No issues. It is easy to scale with the cluster node model.

Elasticsearch is scalable, although we only have about five users and they are not constant. We do plan to increase our usage in the future.