Elastic Search Reviews

The main use cases for Elastic Search are index building and retrieving information using Elastic Search vector, vector search, and related functionalities. Search is the primary use case.

Computation is very good. The scalability is very good because we have a huge customer database that is searching lots of products, and auto-scaling or load balancing are the prominent features we are using in this.

If we look at the impact on operational efficiency, we can see that decision-making has become much faster due to real-time data and quick responses. We have also implemented many automations, which enhance our processes. For example, when we optimize certain fields to improve search functionality, it yields great results.

I have not explored Elastic Search at the most. Searching from vector DB is available in Elastic Search, and there is one more concept of graph searching or graph database searching. I have not explored it, but if it is not there, that would be an improvement area where Elastic Search can improve.

I have been working with Elastic Search for more than two years.

It is very reliable, and it has no downtime.

I believe it is scalable. Every day, we have thousands of users continuously utilizing the search feature. We haven't encountered any problems so far, and there is the potential for auto-scaling. It is currently a scalable solution.

We have not contacted them yet. So far, we haven't had any need.

The initial setup is straightforward.

We have a team of developers, so it is internally managed.

We have not calculated the ROI for Elastic Search, but we are a consumer platform where numerous searches are happening, and we are getting very good results from the current infrastructure of Elastic Search. Though the exact numbers or ROI were never calculated, the performance has been beneficial.

It is average compared to other platforms. There isn’t anything particularly special about the pricing. However, the pay-as-you-go model is advantageous for the organization, as we only pay for what we utilize.

We are using AWS for our solutions. In AWS, we are heavily using Redshift and Glue. We focus more on vector searches and boosting the keywords, and all those features we are using heavily. In search, the key parameter that we boost up during indexing is essential.

We self-implement Elastic Search in our e-commerce application. We are not currently doing a regex setup for RAG Playground, but there is a plan to do that. We are more into vector searches when it comes to how effectively the hybrid search capability meets our needs for combining traditional keyword and vector searches.

Regarding the workflow, we are using the API for real-time inference because lots of data is being loaded at real-time on the application, and it is working well for us. 

I can definitely recommend Elastic Search to be used wherever you have consumer search capabilities needed in a large or scalable manner because it is very effective. 

I would rate Elastic Search an eight out of ten.

Public Cloud

Amazon Web Services (AWS)

I have completed two different Elastic Search implementations, and in both cases, the goal was to speed up very slow Postgres databases. As a platform PM, I am typically responsible for user management and company management. These areas are quite heavy depending on how many users, customers, or companies exist. Before Elastic Search, when we relied solely on Postgres, there were significant delays to user list pages and company list pages. In the other company, there was a lot of data displayed for particular list pages for admins. We combined Postgres with Elastic Search to speed this up, and it certainly does speed it up. We have used it throughout my current job and previous job.

From the customer side, Elastic Search is super fast and very efficient, delivering results quickly. We recently tuned a series of compliance results in the CMS where we would specify that certain results should come up higher by adding keywords and other factors. However, the results were not as good as when we restarted and used Elastic Search out-of-the-box search results. We actually got better results that were more logical.

The most significant issue I find with Elastic Search is that it gets out of sync, and this has happened in both cases where I have implemented it. When Elastic Search gets out of sync, for instance, if I create a company or user, it gets created in Postgres and then sometimes there is a delay for it to appear in Elastic Search. This could be a 15-minute delay depending on how it was implemented. If other significant processes are running on the platform where you are touching a lot of records, such as a million records, that will take a hit on Elastic Search. We have seen differences of 800 records between Postgres and Elastic Search. Proactive tools that would find and adjust any mismatches would be beneficial.

Occasionally, Elastic Search has failed, and when that happens, search results do not come up at all. This has been a rare occurrence, and I am not certain Elastic Search is entirely to blame, as it could have been platform storage or other factors. For the most part, the most common problem is the out-of-sync issue.

I have used Elastic Search since 2019 at the last two companies where I have worked.

I would say Elastic Search is pretty scalable. We have had good results.

Earlier, in the 2019 and 2020 range, we were having a lot of trouble with syncing, and we tried to see if consulting was available. At the time, we could not find what we needed from the knowledge bases, and we could not really get support. There was not a technical support option at that time. That may have changed. Currently, I do not think we have gone to Elastic Search to ask for any significant help.

Negative

Google Appliance was a search engine that Google had for a while, and we used that and were pretty happy with it, but then they deprecated it and it is no longer available. After that, I do not remember what we used, something else.

Although I am not an engineer, it seemed easy to medium to set up. It was not complicated.

For the initial rollout, I would say it was maybe two or three people for the initial implementation, and then I have a team of 40 or 50 engineers with somebody always working on updates and other tasks.

We have not yet used anything in combination with Elastic Search, but that is on the roadmap.

I would say Elastic Search's relevancy is okay, and if I were to give it a score, I would give it a B. Elastic Search works best out of the box as much as possible. When you start to overtune or put in other factors that will increase the priority of specific results you want to come up, it gets really complicated and then you do not necessarily get the best results.

Elastic Search works best when used out of the box without excessive tuning. My overall review rating for Elastic Search is seven out of ten.

Our main use case for Elastic Search is primarily for application search and document discovery.

We built an application with APIs that make documents available for search to the enterprise and we store the documents as well. A typical flow would be when an upstream application delivers a document to us, and then a different application or different user looking for some documents comes to our application, enters the metadata for that document, which we use to search in Elastic Search to retrieve the document and then deliver that document to the end user.

The seamless scalability is something I see as among the best features Elastic Search offers.

The speed with which Elastic Search is able to search through all of the documents we place into it is quite remarkable, as we search through 65 billion documents in less than a second in most cases, on a constant consistent basis.

I find configuring relevant searches within Elastic Search platform very straightforward. Elastic Search is easily scalable.

The customer support for Elastic Search is quite good.

I advise others looking into using Elastic Search to think about the future of your platform and where you intend it to be in five years, and based on that, which version of Elastic Search best suits the needs of your platform. Additionally, jump into the AI products first as you're in the planning phase so that as you're filling out your data, the AI products and machine learning products can enrich the data real-time early on in the process, which will save you a lot of time later.

The overall performance of the platform, scalability of the platform and other additional features, especially when it comes to AI, really earn the nine.

The ability to change field types seamlessly would be a huge improvement for Elastic Search, and more seamless upgrades would also be a big improvement, especially with regards to upgrading between major versions.

The upgrade experience and inflexibility with fields keeps Elastic Search from being a perfect 10.

I have been using Elastic Search the whole time I have been at Optum since 2019.

Elastic Search is stable.

The customer support for Elastic Search is quite good.

I would rate the customer support a nine.

Positive

We previously used a self-hosted Elastic running on virtual machines, and we switched to Elastic Cloud on Kubernetes at the urging of Elastic Search itself, as well as an internal drive towards cloud-first technologies. The features of Elastic Search Cloud on Kubernetes seemed to mesh well with the overall goals of our organization.

My experience with pricing, setup cost, and licensing for Elastic Search is overall fairly straightforward.

I do not have any specific numbers on a return on investment, but I do have a general sense of the overall improvement of efficiency of the platform as we moved from on-prem hosted to Elastic Cloud on Kubernetes, where the time saved from maintaining the platform itself was significant.

My experience with pricing, setup cost, and licensing for Elastic Search is overall fairly straightforward.

We have tried the hybrid search capability, and we have seen overall fairly positive results, though we have yet to roll it out in production.

We have implemented a proof of concept using Inference APIs in our processes, but we have yet to release it into production.

To be clear, we are not on Elastic Cloud serverless; we are on Elastic Cloud on Kubernetes, running on the Azure platform self-hosted.

We have not utilized Better Binary Quantization, BBQ, in our operations.

On a scale of one to ten, I rate Elastic Search a nine out of ten.

Public Cloud

Microsoft Azure

Our use case is mainly for monitoring purposes, as we are getting the logs from our Linux machines where the applications are installed. Then we are forwarding these logs from the Linux servers to Elastic Search.

For now, we are logging the logs into the dashboard, and whenever a user wants to search on the logs, we use the platform directly on Elastic Search. I don't think we use full keywords; we directly use the user interface in the Elastic Search dashboard. Mainly, I think that should be sufficient for our users.

We don't use elastic streams for log ingestion or for structuring raw logs without agents.

We use the attack discovery feature to create alerts.

The best feature of Elastic Search that I appreciate is its monitoring capability. Whatever logs you want to forward to Elastic Search are pretty clear, and you can even edit the logs if you want some logs to delete or some logs not to appear in the monitoring dashboard, so you can clear it from there. It's pretty easy to install, easy to get handy on Elastic Search, and also easy to use it in the project. I think that's the main advantage of Elastic Search.

From a security point of view, I find Elastic Search to be quite secure, as we have a separate cluster that is well secured, and not just anyone can enter it easily.

I've noticed that the logs we are getting from the Linux servers have become automated, and in the long term, I believe Elastic Search will give promising results. When compared to Prometheus and Grafana, Elastic Search plays a main role in injecting SQL-related logs as it can inject any type of logs. It can show us any type of logs, which will be very helpful for any company or organization.

We forward the logs to our internal system that has an internal alerting system maintained by ING. The person monitoring Elastic Search, for instance, an ops guy this week or next week, will take care of the alert and try to fix it, making it quite handy to use this feature.

I think the first area for improvement is pricing, as the cluster cost for Elastic Search is too high for me. When I compare it with Prometheus or Grafana, we get very cheap dashboards with them. Elastic clusters are very costly; I understand the capabilities it has, but the price should be reduced a little bit in the market.

I also think the indexing throughput should be reduced, as using the bulk API in Elastic Search takes a lot of time and should become very fast. Additionally, observability features like search latency, indexing rate, and maybe rejected requests should be added to make the platform more reliable and accessible for everyone.

I have been using Elastic Search for close to two years in my current project.

As far as I have been using it for two years, I did not find any glitches or bugs, so I would rate it an eight or nine.

Positive

When it comes to scalability, it is scalable, but the pricing also matters, so I would rate it six or seven.

Positive

I would rate their technical support a nine because they are pretty reachable every time.

Positive

The deployment was easy for us.

We wrote some Ansible scripts, and it took maybe two weeks, a couple of weeks.

I don't think the hybrid search that combines vectors and text searches will be in my use case.

Currently, we are not using any of the trusted GenAI experience features such as Agentic AI, RAG, or semantic search.

I recommend Elastic Search to other people because it's quite reliable when used in a project. Every project can incorporate Elastic Search because it has a lot of features. The only concern I have is pricing; other than that, the features are very good. Everyone will be able to use it easily, but you need to keep in mind that you have to train some resources because there are not many people experienced with Elastic Search. You should provide some training to them before deploying them onto the project. I would rate this review an eight overall.

Private Cloud

Microsoft Azure

I have implemented Elastic Search in my organization. My experience has been really good with Elastic Search regarding the dashboards and alerts. They have integrated AI/ML capabilities in it. The Attack Discovery feature helps to dig into incidents from where they occurred to determine how the incident originated and its source. It gives an entire path of attack propagation, showing when it started, what happened, and all events that took place to connect the entire cyber incident.

Another feature is image vector analysis, which can authenticate images to prevent impersonation frauds in the ecosystem. This is a major use case in personal information and identifiable information portfolio.

I'm using Elastic Search as an observability tool and a SIEM tool. The indexing, searching, fast indexing, alert mechanisms, and BCDR compatibility are pretty smooth with Elastic Search.

On the resourcing part, I have cut off a good amount. While I don't have a concrete percentage to mention precisely, it has reduced resources to some extent.

Attack Discovery is the first feature that I appreciate. It is truly an amazing feature for any SIEM to have inbuilt. The image vector analysis is another feature that identifies any manipulation done to images. It can authenticate and identify authenticated images. If there are 10 duplicate and forged images, it can identify them through vector-based searching capabilities. These two features are prominent in terms of SIEM capabilities that Elastic Search has.

I can share feedback from the SIEM perspective about Elastic Search, as I had evaluated Elastic Search, LogRhythm, QRadar, and Microsoft.

More AI would be beneficial. I would also appreciate more simplicity in dashboards. A comprehensive dashboard is something I could expect.

I have been using Elastic Search for a year now.

There are no limited parameters to search from the events perspective. When you put one keyword, everything related to that keyword in your ecosystem will showcase all the results. This helps to get into the granularity of any events happening across the system.

It has gained significant visibility. Comparing alert statistics from other SIEMs where they could trigger 50 alerts on average weekly, Elastic Search has given me alerting statistics of roughly 90 plus for a week's time. All those alerts are mapped to MITRE ATT&CK framework. Though it could result in false positives in the earlier stage until you fine-tune and streamline the use cases in your SIEM, which is common with all SIEM tools, the visibility that Elastic Search has given us is amazing.

It was a direct purchase.

Positive

We previously used an on-premises solution.

The setup complexity depends upon the engineering team doing the implementation and the kind of infrastructure you have where logs will be ingested into the solution. For us, it was time-consuming in the earlier stages, but it was manageable and not overly complex.

We have seen moderate returns on investment.

As a CISO, I review and do the governance part. I receive alert notifications, but I don't work directly with the tool. None of my team members have complained or proposed any feature changes or modifications to the existing solution.

It totally depends upon the nature of business you are in. For my organization, it was imperative to have image scanning in place and identifying frauds happening with PII. From that perspective, Elastic Search has played a vital role. It has good inbuilt EDR capabilities as well, making it a good-to-go tool.

I rate Elastic Search eight out of ten.

On-premises

Other

I work in a gaming company where we handle a lot of microservices, observability, monitoring, and metrics. We aggregate all our logs to Elastic Search for troubleshooting across different environments including production, staging, and dev. We use Elastic Search to give us insights and to conduct a lot of troubleshooting.

We decided to go with Elastic Search because of the ability to aggregate everything into one portal where we have access to our entire infrastructure and the correlation about observability and traces. I have used competitors, but we are not using them in the production environment; perhaps on lower environments, but for production, we use Elastic Search.

One thing I appreciate about Elastic Search is the ability to aggregate everything into one dashboard, so I can have monitoring, logs, and traces in one portal instead of having multiple different tools to do the same.

Normally, if you were to use Prometheus, you need to know the Prometheus query language, but with Elastic Search, it gives us the ability to use normal human language for queries. It is very intelligent when it comes to querying. Unless you want to search something in depth, I find it very user-friendly.

I think hybrid search, which combines vector and text searches, is very effective because a developer or platform engineer does not need to spend time learning how to do a query. They can log in and use the standard query language to query a specific log, for example.

The initial deployment of Elastic Search was very easy for our instance because we just needed to enable some annotations for it to start getting the logs. We only needed to do a very minimal deployment on our side. The advantage we had is we had already deployed templates, so we did not need to configure each and every microservice. Once Elastic Search was there and we were able to push the annotations to our deployment, everything came alive.

I think the biggest issue we had with Elastic Search was regarding integrations with our multi-factor authentication tool. We had a challenge with the types of protocols that it allows. Sometimes you find it only supports one or two, and maybe we have a third-party tool for our MFA, so we are limited in how we can do integrations and in terms of audit. Since we are in an environment where we need to be compliant and have all our audits done, it is very hard to audit access logs for Elastic Search. I do not know if that has changed; perhaps we are still on an older version, but that has been the major issue we have experienced.

When it comes to updates for Elastic Search, we might need to push updates, for example, when they have a security patch that we need to enhance or add into our deployments. We do this in the lower environments for staging and then promote it into production. There is not much ongoing maintenance that requires any sort of downtime.

Elastic Search gives you quotas, so you are able to monitor your quotas and know when you are about to fill them up and maybe expand or tighten on your logs. Internally, we try not to have alert fatigue, so we only do important logs and queries, and we rarely have any sort of lag.

Elastic Search is very flexible when it comes to scalability. Being on the enterprise license, it is not really a big issue for us because we can increase the number of quotas we need depending on the logs we want.

For Elastic Search, we have never contacted any support. I appreciate the way they do their documentation and blogs. As a technical professional, before I reach out to support, I have to do my own troubleshooting and research; unless it is something that I cannot resolve, that is when I will probably raise a ticket. In the recent past, we have not raised any specific ticket for Elastic Search.

Before we migrated to Elastic Search, we were using the open-source tools Grafana and Prometheus for logs, but we had to have another third-party tool to do tracing such as Jaeger, or have Sentry to do database logs.

The initial deployment of Elastic Search was very easy for our instance because we just needed to enable some annotations for it to start getting the logs. We only needed to do a very minimal deployment on our side. The advantage we had is we had already deployed templates, so we did not need to configure each and every microservice. Once Elastic Search was there and we were able to push the annotations to our deployment, everything came alive.

The deployment of Elastic Search was done by our DevOps team, because I am part of the DevOps team. Our technical lead was mostly involved in terms of authentications and API key setup. From my side, it was easy for me to enable the annotations on the deployment and commit into the repository and push the changes to it. It was a team effort at different levels.

I would give Elastic Search probably an eight because there is always room for improvement. In IT, everything keeps evolving, and AI is here, and probably tomorrow something else will come, so they will need to elevate their game. I give it a general rating of eight, which for me means it is working perfectly, but it can always get better; there is always something to improve. My overall review rating for Elastic Search is eight out of ten.

I use Elastic Search, and from time to time I use it, but most of the time I am a system administrator. I deployed it more than using it. At the beginning, I was a system administrator, responsible for the deployment and maintenance of Elastic Search clusters. For a few years now, I have started to use it more because the end users are rookie users. They need a lot of help to be able to use Elastic Search effectively. I started to be a user approximately five years ago.

Today, at least, we provide a global, unique Elastic Search cluster for the whole company, and all teams store their logs inside, their traces, and their APM traces. Teams use Kibana to display information. We also use Prometheus exporters to collect metrics from the logs. We execute some query DSL over Elastic Search to collect metrics, which will be injected in a time series database like Prometheus. This is the main usage. We store metrics, logs, and APM traces.

The deployment of Elastic Search is excellent. I like Elastic Search very much for that. I say regularly to the team that Elastic is elastic. It is really difficult to break. This was not the case a few years ago when I worked with Elastic Search version one and version two. Starting with version six of Elastic Search, it started to be really strong. Today, in the past, the main issue was about the data and the volume.

At the moment they integrated lifecycle policy for indices, ILM, Index Life Cycle Management. When it was created, additionally to the data stream, it started to be really easy to have all the same index volume. It is really easy to administrate and to balance data between data centers and between data nodes, and to keep the same everywhere. It is very nice. It is my favorite feature of Elastic Search. It is so easy to manage. Also, maybe because we used it for a long time, we started to be comfortable with all the setup and the node type, and how we should manage our cluster to make it resilient. I think it is really easy to maintain comparatively to some other databases.

To be honest, there is only one downside of Elastic Search that makes sense because we use a basic license, which is a free license. We do not have some features available because of the free license. Except for that, I do not have any complaint. It works perfectly. It is pretty easy to administrate and to use. I do not have complaints, to be honest, except the fact that we do not have all features available such as the APM service map or alerting.

We are not able to use a provider like Sentry, Slack, or PagerDuty. We are forced at some point to generate metrics from the logs in order to use our alerting stack in Prometheus, which works. It is an open-source project which allows us to generate alerts to Slack, PagerDuty, and some third-party tools without any license. However, it is not doable with Elastic Search in the open-source version. The alerting part is the most complicated part to manage because of the license.

From time to time we have some JVM, Java Virtual Machine issues with Elastic Search. However, it is more linked to users' requests. From time to time, people ask Elastic Search to search inside one year of logs without a nice query and without any filters. This is clearly not doable and some nodes will crash. This makes sense. However, Elastic Search is really stable when we do not have this kind of request.

Elastic Search is the perfect tool for scalability. You just need to deploy new nodes. They will be able to join and reach the cluster really easily. I appreciate it for that as well because today at VP, we use Terraform to deploy our infrastructure. All Elastic Search nodes are managed through Terraform. If I need to extend my data node or my ingest node or whatever, I just need to deploy new nodes with the same setup, and the node will join my cluster, and it will scale horizontally really easily.

I have never had to contact the technical support of Elastic Search.

For logs management, I have not used any alternatives or something similar to Elastic Search. For APM as well, there was a plan in the past to try to migrate to Grafana, the Grafana open-source platform for APM traces using Tempo. Tempo is a Grafana Labs project. However, we decided to keep Elastic Search for that, so we do not have any other tool or similar tool to accomplish that.

Maybe just one, it is about error tracking. We can track errors with APM inside an application, and currently we use Sentry, which is not just an error tracking platform, but also about performance management. However, we use it only for error tracking. It is more useful for developers at the beginning of a new project. Most of the time, they prefer to be connected to Sentry more than APM in order to track errors. When the project will be in production, they will be more focused on the performance than the errors. At this moment they will start to use APM, Elastic Search APM more than Sentry. We do not provide any performance indicators. Sentry is also able to manage performance metrics, but we use it only for errors and everything related to performance has been disabled.

I think the pricing of Elastic Search is really, really expensive. The main point is that we do not get any license. I tried in the past, a few times, to contact the Elastic Search team to get a quote, and it was so complicated each time to get a quote because of the volume and the number of nodes. We are a big company at VP, so we have a lot of nodes, more than one hundred. For sure it was so expensive. They tried to tell me about the enterprise mode and about the new license way to manage cost based on CPU and memory usage. It was really expensive because at this moment, we do not use any cloud services. Our Elastic Search cluster is on-premises.

Everything is self-hosted at VP tech, at VP. We do not have any limit. People using AWS or GCP have limits because the volume of data is really expensive in cloud services and cloud platforms. Because we self-hosted everything around our services such as Elastic Search or Sentry, the idea is to let the user be able to store a lot of data and a lot of metrics. We try to train the team to have a good log level. We do not have such limitation in terms of volume. We have a really big cluster, and at the end, the price is so huge. I gave this review a rating of ten out of ten.

On-premises

Other

My main use case is for security, specifically for the SIEM aspect, as I work as a cybersecurity engineer.

We specifically use this system for security-related topics. We have a dedicated environment for Large Language Models (LLMs). We have connected our LLM, but our primary focus remains on security. When we encounter any incidents or need to gather information about connected IPs, we rely on established rules and alerts. We utilize the chat functionality of this LLM to generate queries in Kibana language.

My favorite feature is the ease of use, particularly in how you integrate the agent. I've been using it since version 7, and we're on version 9 now, and I've seen the progress from using Beats to using the agent, making it so simple today to enroll a server with the Elastic Agent. 

Deploying the Elastic Agent internally is relatively straightforward; it only requires a few commands to be run on the server. However, to manage this deployment at scale, we needed to develop a solution using Ansible. This involved creating scripts to install, restart, and uninstall the agent. While I would have preferred if Elastic had provided an official solution for these tasks, they haven't yet developed one that addresses all the necessary aspects. As a result, we've taken it upon ourselves to create these tools internally.

There are two areas in which it could improve. One is the smoother enrollment process for 1,000 or 2,000 servers at the same time, rather than having to develop something internal. 

The second topic is the actual support of YARA rules—it's Y-A-R-A, which is specific for security. As of today, this is not supported, and I've been asking for a while now; I'm unsure if they will ever release it.

I have been using this solution for at least four years.

I haven't seen any downtime.

It is really scalable. Since we're on the cloud, whenever we need to upgrade or add resources, they handle everything. It takes a couple of hours due to the amount of data we have, and I've never faced any issues during upgrades.

I have contacted technical support because we encountered issues when we started using the Elastic integrations, some of which were not finalized on their side. I had countless meetings with engineers from Elastic, including product managers and support engineers, to work on and fix the integrations we wanted to use. They have always been really responsible and responsive to my requests. Once, we had an issue with GCP, Google Cloud Platform, and they even sent us a complimentary five or six hours with an Elastic consultant to help set things up.

I would give them a nine out of ten because they are very responsive. They clearly know what they are talking about. I never encountered a situation where the support team didn’t understand what we needed.

Positive

The initial setup process took around a month.

What they need is to be more transparent about the actual setup of the cluster and the deployment process. When using Elastic out of the box, there is information that is not readily available, requiring users to dig deep into the documentation to truly understand how it works. If you're looking to set up the cluster automatically, it works well for testing purposes. However, when installing two thousand servers at once, if your deployment isn't large enough, it can lead to crashes. Occasionally, we have to delete the logs just to access the interface. Therefore, I believe they should provide clearer guidance on using the deployment manager effectively.

We started four years ago with 200-300 servers, and now we are at around 2,000 servers. The learning curve involved understanding how it works, doing labs, and the difference between Elastic Search and competitors. Elastic really helped with support; we had weekly sessions with engineers from their side to assist us in setting up.

Maintenance on my end is limited to updates. Since we are using Elastic Cloud, they take care of the infrastructure.

I am familiar with the pricing, as we negotiated it last year. Compared to other tools, it's fair. However, if we are talking with full transparency, Elastic pushes clients to buy the Enterprise edition instead of the Premium edition, and we don't see the value in that other than to spend more money more quickly. So, while pricing is good and what we expect to pay for this type of product, I'd love to finalize this concern.

We've tested multiple open-source tools based on Elastic before signing with them, including one tool called Wazuh that is built on top of Elastic. We've also tested the open-source edition of Elasticsearch where we manage the cluster and Splunk. Overall, I believe Elastic Cloud is still one of the best products out there.

I would rate this solution an eight out of ten.