I use Elastic Search for fast search of products in our database. With Elastic Search, we use full-text search with keywords and different rules from the Elastic Search documentation. I do not have cases when a search request is four sentences long. I typically use three, four, or five words for searches.

I think the best feature of Elastic Search is the speed. It is very fast and comfortable to use in requests with transpositions rather than full requests. It has a smart engine inside.

In Elastic Search, the improvements I would like to see require many resources.

I have used Elastic Search for two or three years, though I do not remember exactly which it is.

Maintenance of Elastic Search is easy because we do not have problems. I would rate the stability of Elastic Search at an eight.

I would rate the scalability of Elastic Search at an eight.

I did not have a situation where I needed to ask something in technical support for Elastic Search.

I used a different solution before using Elastic Search. It was Sphinx.

I do not know if the deployment was easy or complex, and it is also not my responsibility.

I do not know how it was purchased as it is our DevOps responsibility. I know that it is in AWS, but I do not know the details of how it is deployed there.

I do not know about features such as Agentic AI, RAG, or Semantic Search in Elastic Search. I did not know that there are AI search features available.

I would recommend Elastic Search to other people who want to have fast search in their applications. It is comfortable, it is fast, and it is very interesting to work with it. I gave this product a rating of eight out of ten.

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.

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.

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

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

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.

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.

We utilize Elastic Search to bring a bunch of data sources together into a large search corpus, which is used to power our core research platform.

We don't generally do a lot of full-text search with Elastic Search. We do a lot of keyword-based searching and a lot of faceted search, and it works really well. We've also had to build custom relevance algorithms based on data that's being stored in the search index. This is more about the algorithm being less about text matching and more about feature matching and relevance on a number of different scales. It's generally worked out really well.

The best feature of Elastic Search is it does exactly what it says. It's really easy to get set up and running and have search running very quickly with basic, out-of-the-box features. It scales very well, and we can do a whole lot with the core feature set before having to move to more advanced concepts. Even then, it performs very well, whether we need to expand into vector databases or decide that the Elastic Search Query DSL doesn't solve our needs anymore and have to go with ESQL or something. It expands and scales really well.

The hosted solution means Elastic Search takes care of the maintenance, which is one of the reasons we chose it. There's been very little maintenance from a data perspective on our side. As we make changes to our database structure, we've had to mirror them into Elastic Search.

We haven't had the opportunity to use the hybrid search with Elastic Search yet. I think there's a place for it in our long-term solution, but we're not quite there yet.

We haven't yet used any AI features built into Elastic Search.

To do what we want to do with Elastic Search, the queries can get complex and require a fuller understanding of the DSL. Once we start to build that understanding, it's another muscle we have, so it's not a bad thing, but it just takes a while to get up and running with expertise for our engineers.

It's not hard to learn how to use more complex things in Elastic Search; it's just a challenge we're going to face.

In my career, I've been using Elastic Search for three or four companies, probably on and off for 10 years.

We've had various very small blips with Elastic Search, but it's never been an issue that was concerning. We have limited infrastructure, so we could go further in terms of our hosted deployment to ensure that some of those things didn't happen. We've simply accepted the level of risk we have.

Thus far, everything seems really good in terms of scalability for Elastic Search. We don't have the largest data set in the world; we have millions of records, single-digit millions, so two or three million records. I feel confident knowing that we could times that by 10 or 100, maybe, and it would still work. The cost would obviously scale, the number of nodes would scale, but Elastic Search would be able to handle that level of scale.

Before I was using Elastic Search and actually before Elastic Search even existed, I previously used Apache Solr and Lucene in my career. The release of Elastic Search way back when was a boon because it was out of the box and did what it said. We've also worked with Pinecone, Amazon's OpenSearch, and essentially Postgres trying to do vector search in Postgres. All of those tools have their place, but if we're doing straight search, Elastic Search is just really the right answer.

The initial deployment of Elastic Search was really straightforward because we used the hosted solution.

We had Elastic Search live and our first initial searches running in our staging environment within a week. We moved into production with our full data set within six weeks.

We had one engineer working on this implementation. That's why it took six weeks.

Elastic Search's pricing is affordable when using the hosted solution through Elastic Search. The pay-as-you-go monthly approach has been nice, and if we scale as a company grows, we'll probably switch to a prepaid model, which will be an even bigger benefit. Having the hosted solution and not having to pay for essentially a DevOps person on staff to manage makes it affordable. We haven't really looked into serverless, which has its own benefits. I think serverless still had some challenges early on, and I wanted to go with something I had previously worked with. The hosted solution pricing fits, but the pricing for serverless also looks really interesting. The self-managed solution is nice from a pricing perspective, but we need the right staff to support it, and we don't have that staff.

We don't use Elastic Search for log ingestion, though I think they have a feature for this.

We haven't worked with anything in terms of Elastic Search integration process for third-party models with interference endpoints.

I'm not using the Attack Discovery feature because we're not using Elastic Search for our observability approach.

We have no partnerships or anything with Elastic Search. I would rate this review as a 9.

Elastic Search is normally used for full-text search where users are fully depending on it for searching by name, address, and similar fields, and we need to gather the data with good latency, so we normally prefer to save it into Elastic Search.

Elastic Search helps for full-text search because we normally use it for keywords and other related terms. If there are keywords and searching requires numerical data and other elements, we prefer RDS over Elastic Search. However, if it is regarding complete full-text search in which we cannot do any kind of indexing and it is very difficult, we prefer Elastic Search.

Elastic Search's best feature is that it is very convenient to save, plus it is schema-less, and it has very good latency and also provides us with different kinds of mapping strategies which allow us to optimize many things according to the data structure. It is a kind of non-structured and structured mix.

The benefits of using Elastic Search are mostly for two to three purposes. For logging, it is very easy to insert the logs into Elastic Search and start searching it using Kibana, and it is very easy to make visualizations over there. The second purpose is that we normally use it for views. If we have searches from the front end with a specific structure, it is very difficult to go to a different table and create the query in the database, so what we do is sync our database with Elastic Search and create a view on Elastic Search which will give us the result in milliseconds. This is how we are currently utilizing it.

Elastic Search has an annoying limitation regarding page size. It has a specific limit for queries on Elastic Search, and the default is ten thousand, and we can increase it. However, after increasing, it can slow down. Pagination in Elastic Search is very slow. If I need to parse one million records saved into Elastic Search, it becomes a nightmare because I need to do the pagination, and it is very problematic in that regard. I need to do ten thousand records and then go to the other page, and when going to the other page, it currently takes much more time than RDS. For specific cases, if we need to do full-text search and searching for one specific word returns less than ten thousand records, it works very well. However, if we go for more than ten thousand, then it creates an issue for us.

It has been almost ten years since using Elastic Search.

Elastic Search receives a stability rating of nine point five; we rely on it.

In terms of scalability, for the managed service, it is very easy, but the scalability aspect is a bit tricky. Scaling up Elastic Search cluster requires a bit of time because of sharding and replications. It takes more time since it needs to copy the data. For example, if we are working on three nodes and adding a fourth node, the synchronization process will occur in the middle, and the higher the data volume, the more time it will take. Scalability is rated around five to six.

Elastic Search's technical support receives a rating of eight.

Previously, we were using the AWS managed cluster on the cloud, but now we have created our own. On the same cloud, we have deployed Elastic Search on our EC2 machines, so it is self-managed, not on-premises. On-premises would be if we give the solution to somebody else, then we would deploy Elastic Search on their specific cloud, but we only deployed it in our system.

I did not go into the deployment part of Elastic Search because it is a DevOps matter. I was in a senior role, so I sent the request and we received it. Normally, it does not take a lot of time if the person deploying is capable; it does not take more than two to three days.

We have about twelve specialists.

I cannot say much about the return on investment part because we normally work on a use case basis. If we find some kind of issue in our database which is currently taking time, then we need to shift to Elastic Search, and it will start giving us very good results. On the cost-saving side, rather than increasing our RDS from a less cluster to a big cluster, we can create a specific separate Elastic Search cluster, and it saves our money on our basic structure while giving us much more performance. I cannot tell you the exact part on how much was saved with the calculation, and I cannot provide the numbers, but it saves our time on the debugging side. Using it on the logs and creating visualization is very convenient for us to search the log and identify the issue as soon as possible. This saves our time, saves the customer's time, and decreases the time to respond and resolve.

Elastic Search's pricing totally depends on the server. Managed services from AWS are used, and we have worked on a self-managed Elastic Search cluster. On the AWS side, it is very expensive because they charge based on query basis or how much data is transferred in and out, making it very expensive. That is why we moved to the self-managed option. In self-managed, it is very easy to handle. We do not think any kind of proprietary Elastic Search solution is required.

Elastic Cloud Serverless is not being used. The GenAI experience with features like agentic AI, RAG, or semantic search is not currently being used. Kafka Streams is being used for log instigation.

Elastic Search has many pros, but the cons of it are that it is not structured, and we need to put all the things which are connected into a single index. Therefore, we cannot use it for our base structure database, but we always use it for supporting purposes.

While part of Careem, there were hundreds of thousands of customers using the solution, and now that in a startup, the clients are no more than one hundred.

Elastic Search requires maintenance. We need to keep it updated because Elastic Search normally launches new features and versions on both Kibana and Elastic Search sides. We need to keep updated ourselves, and also, we need to do maintenance on the storage side. Normally, we use Elastic Search for timelines, saving all the data from beginning to end, so normally the storage maintenance is an issue, and we have to increase the storage time to time, but it is not related to Elastic Search; it is actually related to our use case.

There is lots of support for Elastic Search in different tools like Logstash which we normally use for integration, and there are other tools as well, but it is very easy and not a big issue for that.

The Attack Discovery feature is not being used. Big businesses cannot survive without Elastic Search because it gives us very good visibility and handles our use cases very well. If we need something reliable and trustworthy as a solution, then Elastic Search is the way to go, as it is an integral part of big solutions. The overall review rating for Elastic Search is eight point five.

Elastic Search is being used for two main streams. The first use case is an internal analytics engine for the usage of our services, which is based on logs that are put into Elastic Search indices to build different dashboards for key executives and developers, providing different levels of information. This is essential to provide statistics as a nonprofit organization funded by the Department of Energy and other infrastructures. The main focus is on web access to the Protein Data Bank for scientists and bioinformaticians with a publicly facing service supporting roughly 15 million users and an average load of about 700 requests per second. There are two data centers, one on the East Coast and another on the West Coast, serving the same publicly available interface. Logs from these services are monitored and collected, then put into Elastic Search database, from which different perspectives are provided for various stakeholders.

The second use case is Application Performance Monitoring, where Elastic Search APM stack is used to collect application performance metrics, primarily using Java, with a bit of Python and Node.js. Those three agents are used along with a standard infrastructure with the APM server that injects everything into Elastic Search indices for incident recovery and finding performance bottlenecks. As a nonprofit organization using an open-source license, there have been no problems with Elastic Search trying to change the license. Since no commercialized services are provided, the organization remains out of the scope of those issues and continues using open-source licenses. Recently, integration with an internal Keycloak instance was completed to provide role-based access to the Kibana application, which was a bit non-trivial but was managed successfully.

The experience regarding the relevancy of search results with Elastic Search is positive since it is used for providing search features for end-users of the Protein Data Bank. During ETL processes, information is collected from different data sources regarding proteins, including protein annotations and structures, which are transformed and loaded into the internal database. One part of that database includes Elastic Search indices. For search capabilities, full-text search is performed for end-users while keyword search is used primarily for internal needs, and no complaints have been heard about either of them.

The main focus is on web access to the Protein Data Bank for scientists and bioinformaticians with a publicly facing service supporting roughly 15 million users and an average load of about 700 requests per second. There are two data centers, one on the East Coast and another on the West Coast, serving the same publicly available interface. Logs from these services are monitored and collected, then put into Elastic Search database, from which different perspectives are provided for various stakeholders.

There are a couple of improvements that would definitely save a lot of headache with Elastic Search. One would be if the open-source license had multi-user access to Kibana, which exists in the paid tier license. There were also some difficult times with parallel and point-in-time interfaces, so better documentation could help, particularly more example-driven content. The provided documentation tends to have some common words but lacks real applicable examples. More specific examples, such as step-by-step guides, would be ideal. From a technical point of view, there are no significant issues recalled as Elastic Search has been absolutely awesome for this use case and covers 100% of the needs.

Elastic Search has been used for roughly five years.

Regarding stability, there are no major incidents recalled with Elastic Search. While not part of the DevOps team, nothing significant has ever exploded to affect the whole organization. If there were issues, the DevOps team was able to fix them quickly. Problems have been experienced with other services, but not with Elastic Search.

In terms of scalability, Elastic Search is good for this organization. A standard three-node setup with multiple clusters is being used for internal and public needs, resulting in six nodes per database across the data centers.

There has been no need to contact customer tech support for Elastic Search. It has been sufficient to visit conferences such as SCALE in Southern California Linux Expo, where Elastic Search has a booth to talk to their staff. The organization often relies on publicly available resources such as forums, issue trackers, and an internal knowledge base. Once, a ticket was created on GitHub concerning a Kibana issue with Application Performance Monitoring, but that was essentially the extent of it. The main sources of support are conferences and documentation.

No alternatives similar to Elastic Search have been tried. When the discussion about the open-source license started, OpenSearch was briefly looked into but the decision was made not to move forward because the organization felt secure in the current usage without commercialization.

Elastic Search AI, RAG, and semantic search have not been explored yet, as those opportunities for integration are just beginning. Nothing has been moved into production, so further comment cannot be provided. Standard agents from APM are being used to collect telemetry metrics and send them to the Application Performance Monitoring server, which are different from AI agents.

It is difficult to assess the current pricing of Elastic Search because the organization is in a specific niche as a nonprofit organization. On-premises instances are managed internally and a managed option had been considered, but that did not pass the board's approval. Open-source licensing has worked well, and there have been no ceilings where payment options for additional services needed to be considered. Users are quite satisfied with what is provided, and the organization is happy with what is received from Elastic Search.

The learning curve with Elastic Search was very easy. With a strong background in Java and software engineering, and having a great tutor in the organization who showed how to perform ingestion pipelines with Grok and how to use the development environment within the stack, the process was manageable. While it might be difficult for middle-level and junior developers, having someone experienced in the organization makes it manageable to share knowledge.

Elastic Search mostly requires maintenance during upgrades. While it is running in standard mode, there have been no major incidents from memory, so it has quite low maintenance requirements.

There are no official partnerships with Elastic Search; the organization is just a user utilizing the open-source license. Overall, this review has been given a rating of 9.

Elastic Search is used as an observability tool and logging analyzer for solutions that already exist in the company, mainly in FinTech products and financial products.

Elastic Search's main advantages are the visuals that represent and visualize all entities and system components in a simplified diagram, which provides the ability to identify which component in the system has an issue.

The main benefits include having one centralized place that gathers and aggregates all logs related to different or distributed systems.

Elastic Search could be enhanced by incorporating low-code or no-code plugins that permit developers to integrate it with different or distributed systems. This would allow for configurations that already exist but need customization through plugins or simple code that can facilitate user control over parts of the visuals, dashboards, and sensors.

Graphs should be more interactive by importing different graph schemes or visuals from external resources into Elastic Search.

Given that the product has not been used since 2023, the data might be outdated. If Elastic Search is not integrated with any promised LLM, it should have this capability as soon as possible.

Elastic Search has been used since 2018 to the present moment, depending on the different companies that have been worked with.

Elastic Search is a very stable product, especially after obtaining support licenses from Elastic.

The scalability aspect is straightforward. With self-hosting, resources can be expanded vertically, which is managed from the organization's side.

There is no knowledge about general customer service, but there is previous experience in submitting support cases to the Elastic team to get answers and fulfill requirements.

Elastic Search was installed one time but the work was not completed with it.

Experience exists with Dynatrace observability tool, but Dynatrace is completely different from Elastic Search. Dynatrace is comparable to other observability tools in this category.

Elastic Search has been installed in multiple organizations, including the current employer and previous ones, and used for different purposes.

The setup is somewhat complicated due to multiple dependencies and relations with different systems. However, any engineer should be able to understand and read the documentation well to implement it properly based on business needs and requirements.

The implementation team was involved in the deployment.

Return on investment was achieved more than a year ago.

DataDog might be an equivalent product to Elastic Search, though this requires verification.

Hybrid observability was not used. Enterprise API, whether referring to ESB, API Gateway, or middleware, was not used. Serverless interaction with Kibana was not used. The overall rating for this review is 9 out of 10.

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.

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.

My usual use cases for Elastic Search are that we are using APM, Application Performance Monitoring. We are using Real User Monitoring, as a RUM. We mostly are using it for application performance monitoring and troubleshooting in that regard. I think that's the main thing we're using Elastic Search observability for right now. We are considering expanding it also to have some Metric Beats and some other features. When we have more data, we will probably start to try to activate AI within Elastic Search. That's a possibility. The Elastic Search platform that we are using is an on-prem installation. It's not a cloud solution we have. This is because of the criticality and confidentiality of the data we have in Elastic Search.

I don't think there's a specific feature within Elastic Search that I have found the most valuable so far. We are more or less using all the features in one way or the other. Elastic Search has impacted my organization positively as we use it for logging and APM. It's not all systems which are using it yet, but it's gathering momentum because they have more use cases to present to other parts of the organization. They explain how different departments are using it, and then people see that they could also benefit from using it. More departments and their systems start to use Elastic Search as a result.

The documentation for Elastic Search can be challenging if you're not already familiar with the platform. The approach to Elastic Search can be difficult if you haven't been working with it previously. Within the product itself, some features could be more intuitive, where currently you need to know specifically where to find them and how to use them.

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

From my perspective, Elastic Search has been very stable. The only thing I'm probably missing is what we call the session replay, some kind of tool within Elastic Search based on the data collected that can make some kind of session replay.

Elastic Search is very scalable. The only issue is some features use a huge amount of storage. You need to be in the forefront to make sure that you have the necessary storage to obtain all the data that you're collecting. They probably have surveillance indicating when storage is running low. The engineering department ensures we have sufficient storage. So far, we don't have any scalability issues regarding hosts sending data or the amount of data we are collecting. The engineering department might say we are over-consuming data, but we haven't received any message saying we have reached the ceiling yet.

I do not often communicate with the technical support of Elastic Search. That's the engineering department's responsibility. If I have an issue, I go to the engineering department, and they have the responsibility to communicate with the supplier of Elastic Search or the producer.

I work with many technical solutions compared to Elastic Search, specifically on observability. We are also looking into AI, which is in an experimental phase in my area. We haven't chosen any specific technology regarding AI. For Elastic Search as it is now, we are not looking into other technology to replace it. I am a chief consultant in my department, but in this regard, I'm mostly a user. The ones who are responsible for the platform are in another department. My experience with configuring relevant searches within the Elastic Search platform is limited as I don't search much within the platform. If I have specific needs, I reach out to get assistance from specialists because they are more familiarized with the system and know exactly how to search for things. For implementation configuration of the system, they are more capable than I am, as I'm more of a user than an engineer on the platform. I would rate Elastic Search an eight out of ten because there's always room for improvement, though from a functionality and price perspective, it could be considered a ten.