Elastic Observability Reviews

Machine learning is the most valuable feature of this solution.

Because it is an open, enhanced APR, no custom integrations are required. They're open, so it's an open-wear product that's simple to use and evaluate.

They need more skills in the market. There are not enough skills in the market.

It is not pervasive enough on the market, in my opinion. In other words, there isn't a big enough user base. 

The development of new features, functions, and releases, is not necessarily based on market demand. Which is why I can't rate it a 10 because of that. 

In my opinion, because there are not enough skills, the skills are still expensive. The software and the platform may be affordable, but the skills to deploy and manage it are expensive.

I have been managing teams that use Elastic Observability for 36 months.

It's an AWS platform as a service, so it's obviously as stable as AWS.

Elastic Observability is a scalable solution.

We have approximately 500 users in our organization.

I have contacted technical support and I would rate them a five out of five.

It is a Platform As A Service (PaaS). It's challenging because, in a platform world, you have to have your own abilities. You don't rely on the vendor for help unless something goes wrong.

I have not personally used but I have led teams that used App Dynamics, Dynatrace, Elastic, Splunk, ServiceNow, DXAPM, and Tivoli.

We implemented it in a complex environment, so whether the tool was simple or complicated was irrelevant. Because we worked in a complicated environment in a bank, if I say it was difficult, you will think the tool was complex, which isn't the case. But if I claim it was easy, it wasn't.

I would rate the initial setup a three out of five.

Our organization achieved the ROI.

Pricing is one of those situations where the more you use it, the more you pay. However, the cost is variable. And, if used properly, I believe it is rather inexpensive. If you use it badly, you must pay.

If one is very cheap, and five is very expensive, I would rate the pricing a two out of five.

We had many others and did a replacement. We decided on Elastic Observability because it was the most cost-effective.

My recommendation is to start small and gradually expand. Don't attempt to implement or distribute over a vast estate all at once. Begin small. 

Use Agile methodology. Basically, don't go large at first. Take a little bit and then grow.

I would rate Elastic Observability an eight out of ten.

Elastic Observability can address multiple use cases, including monitoring, visibility, and reporting. It is integrated with a visualization product called Kibana. It's called ELK, which stands for Elasticsearch, Logstash and Kibana. Kibana provides visualization, and the others are security modules. SIEM module is there. 

We use it extensively for reporting, pulling metrics, logs, traces, events, etc. from different systems. Everything is aggregated in Elastic and visualized in Kibana. We use Logstash for ETL — extract, transform and load. We extract the data from the source, transform it—data massaging, data mixing, filters, etc. —and send it to Elasticsearch in the format we need.

The Elastic User Interface framework lets us do custom development when needed. You need to have some Javascript knowledge. We need that knowledge to develop new custom tests.

Elastic Observability is an excellent product for monitoring and visibility, but it lacks predictive analytics. Most solutions are aligned with the AIOps requirements, but this piece is missing in Elastic and should be included.

I've used Elastic Observability for two years.

Elastic Observability is highly stable. There is no problem. We tested performance, including load testing. We ingested nearly 170 million records in the system and we have tested it. It's great performance-wise. You get your reports and dashboards within a few seconds. So it doesn't take much time. Yeah. 

Elastic Observability is scalable. You can attach additional nodes as your data size grows. It's a simple process. In a three-node cluster, you already have two replicas of your data.

Okay. From a technical support perspective, yes, it is good. I have mostly, since I'm basically from the engineering and R&D. So I'm leading the engineering and R&D services basically. So we maximum for our development purpose. So we use Elastic open source. So in the platinum and the enterprise versions of Elastic, the support technical support is good. That's what I have seen.

So Elastic is basically clusters, right? Basically three, we can go with the basic cluster is a three note cluster. So the implementation is quite simple. It's not very complex. So we have to architect the solution in such a way that we have the right number of replicas and right number of charts and all those to hold the data. So basically we have to architect based on the data ingestion, how much data we are going to ingest in the cluster. So this setup is pretty simple, but we have to have the right inputs, right decisions in place before we even implement it.

We have used multiple APM solutions, which we evaluate using the metric MTTR or so meantime-to-resolution. We also have the detect-to-correct lifecycle implemented where Elastic is used to monitor multiple services and automate fixes for any issues. We reduced the number of incidents because you already have automated runbooks that run and fix the issues.

And second thing is we are monitoring an observability, which provides you the complete visibility, and it helps you to figure out the root cause quickly. So these are some of the return on investments that we can see. And also, in terms of reducing the number of incidents, reducing the number of overheads, right? So all those kind of things. And also from a data perspective, you can compress the data very easily and you can manage the data very easily. You have hot, cold retention policies, which allow you to manage your data very well. You can also ensure that your cluster is not getting full. So a lot of good returns are there, some are directly related to, I mean, dollar and some are not.

There are two types: cloud and SaaS. They charge based on data ingestion, ingest rate, hard retention, and warm retention. I believe it costs around $25,000 annually to ingest 30GB of data daily. That is the SaaS version. 

There is also a self-managed license where the customer manages their own infrastructure on-prem. In such cases, there are three license tiers that respectively cost $5,000 annually per node, $7,000 per node, and $12,500 per node.

I rate Elastic Observability eight out of 10. I deduct a couple of points because it lacks predictive analytics and root cause analysis. 

We use it to handle significant volumes of data sourced from various network monitoring protocols like NetFlow and SNMP.

We're capable of monitoring fleet-based applications as well as custom applications effectively. This capability provides us with robust performance insights, enabling us to pinpoint and address issues with precision.

The ability to ensure that the data is searchable and maintainable is highly valuable for our purposes.

The interface could be improved. Currently, the aspect that impresses me the most is the AI functionality. However, the pricing for the AI-powered APM feature is quite steep.

I have been using it for two years.

We are satisfied with the stability, as we never faced any issue with it.

The scalability is excellent, and we're quite satisfied with it. It's quite straightforward for us.

The technical support is actually very good. We haven't encountered any issues with it because our engineers are proficient with Elastic.

We initially experimented with a couple of other systems before settling on Elastic. I can't recall the specific alternatives we explored. After conducting an initial Proof of Concept, we proceeded to production with Elastic, and we're currently satisfied with it.

The initial setup is straightforward.

We have been using the open-source version.

If compared with Splunk, which is known for its high cost, Elastic is freely available as open-source software. I prefer Elastic because of its affordability. However, I acknowledge that Splunk is also a robust platform, albeit at a significant expense.

I would recommend it. Overall, I would rate it seven out of ten.

We use the product to monitor various data pipelines.

Elastic Observability helps us detect more pipeline errors. We were able to resolve 30% of the issues. It also helped us improve our e-commerce sales by 15%.

The product’s most valuable feature is Kibana. We can view and connect different sources to the dashboard using it.

There could be more low-code features included in the product. They should improve the machine learning system. Additionally, more features should be related to LLM. 

We have been using Elastic Observability for more than five years.

I rate the product’s stability an eight out of ten.

I rate the product’s scalability a seven out of ten.

The technical support services need improvement.

Neutral

We have been partners with Grafana and Datadog. Thus, we use those solutions as well.

The initial setup process has medium complexity. We require an expert in Elastic products to deploy it. The on-premises setup is complicated. However, the cloud deployment is manageable as they have good documentation and playbooks.

Elastic Observability generates a return on investment in terms of data availability. It proves to be beneficial.

The product’s pricing needs improvement. It is expensive compared to Grafana.

I rate Elastic Observability a seven out of ten. I advise others to get assistance from a specialist in Elastic products to use all the features effectively.

The architecture and system's stability are simple. The storage management behind the massive platform and the service speed are good.

There could be on-site support services available in the Middle Eastern region. Also, more web features could be added to the product.

I have been using Elastic Observability as a distributor for one and a half years.

The product is stable. There are a few occasional issues with the platform's stability.

The product's scalability is good.

I worked with LogRhythm and Rapid7 before. Elastic provides better security, comparitiviely.

The initial setup process is simple. Working on the dashboard is easy. For small to medium businesses, it can take up to 15 days; for medium to large businesses, it can take 30 days.

Elastic Observability's pricing could be better for small-scale users. It is very competitive and good for large-scale users. The node for the end user might cost around 16k. We'll allow them to implement all the modules Elastic can provide, from EDR to integration with the NDR. All of these features will take full advantage of the node. If we need to enable any other feature, we need a professional service from the experts.

I rate Elastic Observability a nine out of ten.

It offers end-to-end observability, the capability of monitoring and analyzing the entire stack, from the user experience to the low-level infrastructure. It enables troubleshooting and debugging by allowing us to trace issues through the entire system.

It is a powerful tool that allows users to collect and transform logs as needed, enabling flexible visualization and analysis.

Improving code insight related to infrastructure and network, particularly focusing on aspects such as firewalls, switches, routers, and testing would be beneficial.

I have used this solution for three years.

While there have been some stability issues, they are not considered major problems. I would rate it seven out of ten.

In terms of performance and scalability, it provides high efficiency and reliability. It can manage data without any issues with its scalability capabilities. I would rate it eight out of ten.

They provide a really good support. I would rate it eight out of ten.

Positive

Based on previous experience, Dynatrace has been considered practical with good features, but its usage depends on the specific environment. On the other hand, Elastic is versatile and can be tested in any environment to determine its value. It can be configured according to the desired specifications.

The initial setup was complex. It involved significant administration and the implementing team faced many challenges.

The implementation and deployment process took about six months to complete.

Elastic Observability is cheaper than other similar solutions, such as Dynatrace. Its license calculation is based on various factors like data volume and physical infrastructure, particularly related to RAM capacity. It may also vary in different countries.

When considering technology, it is important to focus on its capabilities rather than viewing it solely as a tool. It requires to be constantly learning and adapting to different configurations. I would rate it seven out of ten.

We use Elastic Observability for system monitoring, server monitoring, and application monitoring. I'm working on a project wherein I use the solution for capacity planning.

I have built a mini business intelligence system based on Elastic Observability. We show all the real-time transactions, the transaction type, the transaction amount, and different kinds of metrics based on different transactions. We've built something that helps our different teams working with the same stack make everything visible using Kibana. This helps the compliance team to track some Visa card transactions, etc.

Elastic Observability’s price could be improved.

I have been using Elastic Observability since 2015.

Elastic Observability is a stable solution.

Currently, Elastic Observability is scalable because the client needs to see things working before agreeing to scale the solution.

I am the only guy involved with the solution's deployment.

Users have to pay for some features, like the alerts on different channels, because they are unavailable in different source versions.

The project requires monitoring and tracking everything, including some internal services with the SAP application. The project manager needs the capacity planning dashboard to help him reduce the cost on the cloud.

Overall, I rate Elastic Observability a nine out of ten.

We are using Elastic Observability for monitoring.

The solution has been stable in our usage.

Elastic Observability is difficult to use. There are only three options for customization but this can be difficult for our use case. We do not have other options to choose the metrics shown, such as CPU or memory usage. 

I have been using Elastic Observability for approximately two years.

I rate the stability of Elastic Observability a ten out of ten.

We have approximately 10 people using the solution in my organization.

We use the solution daily.

I have not used the support from the vendor.

I have not used another similar solution to Elastic Observability.

We have a lot of Kubernetes clusters making the initial setup more difficult. If we only had one cluster of Kubernetes it would be simple. We have to do a lot of the setup manually. 

If someone had a simple environment the setup could be easier but it depends on the environment.

I rate the initial setup of Elastic Observability a five out of ten.

The price of Elastic Observability is expensive.

I rate the price of Elastic Observability an eight out of ten.

I rate Elastic Observability an eight out of ten.

We usually use the solution in our production environment to monitor production on Rancher. I'm a DevOps engineer.

The design is good and they provide great support with plenty of documentation available online.

Using this solution is quite complex and there's a steep learning curve if you've never used it before. 

I've been using this solution for a couple of months. 

The solution is stable. 

We have around 50 users, so the solution is reasonably scalable. 

We still use Instana, Grafana and Prometheus for the other environment. Elastic provides a better solution for our needs and has more features than the other solutions.

The initial setup took around a month or so because we are an enterprise company so there were some complex issues that we needed to solve. We don't really have a specific monitoring team for Elastic.

Licensing costs are reasonable and we definitely get our money's worth. 

I rate this solution nine out of 10. 

I use this product in projects that we do for other companies. We use the most updated version of the solution.

We're using Elastic to get information for several points of observability and several projects and solutions. We're using it broadly in lots of systems. For each solution, we're defining the observability points and the data we want to capture in each point. We're deploying Elastic as the tool to capture the data in each of these points in these transactions, and then putting that in the database. It allows us to analyze not only the number of transactions and quantities, but also the business content of each payload of the transactions in order to have business KPIs, not just technical KPIs. We have more than 300 data capture points in several systems.

This has been used by an IO monitoring team. We have two types of users: technical guys that are monitoring the stability of the systems where this tool is used, to see if we are having issues on the operation. This is the IO management team, and there are around 40 users. The second category is people related to business that are actually using this to capture business information, like the amount of transactions, credit sales, the average value of each operation, and things like that. In that sense, there are about 100 people looking at business dashboards. 

The use is much heavier with the first group. They are tuning systems and deploying new data capture points, etc. Although there are more people in the second group, they are using it more to get the information and use it for tech and business decisions, but they are not heavy users in that sense.

It's easy to deploy, and it's very flexible. We have been able to easily deploy it in the data capture points that we want. After you capture the data payload of each transaction, it's also easy to do the search in the database.

It could come with more detailed or sophisticated dashboards that are pre-defined and that could speed up when you start looking at the data of the transactions. If we had some pre-defined templates for observability that we could start using right away after deploying it – instead of having to build or to change some of the dashboards – that would be helpful.

I would like to see an automated deploy tool, like Dynatrace has, that would allow you to have the parts of the system where you want to do the observability and they would deploy very quickly and kind of outer connect with the systems.

I've been using this solution for 12 months.

The stability is good. We didn't have trouble installing and getting the data from it. There haven't been any major incidents, just the normal tuning that you doing as part of the deployment.

We are still increasing the number of data capture points, but so far it's quite stable.

We have plans to increase usage in two dimensions: Horizontally because we are getting the same data points expanded to other instances of the same systems. We're not creating anything new. We are just deploying the same data capture point in different instances of the same solution. 

We're also expanding vertically. We are creating new data capture points. When we start monitoring the solution, we kind of start having ideas of how to better view the operation. It's a little bit of a learning process when you start monitoring and seeing new opportunities.

The available documentation and the skill level that we have in the team has been enough. So far, we haven't used technical support yet.

Setup was straightforward to start getting the data and doing the searches that we want. I would rate setup 4 out of 5.

In comparison, Dynatrace is more automatic in terms of the deployment.

The implementation strategy was to deploy it system by system, point by point. We started looking at the systems that could have the best result for starting using this as observability tool. The idea was to deploy gradually and start getting results ASAP with the most critical transactions, instead of doing a major design of everything and deploying all at once with a bunch of transactions at the same time. It was gradual to start getting results as fast as possible.

Our technical team was about six to seven people. There were development guys because they are the ones that knew the systems and where to include the data capture points and then insert the API from Elastic that would be used to capture the data. The other guys were the IO management team and were monitoring the setup and building the database and dashboards.

Deployment was done internally with our team.

It's quite cost effective depending on your objective. I would rate the ROI 4 out of 5 because it really reached the objectives and at a lower price.

I would rate the pricing 4 out of 5.

So far, there are just the standard licensing fees. Several of the components are embedded in the license or are even open source. They're even free depending on what you use, which makes it even more appealing.

I have also used Dynatrace. Although Dynatrace is a great solution, it's becoming very expensive. They have increased the value of the licenses and the way they license, especially when we moved from on-premise to cloud. Because of the way they count the agents in the cloud for Dynatrace, it becomes really expensive. But Dynatrace is more ready as a solution. With Elastic, you need to code and program more things compared to Dynatrace. 

Dynatrace is very well positioned in the market. I think they are becoming a little too confident in that differentiation, and are reflecting this in the license price, which is becoming prohibitive. I'm in Brazil, and our currency isn't in dollars, but the license is in dollars and is becoming more expensive. The exchange rate hasn't been favorable in the last few years.

I would rate this solution 7 out of 10. 

The very positive features are the cost effectiveness and the range of things that you can implement. An improvement would be the ability to speed up the deployment, like Dynatrace. In that case, Elastic would have the cost effectiveness and would be easier to implement.

My advice is that you should first understand what kind of observability objectives you have in managing your environment. See if what you want to do is really being covered by each solution. If you're doing something that isn't that sophisticated, you don't need to pay the price of Dynatrace or Datadog. You can reach your objectives with something much more cost effective. Sometimes you don't need to buy a really expensive, sophisticated solution. 

Understand your system landscape and what you want to do and what your objectives are before jumping into a specific tool. We put a lot of research into what we wanted to do and what was the best tool for our objectives.

You should also understand what you need to implement the selected solution: what sort of skills, how many people, if you have them or not in your team, and see if you need professional services before putting together the full business case to implement. If you don't have people that really know middleware and APMs properly, they tend to be quite expensive in the market. If you don't consider this properly, you may end with a big issue in fulfilling your business case. Human resource costs are not small in this sort of project.