Elastic Observability Reviews

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.

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 APM, basically. Right now, it is used only for application performance monitoring. In short, it is only the APM module that Observability is looked upon for by all our customers.

In the solution, search, observability, and SIEM are equally important. It is like Red Hat started convincing the people between open source while subscription was difficult. I believe Elastic is going to be the same thing. However, large enterprise customers who understand and who have applications that are nice with proper logs can get a better advantage.

Right now, I don't have any comments on what needs to be improved because I handle the delivery unit at a CEO's level. So, it's my team who works hands-on and on a day-to-day basis. Hence, they may be the right people to comment on this.

The price of the solution can make a 100 percent difference. I think the licensing model may not have major issues because it's only a node-based one or something. However, the customers should first consume the existing features completely because quite a few customers do not use the complete capacity of Elastic's enterprise version. So that's how things are in India right now.

The price is the only issue in the solution. It can be made better and cheaper.

I have been working with Elastic Observability for more than two years. With Elastic, my company has had a partnership for the last eight months, and the actual traction has been there for the last two months.

Most of the solutions I am looking for are not for our internal consumption. Rather it is based on my end customers' requirements. We propose solutions.

It has always been a stable solution. We didn't find any issues because, as a community administrator, we use it in quite a few places, like, across all customers, and it is one of the popular ELK stack tools.

It is scalable, but that works when it comes to cloud models. So, it should be much more.

Our company's interaction is only with three to four customers because we just started three months back. As a community edition, almost all enterprise customers do have it as an ELK or EFK stack, which may not be the enterprise version.

About the technical support, I would say that so far it has been good.

We are not just working only with Elastic. We have four different business units, and this particular competency of Elastic is with one of the units as one of the areas.

The initial setup looks straightforward. My technical team implements it on a day-to-day basis. They will be the right people to comment on these things.

The solution is deployed on the cloud and on-premises. Right now, the use cases we are working on are on the cloud models, and I think that recently they came out with an agent. So we need to see how the agent is because the agent has a lot of advantages. There are some things that we are not able to get on the cloud model that we can achieve through an agent, but we need to see the end customers' adaptability.

One needs to pay for the licenses, and it is an annual subscription model right now. We have not gone much deeper into the pricing part right now, but I can speak on this maybe after the quarter, maybe the end of September.

I would definitely recommend the solution to those planning to use it.

Being an organization into services, our key role in becoming a technology partner with Elastic was to be able to accomplish the purpose of recommending it to others.

Overall, I rate the solution an eight and a half to nine out of ten.

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.

We use the product to monitor our infrastructure. 

The solution is open-source and helps with back-end logging. It is also easy to handle. 

Elastic Observability is reactive rather than proactive. It should act as an ITSM tool and be able to create tickets and alerts on Jira. 

I have been working with the solution for six months. 

Elastic Observability is stable. 

The product is scalable, and we have around 75 users for it. 

The solution's documentation is perfectly fine. 

Elastic Observability's installation is complex. The deployment can be completed in one to two hours. You need three resources from DevOps and cloud operations to handle the deployment. 

We will buy a premium license after POC. 

I rate Elastic Observability a 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.