Centralized log data has improved issue resolution and reduced operational costs
What is our primary use case?
My main use case for Elastic Cloud (Elasticsearch Service) is to capture logs from our various systems.
For our cloud service, we have various Elastic agents that ship logs into a central location. We have it all aggregated in our Elastic Cloud. From there, we use the logs for troubleshooting, creating alerts, look for specific patterns, understanding our service a little bit better, and aggregating all that data in one place.
What is most valuable?
One of the better features of Elastic Cloud (Elasticsearch Service) is Lucene Search, which gives our users the ability to search through the mountains of logs without giving them direct access to production systems.
Another great feature is Index Lifecycle Management that allows us to move data to cheaper storage tiers as our data ages out. The feature that we love the best is LogsDB, which allows us to index our data differently so that it doesn't accumulate as much storage in our hot tier and allows us to ship many of those logs, especially older logs to cheaper storage such as S3.
Elastic Cloud (Elasticsearch Service) has positively impacted my organization by allowing us to move away from expensive services such as DataDog and gives us about the same level of service while allowing us to keep data for a longer period of time at a cheaper price.
What needs improvement?
The logging feature of Elastic Cloud (Elasticsearch Service) itself is pretty valuable, but we tried the observability module and some of the AI features.
Those need improvement. Observability is not on par with feature and ease of use with some of the leading providers out there. The same applies to some of the AI features within Elastic Cloud.
For how long have I used the solution?
I have been using Elastic Cloud (Elasticsearch Service) for five years now.
What do I think about the stability of the solution?
Elastic Cloud (Elasticsearch Service) is stable.
What do I think about the scalability of the solution?
Elastic Cloud (Elasticsearch Service) is very scalable and very easy; we've had no issues with scaling our solution out.
How are customer service and support?
The customer support for Elastic Cloud (Elasticsearch Service) is fantastic. They're very responsive, and gave us great detail in all our tickets.
I would rate the customer support as 10 out of 10. They are very knowledgeable.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
I previously used DataDog. We switched because DataDog was too expensive, especially when it comes to logging.
How was the initial setup?
It was very quick and easy to set up. The hard part for us was taking out the metrics and observability because it wasn't relevant for us.
What was our ROI?
The ROI for this has been positive. We have seen a return of 30-40% in lower costs and improved productivity.
Teams are more productive because they have a level of self-service to research problems without accessing production systems, which they previously did not have the ability to do.
Previously, accessing logs was complicated, but now everything is centralized. This has boosted productivity for our support teams, and both engineers and other staff can quickly view service logs and troubleshoot issues in a timely manner.
Which other solutions did I evaluate?
Before choosing Elastic Cloud (Elasticsearch Service), we evaluated other options, such as Grafana Loki, and Observability.io. We found that Elastic matched what we needed the most.
What other advice do I have?
LogsDB has made the biggest difference for our team because Elastic can get expensive as your data grows. Our teams want to view data back 30, 60, 90 days and with LogsDB, it allows us to be able to capture that data for a longer period of time and without the expense.
The advice I would give others looking into using Elastic Cloud (Elasticsearch Service) is to identify your pain point and find the tool that your users are familiar with.
For us, it was logging, and Elastic was perfect for that. Our users were very familiar with Lucene Search and the Lucene Search syntax, which made Elastic the ideal option for us. There are other solutions out there that are more multi-service, but Elastic does logging the best.
Elastic Cloud (Elasticsearch Service) really saves your organization money. You don't need the folks on the back end to manage it and support it on a daily basis.
On a scale of one to ten, I rate Elastic Cloud (Elasticsearch Service) a nine.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Machine learning features have improved search projects and user experience
What is our primary use case?
We use Elastic Search for search purposes and things related to semantic search.
It is not being used for the moment regarding my main use case for Elastic Search.
What is most valuable?
In my experience, the best features Elastic Search offers are its stability and brand new features that I consider very interesting.
The machine learning features of Elastic Search are very interesting, including the possibility to include models such as ELSER and different multilingual models that let us fine-tune our searches and use them in our search projects.
The machine learning features of Elastic Search have helped us with many things such as improving our searches and experience for the guests.
What needs improvement?
We could benefit from refining the machine learning models that we currently use in Elastic Search, along with the possibility to integrate agents, intelligent artificial intelligence, form of agent, and MCP.
It would be useful to include an assistant into Kibana for recommendations, advice, tutorials, or things that can help improve my daily work with Elastic Search.
For how long have I used the solution?
I have been using Elastic Search and Kibana for about four years.
What do I think about the stability of the solution?
In my experience, Elastic Search is quite stable.
What do I think about the scalability of the solution?
The scalability of Elastic Search is very good in my opinion. It never has incidents that cause issues in our daily tasks.
How are customer service and support?
The customer support for Elastic Search is one of the best I have ever tried. Whenever I had to create a new incident, I got the responses that I needed.
How would you rate customer service and support?
What other advice do I have?
I consider Elastic Search a very good project. On a scale of 1-10, I would give it a 10.
The features and capabilities that Elastic Search provides are very easy to use, and the documentation is rich. You can find and understand everything here to use it properly.
I would tell others looking into using Elastic Search that they can try it and see if it fits their use cases.
Elastic Search is a very good product. I really appreciate all the features that it provides, and I hope this product continues its evolution in the way it has been.
Which deployment model are you using for this solution?
Private Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Unlocking the Power of Data with Fast Search and Analytics
What do you like best about the product?
1. Near real-time search
2. Hugh Scalability
3. In our scenario, it helps us to centralize logs and metrics from different systems into one searchable platform, helping our IT ops and security teams troubleshoot issues quickly.
4. It supports full-text search, filters, geospatial queries, and many more, all in the same engine.
What do you dislike about the product?
1. High resource usage - It is high CPU and memory hungry product.
2. It is quite expensive and complex to manage at scale
What problems is the product solving and how is that benefiting you?
1. It collects logs, metrics, and traces from apps, servers, firewalls, etc. into one platform.
2. It provides real-Time Analytics
3. Root cause analysis in minutes, doesn't take hours/days.
4. Centralized SIEM-like function for threat visibility.
5. Can handle increasing data from Yotta’s hyperscale environment.
6. Elasticsearch turns raw data into actionable insights in real-time — helping us run, secure, and scale our datacenter operations with speed and confidence
My Experience with Elasticsearch
What do you like best about the product?
Elasticsearch is awesome for fast and flexible search. It’s great at handling huge amounts of data and giving near-instant results. You can search, filter, and analyze text, numbers, logs pretty much anything. It’s super helpful for building search engines, monitoring systems, and real-time dashboards. Speed, scalability, and powerful full-text search.
What do you dislike about the product?
Elasticsearch is powerful but not always easy. It can throw errors that are hard to trace, especially with complex queries. Setup and scaling take effort, it uses a lot of resources, and security features are limited unless you pay.
What problems is the product solving and how is that benefiting you?
Elasticsearch helps spot errors and inaccurate X-ray details quickly. It makes it easy to track which technologist used single, double, or triple exposures. The data is searchable and organized, so issues and patterns are easier to find and fix.
Elasticsearch for Observability
What do you like best about the product?
We run Elastic on Kubernetes and have run up to 20 separate elastic clusters throughout the world. Three years ago we invested in the ECK operator and consolidated 9 of those elastic clusters down to 3. ECK has increased our confidence to run fewer, larger environments with multiple node pools and greater flexibility.
What do you dislike about the product?
Elasticsearch can be a bear to tune for performance, at scale, on a budget. Like any database, it's resource hungry. Additionally, the company has work to do to keep up with modern-day vulnerability management practices and remediation schedules.
What problems is the product solving and how is that benefiting you?
As a platform team, we deploy self-hosted Elastic in multiple configurations: vector search, observability, SIEM, and unstructured document storage.
AI Logging Power House
What do you like best about the product?
The bulk logging features and an ability to index, store and search data with ease
What do you dislike about the product?
Complexities involved in having ready out of the box solution for deep dive Observability and log based metrics and insights.
What problems is the product solving and how is that benefiting you?
A single Logging Repository store for IOT workloads and thousands of stateless infra elements used in our product architecture.
Don't run production workloads without Elastic's observability stack
What do you like best about the product?
Elasticsearch's stack is a must-have for application developers where observability can be achieved through APM's distributed tracing, and logs and metrics acquired through the Elastic Agent. A lot of observability into the system can be seen with minimal application configuration so developers can understand latency, throughput, error rate, and saturation of the system. I wouldn't run a production service without Elastic. I use APM every day to monitor the health of services I'm responsible for. A lot of valuable information comes for-free, but creating custom dashboards is also available.
What do you dislike about the product?
Setting up Elasticsearch and running it for production workloads is non-trivial. Many valuable features require a commercial license.
What problems is the product solving and how is that benefiting you?
Elasticsearch provides observability solutions where keeping applications running in a healthy state is critical. Tools within Elastic like Transforms can create views/dashboards that power decision making.
Nice product
What do you like best about the product?
Easy of use, reliable and good customer support
What do you dislike about the product?
Dashboard with using big index takes time to load
What problems is the product solving and how is that benefiting you?
Showing visualization from the data
Elasticsearch – Fast, Flexible, but Needs Care
What do you like best about the product?
I’ve been using Elasticsearch for a while now, and the first thing that consistently impresses me is its speed. No matter if I’m searching through logs, text, or analytics data, it delivers results incredibly quickly once it’s properly configured. I also like how well it scales; adding more nodes allows it to handle larger and larger workloads smoothly.
I also appreciate its flexibility. Elasticsearch supports everything from simple keyword searches to more advanced aggregations, autocomplete, and even fuzzy matching.
What do you dislike about the product?
Elasticsearch is not particularly plug-and-play. There is a noticeable learning curve, especially when it comes to configuring clusters, tuning shards and replicas, and maintaining stable performance as your data volume increases. If you don't size your setup correctly, it can also become quite resource-intensive.
What problems is the product solving and how is that benefiting you?
I mainly use Elasticsearch as an enterprise search tool. It’s where we send a ton of data — logs, records, documents — so people can quickly find what they’re looking for. Instead of digging through raw databases, we can just search and get results instantly.
Before Elasticsearch, searching across big datasets was slow and frustrating. Now it’s basically instant. It handles millions of records without breaking a sweat, and the results are super accurate.
The biggest win for us is speed and scale — things that used to take forever now take seconds. That means faster troubleshooting, better insights, and less wasted time for the team. It just makes working with large amounts of data way more practical.
Scalable, Reliable, and Insightful Platform for Search and Observability
What do you like best about the product?
As a Lead Solutions Architect, I've worked extensively with Elastic over the past few years, and it has become a cornerstone of our infrastructure. From log aggregation to real-time analytics and observability, Elastic consistently delivers high performance and flexibility.
We use Elasticsearch to power dashboards that process large volumes of data from various sources, including MySQL and Elastic Search itself. The ability to create custom indexes, mappings, and use REST APIs like Bulk and Multi Get has made our data ingestion and retrieval seamless. The platform’s support for metrics and aggregations has helped us build meaningful visualizations and improve operational decision-making.
Elastic’s integration with cloud platforms like Azure and AWS has been smooth. We've deployed Elastic Stack in production environments and leveraged its capabilities for distributed search, logging via Logstash, and visualization through Kibana. The training materials and internal documentation have been instrumental in onboarding new team members and scaling our usage.
What stands out most is Elastic’s commitment to innovation. Their recent push into Search AI and generative AI-powered applications, as highlighted in Elastic{ON} events , shows they’re not just keeping up—they’re leading.
Pros:
Powerful search capabilities with support for vector and semantic search
Scalable architecture for large datasets
Seamless integration with cloud and container platforms
Excellent visualization tools via Kibana
Strong community and documentation
Cons:
Initial setup and tuning can be complex for new users
Licensing and pricing models could be more transparent
What do you dislike about the product?
Cons:
Initial setup and tuning can be complex for new users
Licensing and pricing models could be more transparent
What problems is the product solving and how is that benefiting you?
Faster Incident Response
You can quickly search logs and metrics to identify and resolve issues—minimizing downtime and improving MTTR (Mean Time to Recovery) .
Enhanced System Reliability
By leveraging Elasticsearch’s real-time capabilities and redundancy planning, you ensure that services remain available and performant even under stress .
Cost-Efficient Operations
Tools like LogsDB and Elastic Cloud Serverless reduce operational overhead and hidden costs, allowing you to store more data affordably while maintaining visibility.
Smarter Automation
Elasticsearch integrates well with automation pipelines (e.g., Logstash, Kibana), enabling you to automate routine tasks like log parsing, alerting, and dashboard generation.
Future-Proofing with AI
Elastic’s innovations in Search AI and GenAI observability empower you to monitor and optimize AI workloads, which is increasingly relevant in modern SRE practices.
