Great SIEM, security product
What do you like best about the product?
elastic is always improving their products and integrating more AI int their suite of products
What do you dislike about the product?
documentations can get better about newer products.
What problems is the product solving and how is that benefiting you?
elastic's edr is helping us to secure our environment even better, and having a unified all in product to look at the logs ingestion and edr
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?
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.
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.
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.
Amazing solution for introducing AI search with great company support
What do you like best about the product?
The tool is comprehensive yet still approachable and well documented for all configuration needs.
What do you dislike about the product?
Creating a support case does not always lead to quickly talking to a domain expert and it's often better to go through the sales engineer for help.
What problems is the product solving and how is that benefiting you?
Enterprise data search and monitoring/logs
Elasticsearch Review
What do you like best about the product?
- Reliable at scale: sharding and replication deliver solid HA; rolling restarts and node recovery are predictable when procedures are followed.
- Great for observability: fast searches/aggregations and the Elastic stack make log/metrics/APM pipelines effective for troubleshooting.
- Good ops surface: rich APIs and CAT endpoints make it scriptable, monitorable, and easy to automate runbooks.
What do you dislike about the product?
- Finicky to run well: JVM/heap sizing, shard counts, and segment merges need care—or they bite during peak.
- Changes can be risky: upgrades, reindexing, and rebalances can cause latency spikes without tight change control.
- Costly footprint: hot nodes are CPU/IO heavy; replicas and long retention drive storage costs; licensing/features add complexity.
What problems is the product solving and how is that benefiting you?
Elasticsearch lets us deliver fast, relevant search and discovery across our articles. New articles become searchable within seconds, and aggregations power features like “most read,” topic pages, and related-article widgets. Flexible analyzers handle titles, body text, tags, and authors without rigid schemas, while replicas keep article search available during node failures. Net result: lower query latency, higher reader engagement, and a simpler path from article publish to discovery.
Elasticsearch at big belgian bank
What do you like best about the product?
API, dev console, schema-less indexing, documentation
What do you dislike about the product?
changing java sdk, hiding some previously available features behind a subscription
What problems is the product solving and how is that benefiting you?
excellent vector search, good indexing/search performance, support complex searches
The solution is modern and feature rich with extensive customization possibilities
What do you like best about the product?
The amount features present and you can do many custom things with it if something is not present out of the box, we really like the security monitoring features it provides
What do you dislike about the product?
Maintaining self managed deployments can be difficult, mapping conflicts and slow downs when ingesting many log sources can take a lot of time.
What problems is the product solving and how is that benefiting you?
Log collection and threat monitoring
