Elastic Stack Reviews & Product Details

High-Speed Search: The search engine (Elasticsearch) is lightning-fast at processing and retrieving massive datasets.

Real-time Analytics: Capable of monitoring, collecting, and analyzing log or metric data in real-time.

Interactive Visualization: Highly responsive and customizable dashboards (Kibana) to turn raw data into visual charts.

Flexible Data Ingestion: Easily collects, parses, and transforms data from various servers and applications (Logstash & Beats).

High Scalability: Built on a distributed architecture, making it easy to scale horizontally as data volumes grow.

Versatile Use Cases: Ideal for various needs, including IT infrastructure monitoring, cybersecurity (SIEM), and e-commerce application search. Review collected by and hosted on G2.com.

High Complexity: Difficult to install, configure, and maintain, requiring a team with specialized technical expertise.

Resource Intensive: Demands significant hardware resources (especially RAM, CPU, and storage) to run smoothly and reliably.

Cost at Scale (Pricing): While the base software is free, the infrastructure costs to handle massive data or enterprise licenses for premium features (like advanced security and machine learning) are very expensive.

Maintenance Overhead: Keeping the cluster stable during data spikes or network issues requires continuous monitoring and heavy administrative effort. Review collected by and hosted on G2.com.

I like how Elastic Stack brings logs, search, and visualizations together in one place. It makes it easy to filter through large volumes of logs, troubleshoot issues, and build dashboards to monitor application health without having to rely on multiple tools. Review collected by and hosted on G2.com.

Setting up and maintaining Elastic Stack can be complex, especially when handling large volumes of logs. It can also consume significant memory and storage, and tuning queries and index settings often requires some hands-on experience. Review collected by and hosted on G2.com.

What I like best about Elastic Stack is its ability to handle massive volumes of log data in real time. Elasticsearch provides fast indexing and search, while Kibana makes visualization intuitive and powerful. The stack integrates seamlessly with DevOps workflows, enabling proactive monitoring, anomaly detection, and performance optimization. This combination of scalability, speed, and flexibility makes it invaluable for system reliability and observability. Review collected by and hosted on G2.com.

Elastic Stack can be complex to configure and requires significant tuning for large-scale deployments. Scaling clusters is resource-intensive, and some advanced features are only available under commercial licensing. Better documentation for integrations would make adoption smoother. Review collected by and hosted on G2.com.

Elastic is always my go-to product when it comes to big queries and tracking user events, because it makes it easy to debug the issues users have faced, with great performance and real-time updates. Review collected by and hosted on G2.com.

The events appear to be shown as raw JSON, which makes them very hard to understand at a glance. This could be improved with some UI updates in the product so the event details are easier to read and interpret. Review collected by and hosted on G2.com.

As a Scrum Master, my focus is completely on team productivity and keeping our development cycles moving. What I like best about Elastic Stack (specifically the Elasticsearch and Kibana combo) is how quickly it allows my developers to root-cause issues during production incidents. Before we centralized our logs, a production bug meant devs had to manually SSH into individual servers and comb through raw text files, which completely killed our sprint momentum.

Now, everything across our microservices is funneled into Kibana. The search speed even when looking through millions of raw data points is near-instant. Being able to save search patterns and instantly share specific log URLs across Slack allows our devs to collaborate seamlessly and close out hotfixes within the same sprint. Review collected by and hosted on G2.com.

The primary headache is the steep learning curve for non-technical team members and the maintenance overhead. While the developers can write complex Lucene or KQL queries easily, our Product Managers and QA testers find the raw interface completely overwhelming without pre-built dashboards.

Also, from a resource management perspective, it can be a massive hog. If the team doesn't configure index lifecycle management (ILM) correctly right out of the gate, old indices will quietly sit there and inflate your cloud hosting bill or throttle cluster memory. It requires regular tuning and clean-up, which occasionally forces me to dedicate valuable story points to 'infrastructure maintenance' spikes rather than customer-facing features. Review collected by and hosted on G2.com.

I really appreciate how Elastic Stack lets me see the whole behavior of a device simply by entering the user ID. It's very simple to use, and all the logs of the device are recorded, which helps a lot in debugging issues. I use Elasticsearch to see the user logs, and I value the dashboard and analytics for showing how many are using it and the problems they might be facing. It's helpful to analyze each action performed by the user and understand how the device is behaving in production. Switching from Amplitude to Elastic Stack was beneficial because it allows me to see more detailed information, like internet strength, which helps a lot. The initial setup was quite simple, and it was a learning experience while we switched. Review collected by and hosted on G2.com.

Sometimes, I feel the UI needs to improve. The whole UI needs to be improved. I feel it's difficult to find a particular data. Review collected by and hosted on G2.com.

What I like best about Elastic Stack is its powerful search capabilities, fast log analysis, and flexible dashboards. It makes it easy to centralise data from different sources, investigate events, and build custom visualisations. The scalability and extensive integration options also make it suitable for both small and larger environments. Review collected by and hosted on G2.com.

It has a really steep learning curve. From the perspective of a small company, most of the things you actually need are locked behind a paywall. Review collected by and hosted on G2.com.

It supports complex full-text searches, structured queries, and advanced analytics like aggregations through a JSON-based RESTful API. It delivers near real-time search results, often in milliseconds, even across petabytes of data. Review collected by and hosted on G2.com.

It prioritizes fast search performance and scalability over strict data consistency, so updates may not be reflected immediately across all parts of the data. Review collected by and hosted on G2.com.

What I like most about the Elastic Stack is its centralized logging and powerful search capability. Some things I particularly like are that it allows you to quickly find a specific error or request even among millions of logs. Thanks to the Bana dashboard, it becomes easy to visually analyze logs and metrics, and filters and custom queries can be placed wherever needed. Due to real-time log ingestion, issues can be investigated immediately, and we can view logs from multiple applications and services on a single platform. For me, the foremost feature is centralized visibility; when an issue arises in production, there's no need to log into different servers to find the log. With the help of Elastic Stack, the entire investigation happens in one place, which significantly reduces the time for Dev and RCA. Review collected by and hosted on G2.com.

Yes, overall the experience with Linux tech is quite good with us, but there are some areas where I see room for improvement, such as the initial setup and configuration, which becomes a bit tricky, especially for teams using this tool for the first time. Learning the query language and advanced search feature is a bit different. In my experience, the biggest challenge is that while the platform is very capable, it is not easy for new users to fully understand it initially. Review collected by and hosted on G2.com.

I like using Elastic Stack as a single stack for security, monitoring, searching, and alerting. I find the 1000+ prebuilt rules for various integrations, like Salesforce, Windows, Linux, and Postgres, very helpful for a quick security setup. I also appreciate that I can customize these security rules to fit my needs. The initial setup was easy, just requiring an API key with the necessary permissions. Review collected by and hosted on G2.com.

I think Kibana's dashboarding could be improved by getting ideas from Grafana, which offers faster dashboards and real-time capabilities. Also, Elastic Stack should add integration to monitor LLM, their inputs, tokens, etc. Review collected by and hosted on G2.com.