Elasticsearch Reviews & Product Details

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Elasticsearch is a fantastic search and analytics platform. It’s easy to use as a SIEM tool, and creating exceptions is straightforward. I really appreciate the ECS field schemes, the agent/fleet/integrations setup, and the quality of support. These features make the platform flexible and enjoyable to work with.

i use elastic every day with our siem

it's easy to setup without certificates Review collected by and hosted on G2.com.

The documentation could be improved—especially around “detection as code,” which is difficult to set up and barely documented. Having “exceptions as code” would also be a great addition. I miss certain features that competitors like Wazuh provide, such as a built-in vulnerability scanner. Another gap is the lack of community-driven blogs and integration examples (like those published on Medium by SOCFortress for Wazuh). Finally, I find it strange that certain wildcard searches (e.g., *test* across large datasets like Palo Alto logs) can crash the entire stack.

i would expect for small bussiness, there should be an automatic rotation and trust for certificates between clients and fleet server, our between nodes. Review collected by and hosted on G2.com.

Elasticsearch is extremely fast, scalable, and reliable for handling large amounts of data. I’ve used it extensively for log management, search queries, and analytics, and it consistently delivers results in near real-time. Its flexibility with queries, index lifecycle management, and clustering makes it an essential part of our infrastructure. The ecosystem around Elasticsearch (APIs, integrations, documentation) makes it easy to extend and adapt to different use cases. Review collected by and hosted on G2.com.

Managing clusters at scale can sometimes be challenging, especially around balancing shards, force merge operations, and handling 429 rate-limit responses. While it’s very powerful, certain advanced operations require deep knowledge to avoid performance bottlenecks. That said, once tuned properly, it works extremely well and reliably. Review collected by and hosted on G2.com.

I use Elastic on a daily basis, and the visualization and log exploration features are very enjoyable and user-friendly once you get to know the solution. Fleet allows for a simple way to add agents, even in offline implementations, and the documentation in this context is very good. Elastic SIEM is also pleasant to use, but it’s important to keep in mind the retention of Elastic events and processes, as they can take up a lot of storage. The support is very good, although the AI chat is not always useful since it can sometimes point to outdated articles. Review collected by and hosted on G2.com.

Overall, I’m very satisfied with Elastic, but the biggest downside for me is the documentation. It’s often unclear or incomplete, especially when it comes to Elastic Agent and all the integrations. This makes setup and troubleshooting more complicated than it should be. One of the challenges I faced is with log parsing in the TCP custom input integration. The documentation is not very clear, and it’s not always obvious which preprocessors can be used or how to configure them properly. Of course, I should be using pipelines, but since Elastic provides such a solution, it should be properly documented. Sometimes even when debugging pipelines, not everything is clear or easy to understand. Review collected by and hosted on G2.com.

APM feature, I like the APM feature in Elastic which helps to identify the endpoints failing or services which were not healthy at any point of time. The way it shows the failure transaction, latency throughput and mapping with services is useful in my daily works. The dependencies feature is great addon to identify what other services are being affected due to the issue. Review collected by and hosted on G2.com.

Searching for aged logs. In one of our clusters, it is hard for us to get the aged logs when we search with any pattern. Don't think this is fully due to Elastic it has more to do with our logs and tier configuration too. Also getting the logs and metrics of database server is something I feel hard. Review collected by and hosted on G2.com.

Elasticsearch provides extremely fast and powerful search capabilities, even on very large datasets. I like how flexible it is with indexing and querying structured as well as unstructured data. Its ability to handle full-text search, filtering, and aggregations makes it ideal for analytics and real-time monitoring. Integration with Kibana adds strong visualization support, helping us easily explore trends and patterns. The distributed nature of Elasticsearch ensures scalability, making it suitable for high-volume production systems. It is also very easy to integrate with different applications and data pipelines, which makes adoption smooth across teams. Implementation is straightforward, with clear documentation and community support that reduces the learning curve. Customer support is also excellent. In my organization, we use it very frequently as all the logs, service traces, and errors are centralized in Elasticsearch for debugging and monitoring. Review collected by and hosted on G2.com.

While Elasticsearch is powerful, it can be resource-intensive and requires careful configuration to avoid performance bottlenecks. Setting up clusters and managing shard allocation can sometimes be tricky for beginners. Query syntax, while flexible, can feel complex for new users. Also, as the data size grows, managing indexes and optimizing queries requires ongoing effort. Review collected by and hosted on G2.com.

I like best how Elasticsearch handles large amounts of data in a scalable and efficient manner, making it easier to manage and scale as my data grows. Additionally, the extensive community support and integration with other tools make it a valuable addition to my data processing workflow. Review collected by and hosted on G2.com.

While I appreciate the many benefits Elasticsearch provides, some drawbacks include the steep learning curve, potential complexity in management and maintenance, and the risk of performance overhead. Additionally, ensuring security configurations and managing data consistency issues are crucial to avoiding potential data loss or corruption. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

1. High resource usage - It is high CPU and memory hungry product.

2. It is quite expensive and complex to manage at scale Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.

Cons:

Initial setup and tuning can be complex for new users

Licensing and pricing models could be more transparent Review collected by and hosted on G2.com.