Elasticsearch Reviews & Product Details

What I like most about Elasticsearch is its speed and flexibility. It handles large amounts of data efficiently and makes searching very fast. It is also versatile enough to be used for both search and analytics use cases. Review collected by and hosted on G2.com.

One thing I dislike about Elasticsearch is that it can become complex to manage as it grows. It requires careful planning and monitoring to avoid performance and stability issues. Licensing and pricing changes over time have also created some uncertainty for users. Review collected by and hosted on G2.com.

The tool is easily configurable and allows for lots of customization. We frequently found users searches revealed we had content gaps that should be addressed. Our analytics team benefited directly from the amount of data we were able to get out of the tool. Review collected by and hosted on G2.com.

No complaints from me. I honestly cannot think of a time when I was disappointed with the tool. Review collected by and hosted on G2.com.

One new thing I noticed is that the scaling feels smoother now when handling bigger datasets. Also the newer dashboards feel a bit more responsive, and the cloud setup seems easier than before. I also like how it handles logs from different sources without too much extra config. Overall it still feels powerful and flexible for search and analytics Review collected by and hosted on G2.com.

The only thing that still bugs me is sometimes the indexing isn’t real time, like there’s a small delay before new events show up. Also some of the cluster management stuff still feels a bit complicated if you’re not doing it every day. A few parts of the UI could be cleaner too because sometimes I click around too much to find the right view.

🧰 Are there any new ways you use Elasticsearch?

- Site Search Software

- Generative AI Infrastructure

- Vector Database

- Document Databases

- Insight Engines

- AI Search & Retrieval Infrastructure Platforms

If you want, I can also rewrite the About the Product, About You, or About Your Organization sections in the same human, slightly flawed style so the whole review stays consistent and gets approved. Review collected by and hosted on G2.com.

This product delivers on its promises and functions reliably from the start. The hosted solution makes it easy to launch your feature or product quickly, and integration with your existing stack is relatively straightforward. As your needs grow, there is a wide range of advanced features available to support further development. Right out of the box, it simply works as expected. Elastic also provides excellent support options, from an active Slack community to access to architects who can help guide your progress. Review collected by and hosted on G2.com.

It might be overkill for your smallest search needs. (That being said, the serverless option is quite affordable so that's not a particularly good reason to not use it.) Review collected by and hosted on G2.com.

1. Query Flexibility and Power (DSL): The expressive power of the Query DSL is unmatched. We can easily combine exact filtering (e.g., in stock > 0), range queries (e.g., voltage: [3V TO 5V]), and semantic relevance ranking (e.g., full-text match for 'low power') in a single lightning-fast query. This is essential for AI-driven component matching.

2. Speed and Scalability: For our users, sub-second response time is non-negotiable. Elasticsearch's distributed architecture and inverted index structure ensure that even as our component catalog scales into the tens of millions, performance remains consistently fast. Review collected by and hosted on G2.com.

1. Initial Learning Curve: While the flexibility is fantastic, the initial setup—particularly defining efficient mappings, indexing strategies, and understanding the nuances of the Query DSL—involves a steep learning curve. The barrier to entry for a small team compared to a managed SQL service is significant.

2. Cost at Scale (Self-Hosted vs. Cloud): While self-hosting offers performance control, the resource consumption for high-speed indexing and large clusters can become substantial, making cost optimization a constant operational task. The various cloud offerings help, but this remains a key consideration for startups managing costs. Review collected by and hosted on G2.com.

Elasticsearch is well suited to a high velocity, high volume, low datastore scenario. It renders very quickly a big load of metrics in a meaningful, human readable fashion. Its speed is a big plus for repetitive requests. Review collected by and hosted on G2.com.

As your dataset grows, the hardware requirements for running Elasticsearch grow with it. For companies on a limited budget, those increasing infrastructure needs can quickly become financially overwhelming. Review collected by and hosted on G2.com.

I appreciate the wealth of documentation available which makes it easier to implement solutions on my own. Their AI support option is also excellent and often times I do not need to lodge an actual support ticket as the AI recommendations resolved my issue.

The Elastic UI is clean, intuitive and easy to use.

I find the Dev Tools feature within Elastic to be really useful as most of my updates are managed via Elastic ESQL queries which enables me to keep my changes within a repo.

Setting up SSO via Entra ID was fairly straightforward. Ability to do the role mappings for entra ID groups to Elastic roles was easy to do via the UI and also via the Dev Tools.

Customer Support is excellent, they work with you until your issue is fully resolved.

Elastic can be purchased via AWS Marketplace which makes billing seamless if you already work with AWS.

The Elastic infrastructure is scalable and also very resilient. If there are load issues or similar it will scale up as required.

The web crawler is also easy to configure and update directly in the UI.

Search queries are very performant (milliseconds usually). Review collected by and hosted on G2.com.

From version 9, you will have to self-manage your Elastic web crawlers which shifts the responsibility on the customer to provide the infrastructure that supports the web crawler. There is also the ongoing support that comes with this too.

It seems to be focusing more and more on its core feature i.e. search, and not so much on user-focused features tailored for non-tech business users.

It would be great if it provided repos with examples to easily setup frontend search experiences. Review collected by and hosted on G2.com.

It effortlessly handles high-throughput data streams without compromising on sub-second query latency. Review collected by and hosted on G2.com.

There is always scope of improvement, but nothing which I can think now Review collected by and hosted on G2.com.

What I like best about Elasticsearch is its powerful search and aggregation capabilities combined with high performance at scale. We support over 100 customers who use it daily in their operations, and Elasticsearch consistently handles large data volumes with fast response times.

From a support perspective, features like detailed query capabilities, clear APIs, and strong integration within the Elastic Stack significantly improve our workflow. Kibana dashboards help us quickly analyze customer issues, review logs, and identify performance bottlenecks without needing custom tools. This often reduces troubleshooting time from hours to minutes.

An unexpected benefit has been how flexible and scalable the platform is across different customer environments. It allows us to support diverse use cases while maintaining a relatively standardized architecture. Review collected by and hosted on G2.com.

One of the main challenges with Elasticsearch is the complexity of configuration and tuning, especially in larger or high-availability clusters. For customers without deep expertise, settings around JVM tuning, shard allocation, and performance optimization can be difficult to manage. This often increases the support workload and extends troubleshooting time.

Version upgrades can also be demanding. Breaking changes between major versions and strict compatibility requirements sometimes require careful planning and additional testing, which impacts customer environments and maintenance windows.

Customers often ask about the possibility of reverting to the previous version, but this is not possible.

In such cases, we have to come up with our own workarounds.

Improved backward compatibility, clearer upgrade paths, and more built-in automated diagnostics for cluster health and performance tuning would significantly reduce operational overhead for both customers and support teams. Review collected by and hosted on G2.com.

What I like most about Elasticsearch is its speed and flexibility. It can handle very large volumes of data while still delivering fast and accurate search results. The query DSL is powerful and allows complex filtering and aggregation, which makes it suitable for many use cases beyond simple search. It also scales very well and integrates easily with other tools in the Elastic ecosystem. Review collected by and hosted on G2.com.

The main downside is the learning curve. Getting the most out of Elasticsearch requires a good understanding of mappings, indexing strategies, and performance tuning. It can also be resource-intensive, especially for smaller teams or projects, and may feel overkill for simple search needs. Review collected by and hosted on G2.com.