

Elastic Observability and Cribl are key players in the observability and data management space. While Elastic Observability is recognized for its comprehensive toolset and scalability, Cribl stands out with cost-efficient data transformation capabilities.
Features: Elastic Observability offers centralized logging with Elastic Common Search, machine learning enhancements, and a rich suite of features available on the cloud. Its open-source foundation promotes backend logging and allows for extensive customization. Cribl excels in data transformation and integration, offering a flexible platform for real-time log routing, collection, and processing. Its pricing model supports cost-efficient handling of large data volumes, enhancing performance.
Room for Improvement: Elastic Observability could improve its automation, visualization, and monitoring capabilities, as current manual processes can be cumbersome. Future developments should aim at reducing complexity through enhanced machine learning and AI features. Cribl faces challenges integrating with legacy infrastructures and could benefit from improved logging, internal management, and backward compatibility. Enhancing alert mechanisms and historical data management could also increase Cribl's reliability.
Ease of Deployment and Customer Service: Elastic Observability supports various deployment environments including on-premises and hybrid clouds, with strong documentation and generally responsive customer support. However, complex configurations can hinder initial deployment. Cribl excels with its user interface and adaptive customer support, efficiently addressing technical issues. It offers broad deployment options across public, private, and hybrid clouds, emphasizing customer satisfaction through flexible architecture.
Pricing and ROI: Elastic Observability is typically more costly than Cribl but offers extensive features and scalability which may justify the expense for large enterprises. Its often complex pricing model, based on Elastic Search memory, can complicate cost projections. Despite its price, Elastic delivers favorable ROI due to its high utility visibility features. Cribl is noted for cost-effectiveness, offering significant savings in data handling and reduced licensing costs compared to competitors like Splunk. Its transparent pricing and efficient data processing provide strong ROI, making it appealing for organizations handling large data volumes.
What we've seen is really an overall reduction of just shy of 40% in our ingest into our SIM platform versus prior to having Cribl.
The second thing is that data aggregation, sampling, and reduction that we're able to do of the data, lowering our overall data volume, both traversing the network as well as what's being stored inside of our final solutions.
In terms of reduction, we were able to save almost ~40% of our total cost.
Elastic Observability has saved us time as it's much easier to find relevant pieces across the system in one screen compared to our own software, and it has saved resources too since the same resources can use less time.
They had extensive expertise with the product and were able to facilitate everything we needed.
Usually, within an hour, we get a response, and we are able to work with them back and forth until we resolve the issues.
Sometimes by hearing the problem itself, they will know what the solution is, and they will let us know how to resolve it, and we do it immediately.
Elastic support really struggles in complex situations to resolve issues.
Their excellent documentation typically helps me solve any issues I encounter.
The infrastructure behind Cribl Search is also scalable as it uses a CPU and just spawns horizontally more instances as it demands and requires.
Compared to other SIEM tools I use, any slight change on the operating system end impacts a lot on our SIEM tools and other things, but Cribl performs well in that regard.
It's an enterprise version, and we have a good amount of users using this solution.
I rate the scalability of Elastic Observability as a ten, as we have never seen issues even with a lot of data coming in from more customers, provided we have the appropriate configuration.
Elastic Observability seems to have a good scale-out capability.
Elastic Observability is easy in deployment in general for small scale, but when you deploy it at a really large scale, the complexity comes with the customizations.
Migrating from those SC4S servers to Cribl worker nodes has truly been a game-changer.
Regarding scalability, we started with zero servers and have around 285 servers now.
I would rate the stability as ten out of ten.
There are some bugs that come with each release, but they are keen always to build major versions and minor versions on time, including the CVE vulnerabilities to fix it.
It is very stable, and I would rate it ten out of ten based on my interaction with it.
I would rate the stability of Elastic Observability as a ten, as we don't experience any issues.
A more stringent role-based access control feature would enhance security and allow granular control over what users can see and access.
If we can have more internal logs and more debug logs to validate the error, that would be beneficial because instead of reaching out to Cribl support, we can troubleshoot and find the root cause ourselves.
In terms of large datasets—whether they originated from network inputs, virtual machines, or cloud instances—ingesting the data into the destination was relatively easy.
For instance, if you have many error logs and want to create a rule with a custom query, such as triggering an alert for five errors in the last hour, all you need to do is open the AI bot, type this question, and it generates an Elastic query for you to use in your alert rules.
It lacked some capabilities when handling on-prem devices, like network observability, package flow analysis, and device performance data on the infrastructure side.
Some areas such as AI Ops still require data scientists to understand machine learning and AI, and it doesn't have a quick win with no-brainer use cases.
Over time, the licensing cost has increased.
Cribl is very inexpensive, with enterprise pricing around 30 cents per GB, which is really decent.
They have a universal license that allows us to consume the portions of Cribl that we want to use or flex into other portions of Cribl.
The license is reasonably priced, however, the VMs where we host the solution are extremely expensive, making the overall cost in the public cloud high.
Elastic Observability is cost-efficient and provides all features in the enterprise license without asset-based licensing.
Observability is actually cheaper compared to logs because you're not indexing huge blobs of text and trying to parse those.
The data reduction and preprocessing capabilities make Cribl really unique.
Cribl has a feature called JSON Unroll or Unroll function that allows you to differentiate the events; each event will come ingested as a single log instead of piling it up with multiple events.
The Cribl UI is very simple and easy to use, particularly when working with data from various sources; it makes it very easy to create pipelines, add complex logic to those pipelines, and then gives you a preview of what your data looks like before applying that pipeline and what you get after.
The most valuable feature is the integrated platform that allows customers to start from observability and expand into other areas like security, EDR solutions, etc.
the most valued feature of Elastic is its log analytics capabilities.
All the features that we use, such as monitoring, dashboarding, reporting, the possibility of alerting, and the way we index the data, are important.
| Product | Market Share (%) |
|---|---|
| Elastic Observability | 2.6% |
| Cribl | 1.2% |
| Other | 96.2% |


| Company Size | Count |
|---|---|
| Small Business | 9 |
| Midsize Enterprise | 5 |
| Large Enterprise | 18 |
| Company Size | Count |
|---|---|
| Small Business | 9 |
| Midsize Enterprise | 4 |
| Large Enterprise | 16 |
Cribl offers advanced data transformation and routing with features such as data reduction, plugin configurations, and log collection within a user-friendly framework supporting various deployments, significantly reducing data volumes and costs.
Cribl is designed to streamline data management, offering real-time data transformation and efficient log management. It supports seamless SIEM migration, enabling organizations to optimize costs associated with platforms like Splunk through data trimming. The capability to handle multiple data destinations and compression eases log control. With flexibility across on-prem, cloud, or hybrid environments, Cribl provides an adaptable interface that facilitates quick data model replication. While it significantly reduces data volumes, enhancing overall efficiency, there are areas for improvement, including compatibility with legacy systems and integration with enterprise products. Organizations can enhance their operational capabilities through certification opportunities and explore added functionalities tailored towards specific industry needs.
What are Cribl's most important features?Cribl sees extensive use in industries prioritizing efficient data management and cost optimization. Organizations leverage its capabilities to connect between different data sources, including cloud environments, improving both data handling and storage efficiency. Its customization options appeal to firms needing specific industry compliance and operational enhancements.
Elastic Observability offers a comprehensive suite for log analytics, application performance monitoring, and machine learning. It integrates seamlessly with platforms like Teams and Slack, enhancing data visualization and scalability for real-time insights.
Elastic Observability is designed to support production environments with features like logging, data collection, and infrastructure tracking. Centralized logging and powerful search functionalities make incident response and performance tracking efficient. Elastic APM and Kibana facilitate detailed data visualization, promoting rapid troubleshooting and effective system performance analysis. Integrated services and extensive connectivity options enhance its role in business and technical decision-making by providing actionable data insights.
What are the most important features of Elastic Observability?Elastic Observability is employed across industries for critical operations, such as in finance for transaction monitoring, in healthcare for secure data management, and in technology for optimizing application performance. Its data-driven approach aids efficient event tracing, supporting diverse industry requirements.
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