Elastic Observability and Cribl compete in the observability solutions category, with Elastic offering a broader feature set while Cribl excels in real-time data handling capabilities, making them stand out in different aspects as per user assessments.
Features: Elastic Observability provides an open-source platform enriched with machine learning features, comprehensive logging, and integrated tools for alerting, which are particularly beneficial for large-scale deployments. Cribl, on the other hand, is known for its real-time data transformation within pipelines, flexible data routing, and easy plugin configurations, allowing for efficient handling of large data volumes.
Room for Improvement: Elastic Observability needs improvements in automation and better visualization, along with enhanced APM capabilities. Users also indicate that its support system needs broadening to incorporate a wider user base. Cribl could benefit from enhancements in documentation, additional custom packs for log management, and addressing legacy integration issues, in addition to refining its versioning system for improved user accessibility.
Ease of Deployment and Customer Service: Both Elastic Observability and Cribl support cloud and hybrid environments effectively. Elastic boasts strong documentation and community support, whereas Cribl receives positive ratings for its technical support. Elastic's widespread community offers additional resources, enhancing its troubleshooting capabilities.
Pricing and ROI: Elastic Observability offers cost-effective solutions ideal for large enterprises, providing good ROI by improving incident response times. However, its pricing may not be as favorable for smaller users. Cribl is noted for being budget-friendly, especially compared to competitors like Splunk, with a pricing model that proves cost-effective for managing larger data requirements.
In the case of optimization, it has helped return on investment to somewhere close to 50%.
we have saved a significant amount of time and resources moving from a manual approach to something that's more automated.
They had extensive expertise with the product and were able to facilitate everything we needed.
If they could enhance their internal logging, we won't require Cribl support to engage.
The community, including the engineering and sales teams, is available on Slack and is very supportive.
Elastic support really struggles in complex situations to resolve issues.
It's an enterprise version, and we have a good amount of users using this solution.
I don't need to talk to a Cribl engineer to connect a new log source.
Cribl is quite scalable, as we could add worker nodes as our data grows.
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.
What is not scalable for us is not on Elastic's side.
I would rate the stability as ten out of ten.
If the pipeline is down and we receive an alert that it's not sending information to the log collection platform for more than one or two hours, if we receive an alert, it would be great.
Cribl is quite stable and doesn't crash; there's no unusual behavior.
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.
Elastic Observability is really stable.
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.
Since Cribl is such a large platform with numerous features, having a clear, structured approach would make it easier for me and others to understand and utilize its capabilities.
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.
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 community on Slack is excellent for solving questions and getting ideas.
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 | 3.9% |
Cribl | 1.1% |
Other | 95.0% |
Company Size | Count |
---|---|
Small Business | 9 |
Midsize Enterprise | 4 |
Large Enterprise | 8 |
Company Size | Count |
---|---|
Small Business | 8 |
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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