

Prometheus-AI Platform and Cribl are notable competitors in the monitoring solutions category. Prometheus has the upper hand in integration flexibility and extensive customization, while Cribl excels in data management with advanced log and data routing capabilities.
Features: Prometheus integrates seamlessly with diverse environments due to its flexible APIs, emphasizing robust metric collection. It supports scalability, particularly for those using Kubernetes. Cribl offers powerful real-time data transformation and routing, efficiently handling high data volumes. Its sophisticated data processing and log management capabilities are designed for extensive data environments.
Room for Improvement: Prometheus could enhance its visualization features and simplify its setup and query language to be more accessible for non-technical users. It would also benefit from expanding database support and improving stability. Cribl could improve its user interface flexibility, enhance internal logging, and expand compatibility with legacy infrastructures to support broader use.
Ease of Deployment and Customer Service: Prometheus is widely used for its deployment flexibility across on-premises and cloud environments. It relies on community-driven support due to its open-source nature. Cribl offers structured deployment options across various cloud environments, providing comprehensive customer support and managed services.
Pricing and ROI: Prometheus, being open-source, reduces direct costs and offers significant ROI by avoiding extra managed service expenses. Cribl, while more costly, is reasonably priced for its robust data management. It provides strong ROI for enterprises needing efficient log management, leveraging innovative data reduction to offset licensing costs.
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.
Using open-source Prometheus saves me money compared to AWS native services.
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.
Prometheus does not offer traditional technical support.
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.
Prometheus is scalable, with a rating of ten out of ten.
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.
Deploying it on multiple instances or using Kubernetes for automatic management has enhanced its stability.
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.
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.
Prometheus is cost-effective for me as it is free.
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.
It allows me to save money by avoiding costs associated with AWS native services like CloudWatch or Amazon Prometheus.
| Product | Market Share (%) |
|---|---|
| Cribl | 41.1% |
| DataBahn | 13.3% |
| Onum | 12.7% |
| Other | 32.89999999999999% |
| Product | Market Share (%) |
|---|---|
| Prometheus-AI Platform | 1.7% |
| IBM Maximo | 15.4% |
| Oracle Enterprise Asset Management | 8.5% |
| Other | 74.4% |


| Company Size | Count |
|---|---|
| Small Business | 9 |
| Midsize Enterprise | 5 |
| Large Enterprise | 20 |
| Company Size | Count |
|---|---|
| Small Business | 13 |
| Midsize Enterprise | 8 |
| Large Enterprise | 13 |
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.
Prometheus-AI Platform offers flexible solutions for collecting, visualizing, and comparing metrics, appreciated for its scalability, rich integrations, and open-source adaptability.
Prometheus-AI Platform provides a reliable framework for monitoring and analyzing metrics across diverse environments. With extensive API support, it supports data collection, querying, and visualization, integrating seamlessly with tools like Grafana. High availability, scalability, and lightweight configuration make it suitable for traditional and microservice environments, while community support enhances its utility. Though its query language and interface require improvements for better ease of use, and with calls for stronger integration options, the platform remains a leading choice for comprehensive metric analysis.
What are Prometheus-AI Platform's main features?Companies leverage Prometheus-AI Platform across various industries, utilizing it to monitor and analyze metrics from applications and infrastructure. It is extensively used in financial services and IT sectors for collecting, scraping logs, and monitoring Kubernetes deployments. Deployed both on-premise and in cloud environments like Azure and Amazon, it supports system and application metrics analysis, ensuring a comprehensive view for developers.
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