Datadog and Cribl compete in the data management and monitoring category. Datadog seems to have the upper hand with its extensive integrations and monitoring capabilities, while Cribl is known for its data transformation and routing efficiency.
Features:Datadog integrates seamlessly with platforms like AWS, Docker, and Slack, providing anomaly detection, customizable dashboards, and robust alerting. Cribl offers real-time data transformation, efficient log collection, and flexible data routing that appeals to those focused on optimizing data flow.
Room for Improvement:Datadog could enhance real-time performance with older data access, improve API usability, and simplify its pricing model. Cribl needs a better alerting system, improved documentation, and enhanced support for legacy systems and logging capabilities.
Ease of Deployment and Customer Service:Datadog supports deployment across public, private, and hybrid clouds, offering flexibility, though customer service experiences can vary. Cribl excels in on-premises and hybrid solutions, with a reputation for fast and effective customer support.
Pricing and ROI:Datadog offers affordable modules but may incur unexpected costs as usage scales. It offers significant ROI through time-saving features, despite its complex pricing model. Cribl is considered reasonably priced, especially against solutions like Splunk, providing value through reduced licensing costs and efficient data management.
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.
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.
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.
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.
The documentation is adequate, but team members coming into a project could benefit from more guided, interactive tutorials, ideally leveraging real-world data.
In future updates, I would like to see AI features included in Datadog for monitoring AI spend and usage to make the product more versatile and appealing for the customer.
There should be a clearer view of the expenses.
Over time, the licensing cost has increased.
Cribl is very inexpensive, with enterprise pricing around 30 cents per GB, which is really decent.
The setup cost for Datadog is more than $100.
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.
Our architecture is written in several languages, and one area where Datadog particularly shines is in providing first-class support for a multitude of programming languages.
The technology itself is generally very useful.
Product | Market Share (%) |
---|---|
Datadog | 7.4% |
Cribl | 1.1% |
Other | 91.5% |
Company Size | Count |
---|---|
Small Business | 9 |
Midsize Enterprise | 4 |
Large Enterprise | 8 |
Company Size | Count |
---|---|
Small Business | 78 |
Midsize Enterprise | 42 |
Large Enterprise | 82 |
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.
Datadog integrates extensive monitoring solutions with features like customizable dashboards and real-time alerting, supporting efficient system management. Its seamless integration capabilities with tools like AWS and Slack make it a critical part of cloud infrastructure monitoring.
Datadog offers centralized logging and monitoring, making troubleshooting fast and efficient. It facilitates performance tracking in cloud environments such as AWS and Azure, utilizing tools like EC2 and APM for service management. Custom metrics and alerts improve the ability to respond to issues swiftly, while real-time tools enhance system responsiveness. However, users express the need for improved query performance, a more intuitive UI, and increased integration capabilities. Concerns about the pricing model's complexity have led to calls for greater transparency and control, and additional advanced customization options are sought. Datadog's implementation requires attention to these aspects, with enhanced documentation and onboarding recommended to reduce the learning curve.
What are Datadog's Key Features?In industries like finance and technology, Datadog is implemented for its monitoring capabilities across cloud architectures. Its ability to aggregate logs and provide a unified view enhances reliability in environments demanding high performance. By leveraging real-time insights and integration with platforms like AWS and Azure, organizations in these sectors efficiently manage their cloud infrastructures, ensuring optimal performance and proactive issue resolution.
We monitor all Application Performance Monitoring (APM) and Observability reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.