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.
They had extensive expertise with the product and were able to facilitate everything we needed.
The community, including the engineering and sales teams, is available on Slack and is very supportive.
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.
It is pretty scalable, just in terms of cost.
In deploying it into enterprise or production environments, it's quite reliable.
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.
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.
Perhaps more flexibility in terms of metrics would be helpful.
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.
The documentation is adequate, but team members coming into a project could benefit from more guided, interactive tutorials, ideally leveraging real-world data.
There should be a clearer view of the expenses.
I don't think any other solution offers this much value at this price point.
The setup cost for Datadog is more than $100.
The data reduction and preprocessing capabilities make Cribl really unique.
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 | 7.2% |
Cribl | 1.0% |
Other | 91.8% |
Company Size | Count |
---|---|
Small Business | 8 |
Midsize Enterprise | 4 |
Large Enterprise | 6 |
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 is a comprehensive cloud monitoring platform designed to track performance, availability, and log aggregation for cloud resources like AWS, ECS, and Kubernetes. It offers robust tools for creating dashboards, observing user behavior, alerting, telemetry, security monitoring, and synthetic testing.
Datadog supports full observability across cloud providers and environments, enabling troubleshooting, error detection, and performance analysis to maintain system reliability. It offers detailed visualization of servers, integrates seamlessly with cloud providers like AWS, and provides powerful out-of-the-box dashboards and log analytics. Despite its strengths, users often note the need for better integration with other solutions and improved application-level insights. Common challenges include a complex pricing model, setup difficulties, and navigation issues. Users frequently mention the need for clearer documentation, faster loading times, enhanced error traceability, and better log management.
What are the key features of Datadog?
What benefits and ROI should users look for in reviews?
Datadog is implemented across different industries, from tech companies monitoring cloud applications to finance sectors ensuring transactional systems' performance. E-commerce platforms use Datadog to track and visualize user behavior and system health, while healthcare organizations utilize it for maintaining secure, compliant environments. Every implementation assists teams in customizing monitoring solutions specific to their industry's requirements.
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.