Cribl.Cloud Suite

I am working in a PLM environment, which is product lifecycle management. We deal with lots of system logs and tool integrations. I used Cribl Search for debugging system errors quickly and searching logs stored in long-term storage. Instead of pushing all logs into expensive tools, we used Cribl Search to directly investigate issues from stored data.

I am currently using Cribl Search only. I have some experience with Cribl Stream, which we are using for our data pipeline solution.

We have just started using the Search in Place feature because one of our team members recommended it. There is a lot of room for improvement in the way we query the data and the whole data processing pipeline. We weren't using any other tool before.

I have been using Cribl Search for a long time now, and I think Search in Place is a very good feature in Cribl Search. Unify Search is also valuable, where you can search data from multiple sources in one place. Fast investigation reduces steps from multiple tools to a single workflow. Pre-built search packs save effort to configure the dashboards and write the queries. It also works well with other Cribl tools.

The traditional way for certain places is that logs are generated, then sent to SIEM tools like Splunk, and then stored again before you can search them. This has problems including data duplication and high storage costs. With Search in Place in Cribl Search, logs stay in storage such as S3, data lakes, or archives. You can directly run queries on that data without any movement, duplication, or reprocessing. Advantages include cost reduction and faster investigation.

Since we can directly query historical data where it is stored, there is an advantage of deep root cause analysis, which helps understand what happened in the past. This is useful for debugging recurring issues and is cost-efficient. It has helped me in faster troubleshooting because there is no need to reload old logs. We can investigate incidents after days, weeks, or even months. It has the ability to handle large data volumes, so there is no performance bottleneck.

We reduced unnecessary data ingestion by almost 40 to 50% using Search in Place. We could troubleshoot issues faster because data was already available for querying. It eliminates redundancy and keeps the architecture cleaner. As the data grows, we don't need to scale ingestion pipelines.

The user interface of Cribl Search can be more simplified because for non-technical users, it is quite difficult to grasp. There is a need for better beginner tutorials.

Cribl could have built-in guided queries for faster onboarding and better beginner tutorials. A more simplified UI would be better for non-technical people.

I have been working with Cribl for eight to nine months.

Until now, we haven't had any downtimes. It has been working very well.

It is pretty scalable horizontally. We started with one team member but now there are five to six people using it.

We developers ask for support from our in-house IT team, but I don't know what conversation goes on between Cribl customer service and our IT team.

We evaluated Splunk, but due to some reasons, we went with Cribl Search.

Cribl Search was set up by the IT team, but they haven't complained about any issues or complexities that arose during the setup. I think the setup is pretty simple and not that complicated.

The implementation was done by our internal IT team.

With Cribl, we have observed a 40 to 60% reduction in log volume hitting the firewall because Cribl filters unnecessary events and removes verbose fields.

There is reduced pipeline complexity and faster end-to-end workflow because data doesn't wait in ingestion queues. There is also optimized data processing cost because less data processed equals less compute plus storage cost. Other expensive tools are used only for critical data. There is a shift from processing to querying because traditional systems process first and query later, but Cribl stores data cheaply so we can query it when we need it.

Cribl has many filters to remove noise from the data and to remove verbose fields, which has been very good to work with.

Earlier, we had to process and store all logs in monitoring tools, which are very expensive, before analysis. After using Cribl Search, we streamlined the workflow by sending only critical data through pipelines and directly querying archive logs for investigation. This improved efficiency and reduced system load, which helped us indirectly optimize costs. We reduced the overall processing load by around 40%.

I'd highly recommend other organizations to use Cribl Search because it did help us a lot with data processing and everything.

Cribl Search was set up by the IT team, but they haven't complained about any issues or complexities that arose during the setup, so I think the setup is pretty simple and not that complicated. I would rate this review an 8 out of 10.

I use Cribl  in a data management platform for IT security teams. My use cases include Stream , Edge, Search, and Lake.

I appreciate data routing the most about Cribl . I use it for data routing, data processing, and integration support. Cribl's ability to handle high volumes of diverse data types such as logs and metrics is impressive. It can easily handle logs because it is highly scalable and built to process millions of events per second, making it very easy to use.

What I dislike about Cribl are the documentation gaps and the setup complexity.

I have been working with Cribl for one year.

Regarding stability, once the pipelines were properly set up, the ongoing maintenance was minimal and mostly involved small adjustments rather than major changes. Overall, Cribl is not maintenance heavy, but sometimes maintenance is needed.Cribl requires some maintenance on my end; it is relatively low compared to traditional log pipelines.

Cribl provides high availability through distributed architecture, so we can achieve this by developing multiple workers and using load balancing to ensure continuous data flow even during failures in the pipeline.

The initial deployment is medium because the setup is complex. It took me some time to set it up for the first time because my friend helped me, but I found it difficult.

I have not seen a significant decrease in firewall logs while working with Cribl because it is highly scalable, so that much decrease has not occurred.

I am using Cribl  Stream  for data routing and data processing as part of my company's IT team. We primarily use it for monitoring and collecting data.

One of the best features is integration support because it offers more than 80 to 90 sources and destinations via Cribl  packs. Additionally, the security is very good because they offer encryption and access control to protect sensitive telemetry data. The data processing and reduction is also excellent because it filters unwanted fields and removes redundant data.

I have seen a decrease in my firewall logs by 50 to 60%.

Cribl allows me to handle high volumes of diverse data, such as logs and metrics, and it helps manage them effectively.

It is helpful because it handles diverse data types and can process logs, metrics, event streams, JSON, text, structured and unstructured data.

The user interface is acceptable, but I think a person who is just starting to use it will need to go through documentation because there is a steep learning curve to become familiar with Cribl Stream . The setup is also complex, and configuring integrations and pipelines for a large environment requires significant effort.

The areas that have room for improvement are the complex setup and better documentation, such as a user guide.

I have been using this product for six to eight months.

Cribl performs time-to-time updates and maintenance, and it must be managed effectively because we are using it daily and have not experienced any issues for a long time. The team maintaining it must be performing their job very well.

Horizontally, it is quite scalable, so I rate that a ten.

I rate the technical support a nine, and I rate the stability an eight.

I have used Splunk, and what Cribl does is it does not replace Splunk; it optimizes the data before sending it to Splunk, reducing cost and load. Therefore, Cribl is not a direct alternative to Splunk; they are complementary to each other.

The deployment was quite easy.

I do not know exactly how long it took to deploy because I was not the one who deployed it on the cloud, but the ones who deployed it told me that it was quite easy to deploy and there were no complaints from them.

Roughly five to six users use the solution.

I checked out Cribl Search once, and it helped me directly search from S3  data lakes, and it did help me save time and cost.

I have not analyzed the exact amount, but in ballpark terms, it saves about 10 to 20%.

I think it is cost-efficient because overall, after using Cribl, it helps users save cost and time. If you look at the big picture, it is cost-effective.

It saves me about 30 to 40% in terms of time and cost.

I would highly recommend it because it is cost-efficient, helps reduce noisy logs, and filters unnecessary fields.

I gave this review a rating of nine.

Our use case for Cribl  is observability from an infrastructure point of view; we use Cribl  for getting the logs from our infrastructure. The metrics or logs which we require from our servers or containers, or the platforms where we have deployed our product, necessitate real-time data processing, so Cribl helps us in that regard.

I love Cribl Edge feature, which is an agent we can directly deploy at our servers; that is quite a good feature that helps in collecting data locally at the server level. Additionally, the search is good; we can search across all our data sources, and it is quite fast. Cost efficiency also helps in optimizing costs.

Cribl handles high volumes of diverse data types very well. We have around 200 to 250 in-house servers, and we require observability and visibility over those servers. We don't have a team that manages them, and we cannot hire too many people to manage 200 servers. Cribl provides visibility and helps in that regard; we get real-time metrics, allowing us to see when we need to increase the compute of our servers or when we have over-provisioned resources. It helps in optimizing costs at our infrastructure level, and Cribl is quite cost-efficient, helping in that aspect as well.

We haven't gone very deep into it, so we don't have a heavy use case, but most probably, as it helps us in optimizing costs, that is the best thing about it. Cribl's UI is quite simple and minimal, helping the developer and team get familiar with it earlier; however, it provides functionalities in a very deep way. Thus, it becomes difficult if we don't require some metrics or something for filtering, as Cribl has provided many functionalities to filter out metrics which we don't require with our lighter use case. That has created some hindrance for us; otherwise, everything is quite good.

The function section is quite messy and includes too many functionalities which are generally not required at an amateur level. If we advance at that level, then definitely it is required to get the precise logs that filter out unnecessary data when the data stream is quite big. At that time, definitely it is required, but at the initial level, it becomes quite difficult to get the proper data that is required.

I used the solution about six months ago.

We haven't faced much regarding instability such as lagging or crashing; the backend team and support staff are quite nice, and we didn't encounter any significant issues with stability.

Scaling with Cribl is very easy, both horizontally and vertically, so we don't have any hindrance in scaling the tool.

My team has contacted technical support for some tasks they were facing issues with; they reported that the staff is quite nice, and the support is very good. However, we didn't require much support, only maybe twice or thrice.

We used to utilize Node Exporter, Grafana , and Prometheus.

Cribl sits in between those tools; it does not replace any of them. Node Exporter helps collect the host metrics, Prometheus is responsible for scraping the metrics, and Grafana  serves as a dashboard. Cribl assists with infrastructure observability without replacing any of the tools. We use all of them right now as well.

Cribl's initial deployment is quite easy and nice; we didn't face any difficulties in doing that. Additionally, scaling it horizontally or vertically is very good.

I lead my team; I don't set and manage deployment myself anymore. Initially, when we had a very small team, I started building it, but now my team handles all this.

I'm not from the team that handles pricing; another department deals with that. However, the pricing appears to be good because I haven't been approached with concerns about why we are spending a particular amount. I think our pricing is fair.

For our use case, I would give Cribl a score of 10 out of 10, but overall, if I rated it for a large organization that requires it, it would be fair to give an eight. I would rate this review as an 8 overall.

I use Cribl  for data integration, pipelining, data monitoring, scalability, and to check how my monitor is working. The main product we use is Cribl  Stream , which we use for log routing, filtering, and transforming data before sending it to our SIEM  platform. This is the core part of our log management pipeline. Through Cribl Stream , we mainly work with features such as data pipelining, routing rules, and data transformation functions to control how logs move between different systems. My hands-on experience is primarily with Stream, since that is the component we rely on most for processing and optimizing log data in our environment.

The main product we use is Cribl Stream, which we use for log routing, filtering, and transforming data before sending it to our SIEM  platform. Through Cribl Stream, we mainly work with features such as data pipelining, routing rules, and data transformation functions to control how logs move between different systems. My hands-on experience is primarily with Stream, since that is the component we rely on most for processing and optimizing log data in our environment.

One of the biggest advantages for my organization is better control over log data. We can filter, transform, and route logs before they reach downstream systems such as the SIEM platform, which helps reduce noise and focus only on relevant data. Another key benefit is cost optimization. By dropping unnecessary logs and sending only important data, we significantly reduce ingestion and storage costs in tools such as Splunk. It also improves operational efficiency.

One key area is simplifying the user experience, especially for new users. Since it has multiple components such as metrics, traces, and detectors, making onboarding and navigation more intuitive would be beneficial. One area of improvement could be reducing the learning curve. Since it is a very flexible tool with powerful pipeline configuration, new users may take some time to fully understand how to design and optimize pipelines efficiently. Another improvement could be more pre-built templates or out-of-the-box integration of common data sources, which would help teams get started faster without building from scratch. I also think enhanced monitoring and troubleshooting visibility for pipelines would be helpful, especially in large environments where multiple data flows are being processed.

The main strength is its flexibility, scalability, and cost optimization benefits. It gives strong control over what data is processed and sent to downstream systems. The reason I would not give it a ten is mainly due to the learning curve and initial complexity, especially for new users. Some areas such as documentation or advanced troubleshooting could be improved.

I have been working in the cybersecurity and security operations space for around one year.

Cribl is stable and reliable. I would rate stability and reliability at eight out of ten. In my experience, it is generally performing well.

I would rate the scalability of Cribl at eight or nine out of ten. Its ability to handle a high volume of different data types would get a rating of eight or nine out of ten. It is designed to process large-scale telemetry data from multiple sources such as firewalls, cloud services, applications, and infrastructure. It can handle different formats such as JSON, syslog, and custom logs, and transform them within the pipeline with its distributed architecture. We can scale horizontally by adding worker nodes, which allows it to handle increased data volumes without major performance issues.

We faced an issue with a pipeline dropping certain log events unexpectedly. We reached out to support, and they helped us analyze the pipeline configuration and logs. Initially, the response was general, but after sharing more details such as sample logs and pipeline rules, they were able to identify that the filter condition was incorrectly configured, which was causing the data to be dropped. They guided us on how to modify the rule and validate the data flow using a live preview, and we were able to resolve the issue very quickly. Overall, the support team was very helpful and knowledgeable, especially once the issue was clearly explained, and it helped us solve the problem without major downtime.

Before Cribl, most log processing was handled directly within the SIEM platforms, mainly using tools such as Splunk native and sometimes Logstash  for data processing. The limitation with that approach was that all the raw log data was first ingested into the SIEM, and then filtering or transformation were applied afterwards. This increased the data volume and cost complexity. We moved to Cribl to introduce a dedicated data pipeline layer before the SIEM, which allows us to filter, transform, and route data more efficiently before ingestion.

As I am on the technical side, I was involved in the initial setup of Cribl. My role included configuring data sources, setting up pipelines, and defining routing and filtering rules based on our different requirements. I also worked on integrating Cribl with our SIEM platform, ensuring that only relevant and optimized data is forwarded. During the setup, we focused on designing efficient pipelines, testing data flow, and validating transformations to make sure everything was working correctly. Overall, the initial setup was not very complex, but it required proper planning to design the pipelines.

Other than this platform, it is more valuable. Before adopting Cribl, we did look at a few other approaches. Some of the evaluations were around using native capabilities within SIEM platforms such as Splunk, as well as open-source log processing tools such as Logstash  for handling data pipelines. Those options can work for log collection and processing, but Cribl stood out because it provides a dedicated platform specifically designed for observability and security data pipelines. It offers more flexibility in routing, filtering, and transforming logs without heavily relying on the SIEM itself. The visual pipeline management and real-time visibility into data flow were also important factors that made Cribl a better fit for managing large volumes of log data across multiple systems. We saw other options, but by way of references, we determined that Cribl is more relevant for our work. So we chose Cribl.

I would recommend starting with a few simple pipelines, then gradually expanding as you become more comfortable with the platform. I would rate Cribl eight out of ten. A few improvements in Splunk Observability Cloud  could make it even better. Overall, I would give Cribl a rating of 8.5 out of ten.