We performed a comparison between Datadog and Graylog based on our users’ reviews in five categories. After reading all of the collected data, you can find our conclusion below.
Features: Datadog users like its customizable displays, error tracking, and advanced AI/ML capabilities. Graylog stands out with its exceptional search functions, seamless integration with Elasticsearch, and real-time data access. Datadog could enhance its usability and reduce its learning curve. Users said integration was another pain point. Graylog could benefit from additional customization options and an improved rule-creation process.
Service and Support: While many users spoke highly of Datadog’s support team, others reported slow support, especially in the Asia-Pacific region. Graylog's customer service is generally well-regarded, with reviewers noting effective solutions and satisfactory experiences. While response times may differ, Graylog's support is considered superior compared to that of other products.
Ease of Deployment: Datadog’s setup is considered straightforward, and users often receive help from a partner or vendor. Some Graylog users said the setup was easy. Other reviewers faced challenges, but these were easily resolved with help from the vendor’s support staff. Graylog is easier to set up in smaller environments, but it could get complicated in large clusters.
Pricing: Opinions about Datadog's price are divided. Some users found it costly, but others thought it was acceptable. Some said the pricing model could be clearer and better explained. Graylog offers an enterprise edition and an open-source option with a daily capacity restriction. Some users said that data costs can be expensive.
ROI: Users said Datadog saved them time and improved visibility into security blind spots. Graylog can offer some cost savings. The precise ROI may vary depending on the organization’s size and use case.
"I have found the logging and tracing features the most valuable."
"Having a wealth of information has helped us investigate outages, and having historical data helps us tune our system."
"Datadog dashboards are pretty great."
"The network map is crucial in identifying bottlenecks and determining what needs more attention."
"Datadog's ability to group and visualize the servers and the data makes it relatively easy for the root cause analysis."
"We have a better grasp of what is occurring during the deployment cycle. If something fails, we have an idea what has failed, where it has failed, and how it failed to better mitigate the situation."
"The ingestion points are unlimited and support customization. We haven't had anything yet that we haven't been able to integrate with it."
"Sometimes it's more user friendly for development teams. There are some parts of Datadog that are more understandable for development teams. For example, the APM in Datadog works more manually and works like the tools in New Relic or Grafana, or Elastic. It is easier to understand for software development teams."
"It is used as a log manager/SIEM. It provides visibility into the infrastructure and security related events."
"Graylog's search functionality, alerting functionality, user management, and dashboards are useful."
"What I like about Graylog is that it's real-time and you have access to the raw data. So, you ingest it, and you have access to every message and every data item you ingest. You can then build analytics on top of that. You can look at the raw data, and you can do some volumetric estimations, such as how big traffic you have, how many messages of data of a type you have, etc."
"The build is stable and requires little maintenance, even compared to some extremely expensive products."
"I am very proud of how very stable the solution is."
"One of the most valuable features is that you are able to do a very detailed search through the log messages in the overview."
"The solution's most valuable feature is its new interface."
"Allowing us to set up alerts and integrate with platforms we already use, such as Slack and OpsGenie to alert users of these errors proactively, is also a very useful feature."
"The solution should provide alerts for cloud outages."
"Datadog is so feature-rich that it is often hard to onboard new folks and tough to decide where to invest time."
"Datadog isn't as mature as some of the established players like Dynatrace or Splunk. It's a new product, so they are constantly releasing new features, and I don't have much to complain about."
"The product could be improved by providing remote control to agents, enabling them to execute automation and collections without requiring another automation tool or integration."
"I think better access to their engineers when we have a problem could be better."
"I'm still exploring the trial version, and it is fine. One thing that I haven't been able to figure out is how to retrieve a report. This is something that could be improved. I probably need to navigate to a place to access the reports."
"I would love to see more metrics or analytics in IoT devices."
"Additional metrics should be included."
"Its scalability gets complicated when we have to update or edit multiple nodes."
"More customization is always useful."
"The biggest problem is the collector application, as we wanted to avoid using Graylog Collector Sidecar due to its architecture."
"I would like to see some kind of visualization included in Graylog."
"It would be great if Graylog could provide a better Python package in order to make it easier to use for the Python community."
"Elasticsearch recommendations for tuning could be better. Graylog doesn't have direct support for running the system inside of Kubernetes, so it can be challenging to fill in the gaps and set up containers in a way that is both performant and stable."
"Over six months, I had two similar issues where searches were performed on field "messages". It exhausted all the memory of the ES node causing an ES crash and a Graylog halt."
"More complex visualizations and the ability to execute custom Elasticsearch queries would be great."
Datadog is ranked 3rd in Log Management with 137 reviews while Graylog is ranked 11th in Log Management with 18 reviews. Datadog is rated 8.6, while Graylog is rated 8.0. The top reviewer of Datadog writes "Very good RUM, synthetics, and infrastructure host maps". On the other hand, the top reviewer of Graylog writes "Great detailed search features and easy Java integration, but needs improvement in integration with Python". Datadog is most compared with Dynatrace, Azure Monitor, New Relic, AWS X-Ray and Elastic Observability, whereas Graylog is most compared with Grafana Loki, Wazuh, syslog-ng, Fortinet FortiAnalyzer and ManageEngine Log360. See our Datadog vs. Graylog report.
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