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.
"For us to have visibility into our app stack and the hardware we run has been highly beneficial."
"We've been able to glean from the monitors what servers are down, and can alert the team in Slack."
"The monitoring functionality, in general, and tagging infrastructure are great."
"The solution has offered increased visibility via logging APM, metrics, RUM, etc."
"Even if we don't end up using Datadog, it revealed problems and optimizations to us that weren't obvious before."
"We have hundreds of microservices, and knowing how top-level requests weave throughout all of them is invaluable."
"Since we integrated Datadog, we have had increased confidence in the quality of our service, and we had an easier time increasing our delivery velocity."
"The integration and configuration are incredibly simple. The SaaS offering is remarkably easy to set up, especially if you're coming from a Graphite environment or anything that uses a StatsD."
"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."
"Graylog's search functionality, alerting functionality, user management, and dashboards are useful."
"The build is stable and requires little maintenance, even compared to some extremely expensive products."
"The best feature of Graylog is the Elasticsearch integration. We can integrate and we can run filters, such as an event of interest, and those logs we can send to any SIEM tool or as an analytic. Additionally, there are clear and well-documented implementation instructions on their website to follow if needed."
"I am very proud of how very stable the solution is."
"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 product is scalable. The solution is stable."
"UDP is a fast and lightweight protocol, perfect for sending large volumes of logs with minimal overhead."
"While the tool is robust with many different capabilities, users would greatly benefit from more examples in the documentation."
"The setup was a bit complex."
"I often have issues with the UI in my browser."
"I found the solution to be stable, I did not experience any bugs or glitches. However, some of the managing team did."
"The current way accounts are billed could be vastly improved - especially when involving multiple organizations across multiple accounts in combination with reserved commitments."
"Lately, chat support has a longer waiting time."
"I would like testing for data in the future."
"I want to applaud the efforts in making the UI extremely usable and approachable. My suggestion would be to take another look at how the menu structure is put together, however. Even after using the platform mostly every day for months, I still find myself trying to find a service or feature in the menus."
"Dashboards, stream alerts and parsing could be improved."
"More customization is always useful."
"Lacks sufficient documentation."
"Since container orchestration systems are popular and Graylog fits the niche well, perhaps they could officially support running in docker containers on Kubernetes as a StatefulSet as a use case. That way, the declarative nature of Kubernetes config files would document their best case deployment scenario-"
"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."
"Graylog could improve the process of creating rules. We have to create them manually by doing parses and applying them. Other SIEM solutions have basic rules and you can create and get more events of interest."
"I hope to see improvements in Graylog for more interactivity, user-friendliness, and creating alerts. The initial setup is complex."
"We ran into problems with Elasticsearch throwing a circuit-breaking exception due to field data size being too large. It turned out that the heap size directly impacted this size in a high-throughput environment, causing unexplained instability in Graylog. We were able to troubleshoot on the Elasticsearch size, but we should have been able to reference some minimum requirements for Graylog to know that our settings weren't sufficient."
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, Splunk Enterprise Security and ManageEngine Log360. See our Datadog vs. Graylog report.
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We monitor all Log Management 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.
