Users reported that Datadog excels in customer service and support, with prompt assistance and knowledgeable staff. ROI was deemed highly positive, with improvements in monitoring and troubleshooting capabilities. Datadog users seek enhanced customization options and better integration capabilities. Honeycomb.io offers advanced visualization capabilities and high cardinality query support. Users appreciate the platform's collaborative nature but call for better integrations, simplified pricing, and improved documentation.
Features: Datadog excels in comprehensive monitoring and customizable dashboards, with seamless integrations. Honeycomb.io stands out for advanced visualization and detailed insights with high cardinality query support. Users appreciate each platform's collaboration features for efficient teamwork.
Pricing and ROI: Datadog's setup cost is minimal and praised for its transparency and flexibility in pricing. Honeycomb.io also offers a competitive pricing with straightforward setup cost and licensing structure, making it easy to budget for. Datadog's ROI is praised for significant monitoring and troubleshooting improvements, while Honeycomb.io's ROI highlights system performance enhancements and powerful querying capabilities, leading to increased productivity.
Room for Improvement: Datadog's room for improvement lies in enhanced customization options for dashboard layouts, better integration capabilities, and improved alerts and notifications setup. Meanwhile, Honeycomb.io needs more usability, customization choices, smoother integrations, and enhanced overall performance.
Deployment and customer support: The reviews on Datadog indicate a range of timeframes for deployment and setup, with potential confusion on whether they are distinct phases. In contrast, feedback on Honeycomb.io shows a clearer distinction between deployment and setup timelines, emphasizing the need for careful consideration in evaluating implementation durations. Datadog has satisfied customers with their prompt and knowledgeable customer service, while Honeycomb.io's team stands out for their helpfulness, clear communication, and expert assistance. Both companies excel in addressing issues effectively.
The summary above is based on 195 interviews we conducted recently with Datadog and Honeycomb.io users. To access the review's full transcripts, download our report.
"I find the greatest feature is being able to search across logs from various microservices."
"Datadog dashboards are pretty great."
"The performance of Datadog is good."
"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."
"We've found it most useful for managing Rstudio Workbench, which has its own logs that would not be picked up via Cloudwatch."
"The most valuable aspects of the product include the APM and profiler."
"The biggest thing I liked was the combination of all the things - monitoring, log aggregation, and profiling."
"I really enjoy the RUM monitoring features of Datadog. It allows us to monitor user behavior in a way we couldn't before."
"The solution's initial setup process was straightforward since we were getting enough support from Honeycomb.io's team."
"I would love to see more metrics or analytics in IoT devices."
"We need more visibility into the error tracking dashboard."
"I find the training great. That said, it is set for the LCD (lowest common denominator). Of course, this is very helpful to sell the product, yet, to really utilize the product, you need to get more detailed."
"The more tools that they can build that allow you to run AWX playbooks, or other similar fixes, would benefit clients greatly."
"We need to learn more about the session reply feature inside of DD."
"One area where I was really looking for improvement was the CSPM product line. I had really wanted to have team-level visibility for findings, since the team managing the resources has much more context and ability to resolve the issue, as the service owner. However, this has been added to the announcement in a recent keynote."
"The solution needs to integrate AI tools."
"I would like testing for data in the future."
"The process of log scraping gets delayed on Honeycomb.io. At times, it gives false alerts to the application team."
Datadog is ranked 1st in Application Performance Monitoring (APM) and Observability with 137 reviews while Honeycomb.io is ranked 37th in Application Performance Monitoring (APM) and Observability with 1 review. Datadog is rated 8.6, while Honeycomb.io 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 Honeycomb.io writes "A valuable solution for application teams to identify downtime and SLO-related issues". Datadog is most compared with Dynatrace, Azure Monitor, New Relic, AWS X-Ray and Elastic Observability, whereas Honeycomb.io is most compared with Grafana, Sentry, Chronosphere, New Relic and Azure Monitor.
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