We performed a comparison between BigPanda and Datadog based on real PeerSpot user reviews.
Find out in this report how the two AIOps solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."One of the most valuable features of BigPanda is its user-friendly interface."
"The solution is user-friendly and has good performance and certification."
"The program is very stable."
"A user-friendly solution."
"The event correlation is really good and it is able to reduce the noise. It is a good tool for anomaly detection."
"The most useful feature has been the AI/ML. The way BigPanda uses the AI/ML is good compared to other SRE tools."
"The main thing that we like about BigPanda is the user interface."
"The most valuable features of BigPanda are the API integration was good. It enables us to do faster onboarding."
"Datadog is constantly adding new features."
"Datadog has flexibility."
"The solution has offered increased visibility via logging APM, metrics, RUM, etc."
"We can handle debugging and find out why things are breaking in our applications."
"Dashboards and their versatility are among the most valuable features."
"Datadog agents act as an integration to different services, providing easy access and management."
"Datadog is providing efficiency in the products we develop for the wireless device engineering department."
"The tools are powerful and intuitive to set up."
"Analytics is an area for improvement, being able to break down the actions that are being taken by users of BigPanda, as well as the auto-magical work that is being done by BigPanda."
"BigPanda could improve by syncing its threshold settings with Dynatrace to align with users' familiarity."
"The solution could improve by having better integration."
"Lacks sufficient dashboard features."
"Our infrastructure is quite large - tens of thousands of servers, often with 30-plus checks running on each host with one minute intervals. This generates a lot of data often in bursts (when we have a large scale failure). This has caused some delay in the ingestion pipeline."
"The cost of this product is too high compared to New Relic."
"The observability can be enriched with regards to infrastructure and the application-integrated environment. The dashboard and reports could be improved."
"BigPanda attempts a little of everything and fails at most."
"When the logs are too big, and Datadog splits them, the JSON format breaks and it is not so useful for us."
"The pricing model could be simplified as it feels a bit outdated, especially when you look at the billing model of compute instances vs the containers instances."
"Datadog has a lot of documentation, but a lot of that documentation assumes you know how the service works, which can lead to confusion."
"I think better access to their engineers when we have a problem could be better."
"Once agents are connected to the Datadog portal, we should be able to upgrade them quickly."
"I would like better navigability across pages."
"Delta traces on the Golang profiler are extremely expensive concerning memory utilization."
"I'm not sure what kind of features are in the roadmap right now, but I encourage the development of features for defining your organization, and allowing the visibility of what kind of metrics you can get. Those features would be really useful for us."
BigPanda is ranked 15th in AIOps with 12 reviews while Datadog is ranked 1st in AIOps with 137 reviews. BigPanda is rated 7.2, while Datadog is rated 8.6. The top reviewer of BigPanda writes "Offers comprehensive alert monitoring and a user-friendly interface but requires manual validation to provide accurate alerts". On the other hand, the top reviewer of Datadog writes "Very good RUM, synthetics, and infrastructure host maps". BigPanda is most compared with ServiceNow, Moogsoft, PagerDuty Operations Cloud, ServiceNow IT Operations Management and Splunk ITSI (IT Service Intelligence), whereas Datadog is most compared with Dynatrace, Azure Monitor, New Relic, AWS X-Ray and Elastic Observability. See our BigPanda vs. Datadog report.
See our list of best AIOps vendors and best IT Infrastructure Monitoring vendors.
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There are many factors and we know little about your requirements (size of org, technology stack, management systems, the scope of implementation). Our goal was to consolidate APM and infra monitoring. We maintain critical processing on our mainframe so there was a desire to include this in our transaction trace. Due to a highly mature ELK implementation, we are not trying to incorporate log analytics into solution buy may consider in the future. We had AppD, Dynatrace, New Relic, and CA Wily all in house at the time of our evaluation. We eliminated Datadog due to a lack of real user monitoring and AppD based on experience and licensing. Between Dynatrace and New Relic, Dynatrace won based on the automation, integrated AI, support for "old" techs, and confidence we could eliminate multiple APM and infra monitoring tools.
I would not include products like BigPanda, MoogSoft, in this analysis. They are not monitoring solutions but event correlation solutions. You will need additional monitoring products to capture data and feed them. Having said that if you cannot consolidate tools you will likely need to purchase an event solution to make sense of all the alarms. We did evaluate these products but with Dynatrace AI did not feel the business value was there for the investment.
Here's a quick pro/con list on Dynatrace & New Relic from our analysis.
New Relic Pros: Insights is an awesome product and capability. Lots of capabilities and plugins to extend data collection. The APM dashboard is aesthetically pleasing and intuitive. Good training and documentation are available to support the product.
New Relic Cons: Requires lots of manual configurations to implement and support. Insights product requires an investment of time to achieve value. Licensing is a nightmare as there is virtually no transparency in what you are being charged for. Lack of solution to consolidate alerts across implementation other than significant investment in insights to manually achieve this.
Dynatrace Pros: Very simple to implement and maintain with out of the box automation which supports modern (cloud/Kubernetes) and "old" (mainframe). In-app chat is helpful. High integration of infra and APM data for full-stack observability and engineering. Topology and trace discovery is more reliable than other products or our CMDB. Synthetics are easy to set up for any user. AI-assisted problem analysis on the trace discovery streamlines troubleshooting. AI includes "events" in an analysis like VMotion, deployment events. Have not done yet but looking to leverage monitoring as code for a fully integrated and automated delivery pipeline. See keptn.sh open source project.
Dynatrace Cons: User SQL lacks some functions of NRQL for user analysis. Host, process, and service data is not available to query within the product. Alarm processing lacks some granular controls. The Plug-in library is less robust.
Good luck with your decision!
We are currently going through a paper-based analysis to select an Enterprise APM solution.
Our Contenders are
1. Dynatrace
2. Cisco(AppDynamics)
3. Broadcom DX-APM
Shortlisted based on existing relationships with other products and services they provide.
We discounted New Relic- despite their growing capability - as they are yet to enter the enterprise APM solution scene.
With regards to your response "We eliminated Datadog due to a lack of real user monitoring and AppD based on experience and licensing .." :
Will you be willing to expand on Appd - what was your experience and issues w.r.t licensing. These could help us with our evaluation. Much appreciated. Regards Adrian
Could you please share your requirements ? There are a lot tools can be added to the list. I spent almost 6 months to test and check many tools then I select eG enterprise.
Thanks