

BigPanda and Nagios Core are competing products in IT monitoring and incident response. BigPanda appears to have the upper hand in terms of pricing and support due to its modern, AI-driven alert management, while Nagios Core is renowned for its comprehensive features and customizability.
Features: BigPanda's AI-driven alert correlation, integration capabilities, and streamlined incident management stand out as its key features. Nagios Core offers extensive plugin support, robust monitoring features, and highly customizable solutions that cater to specific needs.
Room for Improvement: BigPanda might enhance its flexibility and depth of features to better compete with Nagios Core. It could also improve cost-effectiveness for smaller businesses and expand its plugin ecosystem. Nagios Core may focus on simplifying the deployment process, enhancing user interface experience, and reducing the time required to realize its full potential.
Ease of Deployment and Customer Service: BigPanda is noted for its intuitive deployment process and strong customer support, serving companies with rapid implementation needs. Nagios Core is powerful and flexible, but often requires technical expertise for deployment, resulting in a more complex initial setup.
Pricing and ROI: BigPanda generally incurs a higher initial setup cost, often justified by improvements in incident response efficiency, leading to a strong ROI. Nagios Core is favored for its lower upfront costs, though it can require more time and resources to achieve its full potential.
BigPanda offers significant time-saving, cost-saving, and resource-saving benefits.
BigPanda saves time with its advanced features and manages large environments while requiring fewer resources compared to our previous tool, Netcool.
If BigPanda can consistently provide such competent contacts, I would rate the support ten out of ten, otherwise, it is an eight out of ten.
Companies like CoreLogix, which is a log platform, achieve ten out of ten due to their responsiveness.
For technical support, we have only had to address password resets and alert mismatching.
It handles large volumes of alerts without limitations.
We manage a large environment with over 50,000 servers and various monitoring tools like Dynatrace, New Relic, Splunk, Nagios, and Datadog.
I rate the scalability of BigPanda at eight.
The solution is scalable.
BigPanda is now stable.
I would rate the availability of BigPanda at nine because it's almost 99.99% available.
However, when handling critical traffic, the BigPanda site can slow down, which we manage with a load balancer.
I tried many other solutions at work, however, in terms of Nagios, I haven't seen any disruption or downtime.
A 'deep dive' analysis feature would be appreciated to give detailed insights such as CPU usage and disk space analysis.
It would be beneficial if BigPanda leveraged AI to solve critical issues related to editing and sending alerts based on enrichment mapping files.
If BigPanda could integrate AI, it would enhance the platform significantly by offering chatbot functionality within the BigPanda UI.
The pricing for BigPanda is reasonable compared to other event management tools, given its advantages.
Its automation has significantly improved incident response times, reducing the process to within one minute.
It can correlate multiple issues within a single device, create a single incident, and thus reduce noise and provide faster resolution.
BigPanda improves service reliability with instant resolution, increased uptime, and reduced mean time to resolution, thus enhancing service quality.
You can monitor anything.
| Product | Mindshare (%) |
|---|---|
| Nagios Core | 1.9% |
| BigPanda | 0.6% |
| Other | 97.5% |

| Company Size | Count |
|---|---|
| Small Business | 6 |
| Large Enterprise | 11 |
| Company Size | Count |
|---|---|
| Small Business | 20 |
| Midsize Enterprise | 11 |
| Large Enterprise | 23 |
BigPanda enhances incident management through root cause analysis, alert deduplication, and event correlation. The AI-driven platform is designed for environments with high alert volumes, providing insights for data-driven decisions and seamless integration with tools like ServiceNow and Teams.
BigPanda addresses the complexities of incident management by offering an AI-focused approach to anomaly detection. Automation improves response times, while unified analytics supports informed decision-making. Despite AI integration and usability needing enhancement, the platform simplifies observability and ticketing through integrations with New Relic and Slack. Features like enrichment mapping and unified search improve functionality, though reporting and visualization aspects require development.
What are the key features of BigPanda?BigPanda is widely implemented in industries focusing on observability and predictive analysis, providing efficient alert processing and incident management. Users utilize its capabilities to seamlessly integrate with solutions like Dynatrace, particularly in environments that handle high volumes of alerts, ensuring effective notification delivery through various platforms.
Nagios Core offers a versatile monitoring solution that efficiently manages notifications, reporting, and resource usage. Its open-source architecture provides flexibility and customization options for comprehensive infrastructure health management.
Nagios Core, known for its extensible plugin architecture, proactively enhances infrastructure management with customizable notifications and reliable reporting. Seamlessly integrating various plugins, it offers real-time dashboards and efficient alerting systems for thorough monitoring. Its adaptability and ease of configuration make it popular among users seeking flexible monitoring solutions. However, improvements in the web interface, scalability, performance, and visualization are needed to enhance accessibility. Users seek better alert mechanisms, more robust PDF export features, and simpler setup processes for increased efficiency.
What are the key features of Nagios Core?In many industries, Nagios Core is integral to monitoring infrastructure and services, including cloud servers, applications, and network devices. Users rely on it for issue detection, capacity planning, and maintaining system stability in environments like AWS and on-premise servers. Its capabilities in CPU, memory, and bandwidth monitoring along with alert systems support sectors needing real-time oversight of critical equipment.
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