BigPanda and Elastic Observability compete in IT monitoring and incident management. BigPanda excels in pricing and support, while Elastic Observability offers robust features justifying its higher price.
Features: BigPanda focuses on event correlation, noise reduction, and automation capabilities. It streamlines workflows for IT teams, helps manage incidents efficiently, and integrates seamlessly with existing systems. Elastic Observability offers comprehensive data analysis, integration with diverse data sources, and deep system performance insights. It is valuable for businesses needing detailed user behavior analytics and supports advanced data exploration.
Room for Improvement: BigPanda could improve its analytical capabilities and expand its data source integration options to match competitors. A richer set of user behavior insights could enhance its offerings. Better documentation and resources could further assist users. Elastic Observability may refine its deployment procedures for a quicker setup, simplify integration steps to lower complexity, and offer more budget-friendly pricing options for small businesses.
Ease of Deployment and Customer Service: BigPanda provides straightforward deployment with dedicated support and a quick onboarding process minimizing downtime. Elastic Observability has a scalable model with robust technical support, but its complexity may result in longer setup times, demanding careful integration steps.
Pricing and ROI: BigPanda presents an attractive, cost-effective pricing structure, facilitating efficiency and quick decision-maker buy-in without high initial costs. It delivers solid ROI, appealing to budget-conscious businesses. Elastic Observability has a higher cost but justifies it with advanced analytics and data insights, promising high ROI for companies investing in its comprehensive features.
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.
For technical support, we have only had to address password resets and alert mismatching.
Their collaboration was good, and I would rate them nine out of ten.
Elastic support really struggles in complex situations to resolve issues.
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.
Elastic Observability seems to have a good scale-out capability.
Elastic Observability is easy in deployment in general for small scale, but when you deploy it at a really large scale, the complexity comes with the customizations.
What is not scalable for us is not on Elastic's side.
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.
There are some bugs that come with each release, but they are keen always to build major versions and minor versions on time, including the CVE vulnerabilities to fix it.
It is very stable, and I would rate it ten out of ten based on my interaction with it.
Elastic Observability is really stable.
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.
For instance, if you have many error logs and want to create a rule with a custom query, such as triggering an alert for five errors in the last hour, all you need to do is open the AI bot, type this question, and it generates an Elastic query for you to use in your alert rules.
It lacked some capabilities when handling on-prem devices, like network observability, package flow analysis, and device performance data on the infrastructure side.
Some areas such as AI Ops still require data scientists to understand machine learning and AI, and it doesn't have a quick win with no-brainer use cases.
The pricing for BigPanda is reasonable compared to other event management tools, given its advantages.
The license is reasonably priced, however, the VMs where we host the solution are extremely expensive, making the overall cost in the public cloud high.
Elastic Observability is cost-efficient and provides all features in the enterprise license without asset-based licensing.
Observability is actually cheaper compared to logs because you're not indexing huge blobs of text and trying to parse those.
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.
The most valuable feature is the integrated platform that allows customers to start from observability and expand into other areas like security, EDR solutions, etc.
the most valued feature of Elastic is its log analytics capabilities.
All the features that we use, such as monitoring, dashboarding, reporting, the possibility of alerting, and the way we index the data, are important.
| Product | Market Share (%) |
|---|---|
| Elastic Observability | 2.9% |
| BigPanda | 0.9% |
| Other | 96.2% |
| Company Size | Count |
|---|---|
| Small Business | 6 |
| Large Enterprise | 11 |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 4 |
| Large Enterprise | 16 |
BigPanda Autonomous Operations platform is a cutting-edge solution that helps IT Ops, NOC and DevOps teams detect, investigate, and resolve IT incidents faster and more easily than ever before. Powered by Open Box Machine Learning, BigPanda correlates IT noise into insights, automates incident management, and unifies fragmented IT operations.
BigPanda’s Autonomous Operations platform increases the ease with which IT administrators are able to both manage their systems and respond to issues that arise. It combines machine learning and automation, which not only provide administrators with reliable suggestions concerning future issues that may arise, but also allows for certain tasks to be assigned to the system itself. These allow organizations to save both time and resources that can be dedicated to more critical tasks.
BigPanda’s Autonomous Operations platform possesses a simplified and unified user interface, enabling users to manage the solution from one place. The UI allows users to view and manage all of the information and controls that IT teams need to be able to easily access, providing a level of visibility that ensures users will be able to view all of their system’s most up-to-date and critical information at any time that they wish.
Benefits of BigPanda Autonomous Operations Platform
Some the benefits of using BigPanda’s Autonomous Operations platform include:
Reviews from Real Users
BigPanda’s Autonomous Operations platform stands out among its competitors for a number of reasons. Two major ones are its centralized user interface and its ability to easily integrate with other security solutions. The centralized UI gives users the ability to clearly see all their alert data in a single location without having to search for it. Its easy integration allows administrators to augment their IT security capabilities when they find it necessary to do so.
PeerSpot user Larry C., the principal engineer in charge of site operations for a tech vendor, notes this benefit when he writes, "Alert aggregation was the primary requirement. BigPanda pulls all this together into a single UI for us, allowing us to see related alerts grouped together into an incident, and enables us to easily create a JIRA ticket and Slack channel to manage an issue."
Another PeerSpot user, who is an independent entrepreneur in the IT environment at a tech services company, notes the flexibility of this platform when they write “BigPanda integrates well with other solutions, such as WatchGuard.”
Elastic Observability offers a comprehensive suite for log analytics, application performance monitoring, and machine learning. It integrates seamlessly with platforms like Teams and Slack, enhancing data visualization and scalability for real-time insights.
Elastic Observability is designed to support production environments with features like logging, data collection, and infrastructure tracking. Centralized logging and powerful search functionalities make incident response and performance tracking efficient. Elastic APM and Kibana facilitate detailed data visualization, promoting rapid troubleshooting and effective system performance analysis. Integrated services and extensive connectivity options enhance its role in business and technical decision-making by providing actionable data insights.
What are the most important features of Elastic Observability?Elastic Observability is employed across industries for critical operations, such as in finance for transaction monitoring, in healthcare for secure data management, and in technology for optimizing application performance. Its data-driven approach aids efficient event tracing, supporting diverse industry requirements.
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