Splunk Observability Cloud and Red Hat OpenShift compete in the monitoring and application deployment categories. Splunk Observability Cloud has an advantage in real-time monitoring and application performance monitoring, while Red Hat OpenShift excels in deployment capabilities and scalability.
Features: Splunk Observability Cloud is effective in real-time monitoring, log analytics, and custom dashboard creation, and it integrates seamlessly with various environments. Red Hat OpenShift offers robust application deployment, strong DevOps support, and container orchestration with high availability.
Room for Improvement: Splunk Observability Cloud users suggest enhancing integration with third-party products, improving automation, and refining pricing complexity. Red Hat OpenShift users highlight the need for streamlined documentation, enhanced scalability, and improved external service integration.
Ease of Deployment and Customer Service: Splunk Observability Cloud supports diverse deployment environments but is noted for high costs and complexity. Their customer service is responsive but needs more prompt resolutions. Red Hat OpenShift offers flexible deployment options and robust support with noted setup complexity.
Pricing and ROI: Splunk Observability Cloud, though expensive, provides significant ROI with enhanced visibility and efficiency. The pricing model based on data volume can lead to escalating costs. Red Hat OpenShift has competitive pricing, especially for enterprises using multiple Red Hat products, and offers notable ROI with cost-effectiveness relative to its functionalities.
With OpenShift combined with IBM Cloud App integration, I can spin an integration server in a second as compared to traditional methods, which could take days or weeks.
Moving to OpenShift resulted in increased system stability and reduced downtime, which contributed to operational efficiency.
It is always advisable to get the bare minimum that you need, and then add more when necessary.
Using Splunk has saved my organization about 30% of our budget compared to using multiple different monitoring products.
Anyone working in front-end management should recognize the market price to see the true value of end-user monitoring.
Red Hat's technical support is responsive and effective.
I have been pretty happy in the past with getting support from Red Hat.
Red Hat's technical support is good, and I would rate it a nine out of ten.
They often require multiple questions, with five or six emails to get a response.
Support from Splunk is not very helpful because Splunk doesn't have a dedicated APM; they only have one APM engineer in Korea.
They did respond to us, but they did not explicitly inform us about the feature's absence.
The on-demand provisioning of pods and auto-scaling, whether horizontal or vertical, is the best part.
OpenShift's horizontal pod scaling is more effective and efficient than that used in Kubernetes, making it a superior choice for scalability.
Red Hat OpenShift scales excellently, with a rating of ten out of ten.
We've used the solution across more than 250 people, including engineers.
I would rate its scalability a nine out of ten.
The issue is mainly about pricing because if they want to monitor more, it costs money.
It provides better performance yet requires more resources compared to vanilla Kubernetes.
I've had my cluster running for over four years.
It performs well under load, providing the desired output.
I would rate its stability a nine out of ten.
We rarely have problems accessing the dashboard or the page.
Unlike NetScout or regular agents for APM, RUM has many problems during the POC phase because customer environments vary widely.
Learning OpenShift requires complex infrastructure, needing vCenter integration, more advanced answers, active directory, and more expensive hardware.
Red Hat OpenShift's biggest disadvantage is they do not provide any private cloud setup where we can host on our site using their services.
We should aim to include VMware-like capabilities to be competitive, especially considering cost factors.
There is room for improvement in the alerting system, which is complicated and has less documentation available.
Improvements in dashboard configuration, customization, and artificial intelligence functionalities are desired.
Customers sometimes need to create specific dashboards, particularly for applicative metrics such as Java and process terms.
Initially, licensing was per CPU, with a memory cap, but the price has doubled, making it difficult to justify for clients with smaller compute needs.
Red Hat can improve on the pricing part by making it more flexible and possibly on the lower side.
The cost of OpenShift is very high, particularly with the OpenShift Plus package, which includes many products and services.
Splunk is a bit expensive since it charges based on the indexing rate of data.
It appears to be expensive compared to competitors.
Splunk is a little expensive, however, it is in line with the current market pricing.
Because it was centrally managed in our company, many metrics that we had to write code for were available out of the box, including utilization, CPU utilization, memory, and similar metrics.
The concept of containers and scaling on demand is a feature I appreciate the most about Red Hat OpenShift.
A valuable feature of Red Hat OpenShift is its ability to handle increased loads by automatically adding nodes.
Splunk provides advanced notifications of roadblocks in the application, which helps us to improve and avoid impacts during high-volume days.
For troubleshooting, we can detect problems in seconds, which is particularly helpful for digital teams.
It offers unified visibility for logs, metrics, and traces.
Red Hat OpenShift offers a robust, scalable platform with strong security and automation, suitable for container orchestration, application deployment, and microservices architecture.
Designed to modernize applications by transitioning from legacy systems to cloud-native environments, Red Hat OpenShift provides powerful CI/CD integration and Kubernetes compatibility. Its security features, multi-cloud support, and source-to-image functionality enhance deployment flexibility. While the GUI offers user-friendly navigation, users benefit from its cloud-agnostic nature and efficient lifecycle management. However, improvements are needed in documentation, configuration complexity, and integration with third-party platforms. Pricing and high resource demands can also be challenging for wider adoption.
What are the key features of Red Hat OpenShift?Red Hat OpenShift is strategically implemented for diverse industries focusing on container orchestration and application modernization. Organizations leverage it for migrating applications to cloud-native environments and managing CI/CD pipelines. Its functionality facilitates efficient resource management and microservices architecture adoption, supporting enterprise-level DevOps practices. Users employ it across cloud and on-premises platforms to drive performance improvements.
Splunk Observability Cloud offers sophisticated log searching, data integration, and customizable dashboards. With rapid deployment and ease of use, this cloud service enhances monitoring capabilities across IT infrastructures for comprehensive end-to-end visibility.
Focused on enhancing performance management and security, Splunk Observability Cloud supports environments through its data visualization and analysis tools. Users appreciate its robust application performance monitoring and troubleshooting insights. However, improvements in integrations, interface customization, scalability, and automation are needed. Users find value in its capabilities for infrastructure and network monitoring, as well as log analytics, albeit cost considerations and better documentation are desired. Enhancements in real-time monitoring and network protection are also noted as areas for development.
What are the key features?In industries, Splunk Observability Cloud is implemented for security management by analyzing logs from detection systems, offering real-time alerts and troubleshooting for cloud-native applications. It is leveraged for machine data analysis, improving infrastructure visibility and supporting network and application performance management efforts.
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