Datadog and Splunk Observability Cloud compete in the observability and monitoring solutions market. Datadog seems to have an edge due to its hosted capabilities which reduce infrastructure dependency, offering potentially streamlined operations.
Features: Datadog offers extensive hosted capabilities with sharable dashboards and integrations with platforms like Slack and AWS, making it a comprehensive solution for monitoring. Its interface is easy to use, offering intuitive tag utilization for metrics and alerts creation. Splunk Observability Cloud provides powerful dashboard creation and data collection integration for various environments, allowing customizability and deep visibility into application performance.
Room for Improvement: Datadog could enhance its dashboard sharing controls, streamline API integration, and improve pricing transparency. Users are seeking better customization options and real-time data insights. Splunk could reduce its complexity, offer better pricing models, and enhance endpoint protection to remain competitive. Users often find cost and configuration complexity challenging.
Ease of Deployment and Customer Service: Datadog supports versatile deployment and offers responsive customer support, although the support speed can vary. Its setup is straightforward, especially helpful during initial configurations. Splunk supports multi-cloud configurations but may involve more complexity. Both products offer positive customer service experiences, although response times may occasionally lag.
Pricing and ROI: Datadog's competitive pricing with flexible module-based options is attractive; however, usage-based plans require careful monitoring to prevent unexpected costs. It generally provides good ROI. Splunk, while effective, is seen as expensive; thus, clarity in pricing models and cost-effectiveness is desired. The ROI for both solutions varies depending on organizational scale and specific usage contexts, with Datadog being favored for predictable costs.
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.
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.
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.
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.
The documentation is adequate, but team members coming into a project could benefit from more guided, interactive tutorials, ideally leveraging real-world data.
There should be a clearer view of the expenses.
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.
The setup cost for Datadog is more than $100.
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.
Our architecture is written in several languages, and one area where Datadog particularly shines is in providing first-class support for a multitude of programming languages.
The technology itself is generally very useful.
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.
Datadog is a comprehensive cloud monitoring platform designed to track performance, availability, and log aggregation for cloud resources like AWS, ECS, and Kubernetes. It offers robust tools for creating dashboards, observing user behavior, alerting, telemetry, security monitoring, and synthetic testing.
Datadog supports full observability across cloud providers and environments, enabling troubleshooting, error detection, and performance analysis to maintain system reliability. It offers detailed visualization of servers, integrates seamlessly with cloud providers like AWS, and provides powerful out-of-the-box dashboards and log analytics. Despite its strengths, users often note the need for better integration with other solutions and improved application-level insights. Common challenges include a complex pricing model, setup difficulties, and navigation issues. Users frequently mention the need for clearer documentation, faster loading times, enhanced error traceability, and better log management.
What are the key features of Datadog?
What benefits and ROI should users look for in reviews?
Datadog is implemented across different industries, from tech companies monitoring cloud applications to finance sectors ensuring transactional systems' performance. E-commerce platforms use Datadog to track and visualize user behavior and system health, while healthcare organizations utilize it for maintaining secure, compliant environments. Every implementation assists teams in customizing monitoring solutions specific to their industry's requirements.
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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