Datadog and Splunk compete in the application performance monitoring category. Based on the provided data, Datadog seems to have an edge in integration versatility and ease of use, while Splunk provides comprehensive data visibility and robust monitoring functionalities.
Features: Datadog offers hosted solutions that simplify infrastructure dependencies and supports features like sharable dashboards, Slack integration, and extensive AWS integrations. It is praised for intuitive tagging and a seamless user interface. Splunk provides a full view of server metrics with unified log visibility and efficient monitoring excels in large-scale data environments, aided by its no-sample policy for deep insights.
Room for Improvement: Datadog could enhance areas such as metric control, API consistency, and integration support, coupled with more inline notifications. Splunk would benefit from flexible synthetic monitoring outputs, improved scalability with microservices, and streamlined alert setups. Usability and cost forecasting remain challenges for both services.
Ease of Deployment and Customer Service: Datadog is well-suited for various cloud environments and is known for mostly responsive customer service. Some users encounter initial complexities necessitating better integration details. Splunk offers quality customer support with some delays and is known for smooth private and hybrid cloud integration. Splunk's in-stream support tends to surpass Datadog's service lags.
Pricing and ROI: Datadog employs a usage-based pricing model, which can lead to high costs if unchecked but promises ROI through operational streamlining. Splunk commands higher costs justified by robust features, with potential future pricing pressures. Datadog appeals with cost-effectiveness and integration benefits, while both provide noticeable ROI in incident response and infrastructure understanding.
Problem resolution typically takes between two and five days, which isn't very helpful.
If any issues arise, we can raise a vendor case, and resolutions are provided in a timely and accurate manner.
They did not have a clear answer.
We've used the solution across more than 250 people, including engineers.
I would rate its scalability an eight out of ten.
We rarely have problems accessing the dashboard or the page.
I would rate its stability a nine out of ten.
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.
It would be beneficial if server details could be retrieved directly in synthetic monitoring.
Customers sometimes need to create specific dashboards, particularly for applicative metrics such as Java and process terms.
There is room for improvement in the alerting system, which is complicated and has less documentation available.
The setup cost for Datadog is more than $100.
Splunk Observability Cloud is expensive.
It appears to be expensive compared to competitors.
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.
It offers unified visibility for logs, metrics, and traces.
Saving time with automation can save us weeks. It's improving our resilience.
Unlike other APMs, Splunk's service map is quite effective.
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 combines log search, data integration, and dashboards for seamless monitoring, enhancing infrastructure visibility and security. Its cloud integration and scalability support diverse environments, improving operational efficiency.
Splunk Observability Cloud offers comprehensive monitoring tools with user-friendly interfaces, enabling end-to-end infrastructure visibility. Its real-time alerting and predictive capabilities enhance security monitoring, while centralized dashboards provide cross-platform visibility. Users benefit from fast data integration and extensive insights into application performance. Despite its advantages, improvements could be made in integration with other tools, data reliability, scalability, and cost management. Users face challenges in configuration complexity and require better automation and endpoint protection features. Enhancing AI integration, alerts, and adaptation for high-throughput services could further improve usability.
What are the key features of Splunk Observability Cloud?In industries like finance and healthcare, Splunk Observability Cloud is implemented for application performance monitoring and infrastructure metrics. Its ability to track incidents and analyze machine data benefits network infrastructure, while distributed tracing and log analysis aid in tackling security threats. Organizations often integrate it for compliance and auditing purposes, enhancing visibility into network traffic and optimizing performance.
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