

Datadog and Grafana both compete in the observability and monitoring tools category. Based on feature capabilities, integrations, and pricing structures, Datadog appears to have the upper hand due to its comprehensive package and enterprise-level support.
Features: Datadog provides robust hosted monitoring, sharable dashboards, and integrates with numerous services, focusing on real-time metrics and alerting. It supports wide-ranging integrations and offers anomaly detection capabilities. Grafana excels in its open-source dashboard capabilities, offering high customization and flexibility with rich visualization tools, suitable for integrating multiple data sources.
Room for Improvement: Datadog could improve its dashboards, simplify its pricing model, and enhance logging solutions. Users cite challenges with API consistency and front-end integration. Grafana is noted for needing better API support, simplification in use, and improvements in alerting capabilities. Grafana's dependency on other tools for full functionality indicates room for independence.
Ease of Deployment and Customer Service: Datadog supports deployment in various environments, including public, private, and hybrid clouds, offering strong but sometimes inconsistent customer service. Grafana focuses mainly on on-premises and public cloud deployments with community-driven support, lacking the corporate support infrastructure that Datadog provides.
Pricing and ROI: Grafana offers a cost-effective solution with a free open-source option and paid tiers for enhanced features, ideal for small to medium businesses. Datadog's complex usage-based pricing can be expensive but provides significant ROI through advanced functionalities, particularly for large-scale environments.
Previously we had thirteen contractors doing the monitoring for us, which is now reduced to only five.
Datadog has delivered more than its value through reduced downtime, faster recovery, and infrastructure optimization.
I believe features that would provide a lot of time savings, just enabling you to really narrow down and filter the type of frustration or user interaction that you're looking for.
When I have additional questions, the ticket is updated with actual recommendations or suggestions pointing me in the correct direction.
Overall, the entire Datadog comprehensive experience of support, onboarding, getting everything in there, and having a good line of feedback has been exceptional.
I've had a couple instances where I reached out to Datadog's support team, and they have been really super helpful and very kind, even reaching back out after resolving my issues to check if everything's going well.
My advice for people who are new to Grafana or considering it is to reach out to the community mainly, as that's the primary benefit of Grafana.
I do not use Grafana's support for technical issues because I have found solutions on Stack Overflow and ChatGPT helps me as well.
I very rarely get in touch with technical support as we don't have that option.
Datadog's scalability has been great as it has been able to grow with our needs.
We did, as a trial, engage the AWS integration, and immediately it found all of our AWS resources and presented them to us.
Datadog's scalability is strong; we've continued to significantly grow our software, and there are processes in place to ensure that as new servers, realms, and environments are introduced, we're able to include them all in Datadog without noticing any performance issues.
In terms of our company, the infrastructure is using two availability zones in AWS.
In assessing Grafana's scalability, we started noticing logs missing or metrics not syncing in time.
Datadog is very stable, as there hasn't been any downtime or issues since I've been here, and it's always on time.
Datadog seems stable in my experience without any downtime or reliability issues.
These incidents are related to log service, indexes, and metric capturing issues.
When something in their dashboard does not work, because it is open source, I am able to find all the relative combinations that people are having, making it much easier for me to fix.
Once you get to a higher load, you need to re-evaluate your architecture and put that into account.
The product has been stable.
It would be great to see stronger AI-driven anomaly detection and predictive analytics to help identify potential issues before they impact performance.
The documentation is adequate, but team members coming into a project could benefit from more guided, interactive tutorials, ideally leveraging real-world data.
In future updates, I would like to see AI features included in Datadog for monitoring AI spend and usage to make the product more versatile and appealing for the customer.
It would be better if they made the technology easy to use without needing to read extensive documentation.
Grafana cannot be easily embedded into certain applications and offers limited customization options for graphs.
I would want to see improvements, especially in the tracing part, where following different requests between different services could be more powerful.
The setup cost for Datadog is more than $100.
Everybody wants the agent installed, but we only have so many dollars to spread across, so it's been difficult for me to prioritize who will benefit from Datadog at this time.
My experience with pricing, setup cost, and licensing is that it is really expensive.
In an enterprise setting, pricing is reasonable, as many customers use it.
The costs associated with using Grafana are somewhere in the ten thousands because we are able to control the logs in a more efficient way to reduce it.
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.
Having all that associated analytics helps me in troubleshooting by not having to bounce around to other tools, which saves me a lot of time.
Datadog was able to find the alerts and trigger to notify our team in a very prompt manner before it got worse, allowing us to promptly adjust and remediate the situation in time.
Users can monitor metrics with greater ease, and the tool aids in quickly identifying issues by providing a visual representation of data.
Its alerting feature is effective because it allows me to set thresholds to send an email if a certain threshold is met.
It's definitely useful for monitoring, alerting, logs, and analysis.
| Product | Market Share (%) |
|---|---|
| Datadog | 6.6% |
| Grafana | 4.6% |
| Other | 88.8% |


| Company Size | Count |
|---|---|
| Small Business | 80 |
| Midsize Enterprise | 46 |
| Large Enterprise | 95 |
| Company Size | Count |
|---|---|
| Small Business | 13 |
| Midsize Enterprise | 8 |
| Large Enterprise | 24 |
Datadog integrates extensive monitoring solutions with features like customizable dashboards and real-time alerting, supporting efficient system management. Its seamless integration capabilities with tools like AWS and Slack make it a critical part of cloud infrastructure monitoring.
Datadog offers centralized logging and monitoring, making troubleshooting fast and efficient. It facilitates performance tracking in cloud environments such as AWS and Azure, utilizing tools like EC2 and APM for service management. Custom metrics and alerts improve the ability to respond to issues swiftly, while real-time tools enhance system responsiveness. However, users express the need for improved query performance, a more intuitive UI, and increased integration capabilities. Concerns about the pricing model's complexity have led to calls for greater transparency and control, and additional advanced customization options are sought. Datadog's implementation requires attention to these aspects, with enhanced documentation and onboarding recommended to reduce the learning curve.
What are Datadog's Key Features?In industries like finance and technology, Datadog is implemented for its monitoring capabilities across cloud architectures. Its ability to aggregate logs and provide a unified view enhances reliability in environments demanding high performance. By leveraging real-time insights and integration with platforms like AWS and Azure, organizations in these sectors efficiently manage their cloud infrastructures, ensuring optimal performance and proactive issue resolution.
Grafana is an open-source visualization and analytics platform that stands out in the field of monitoring solutions. Grafana is widely recognized for its powerful, easy-to-set-up dashboards and visualizations. Grafana supports integration with a wide array of data sources and tools, including Prometheus, InfluxDB, MySQL, Splunk, and Elasticsearch, enhancing its versatility. Grafana has open-source and cloud options; the open-source version is a good choice for organizations with the resources to manage their infrastructure and want more control over their deployment. The cloud service is a good choice if you want a fully managed solution that is easy to start with and scale.
A key strength of Grafana lies in its ability to explore, visualize, query, and alert on the collected data through operational dashboards. These dashboards are highly customizable and visually appealing, making them a valuable asset for data analysis, performance tracking, trend spotting, and detecting irregularities.
Grafana provides both an open-source solution with an active community and Grafana Cloud, a fully managed and composable observability offering that packages together metrics, logs, and traces with Grafana. The open-source version is licensed under the Affero General Public License version 3.0 (AGPLv3), being free and unlimited. Grafana Cloud and Grafana Enterprise are available for more advanced needs, catering to a wider range of organizational requirements. Grafana offers options for self-managed backend systems or fully managed services via Grafana Cloud. Grafana Cloud extends observability with a wide range of solutions for infrastructure monitoring, IRM, load testing, Kubernetes monitoring, continuous profiling, frontend observability, and more.
The Grafana users we interviewed generally appreciate Grafana's ability to connect with various data sources, its straightforward usability, and its integration capabilities, especially in developer-oriented environments. The platform is noted for its practical alert configurations, ticketing backend integration, and as a powerful tool for developing dashboards. However, some users find a learning curve in the initial setup and mention the need for time investment to customize and leverage Grafana effectively. There are also calls for clearer documentation and simplification of notification alert templates.
In summary, Grafana is a comprehensive solution for data visualization and monitoring, widely used across industries for its versatility, ease of use, and extensive integration options. It suits organizations seeking a customizable and scalable platform for visualizing time-series data from diverse sources. However, users should be prepared for some complexity in setup and customization and may need to invest time in learning and tailoring the system to their specific needs.
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