

Grafana and AWS Auto Scaling are tools competing in the IT infrastructure management space. AWS Auto Scaling often stands out for its automation features and seamless integration with other AWS services.
Features: Grafana offers advanced data visualization capabilities, customizable dashboards, and rich plugin support. AWS Auto Scaling provides automatic scaling based on demand, integrates seamlessly with other AWS services, and includes detailed scaling metrics.
Room for Improvement: Grafana could enhance its alerting mechanisms, improve support for various data sources, and offer better built-in reporting. AWS Auto Scaling might benefit from more intuitive configuration, detailed documentation, and enhanced user interface.
Ease of Deployment and Customer Service: Grafana is known for its straightforward deployment and strong community support. AWS Auto Scaling, while more complex to configure, benefits from comprehensive AWS ecosystem support and extensive documentation.
Pricing and ROI: Grafana is often recognized for its cost-effectiveness, especially in open-source deployments. AWS Auto Scaling's pricing is more variable, tied to resource usage, leading to potentially higher costs in some scenarios. Users often justify the premium for AWS Auto Scaling due to its automation benefits and integration with AWS infrastructure.
I identified over-provisioned servers and reduced my AWS monthly bill by 15%, which is a significant saving in terms of costs.
AWS support is very good.
The technical support team is very helpful with complex PromQL troubleshooting.
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.
Scalability is impressive, as it allowed us to go from 1,000 to 10,000 active users within a week during a traffic spike.
It is highly scalable and built on a big data architecture capable of ingesting trillions of data points.
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.
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.
Even when handling millions of data points, the visualization layer remains responsive.
This complexity led me to migrate to CloudFormation, which simplifies the deployment process.
It requires a downtime before deploying the Auto Scaling group.
If you could add more training on how to use it correctly and on the functions that I haven't used before or some people have not really used before, that would help.
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 pricing of Auto Scaling is medium range, neither high nor low.
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.
I purchased my Grafana Cloud subscription through the AWS Marketplace, which simplified my procurement process and allowed me to apply the cost towards my AWS committed spend.
During peak traffic times, the Auto Scaling group can be deployed to ensure that the client works well, and the traffic remains average.
The automation aspect where you can automate it to whatever you want is what I value the most about Auto Scaling.
Its automatic scaling capabilities are very useful.
Users can monitor metrics with greater ease, and the tool aids in quickly identifying issues by providing a visual representation of data.
The fact that I can join data from my SQL database with metrics from Prometheus in the same table is a feature I have not found performed as well elsewhere.
Its alerting feature is effective because it allows me to set thresholds to send an email if a certain threshold is met.
| Product | Market Share (%) |
|---|---|
| Grafana | 3.6% |
| AWS Auto Scaling | 0.4% |
| Other | 96.0% |


| Company Size | Count |
|---|---|
| Small Business | 12 |
| Midsize Enterprise | 2 |
| Large Enterprise | 11 |
| Company Size | Count |
|---|---|
| Small Business | 13 |
| Midsize Enterprise | 8 |
| Large Enterprise | 25 |
AWS Auto Scaling monitors your applications and automatically adjusts capacity to maintain steady, predictable performance at the lowest possible cost. Using AWS Auto Scaling, it’s easy to setup application scaling for multiple resources across multiple services in minutes. The service provides a simple, powerful user interface that lets you build scaling plans for resources including Amazon EC2 instances and Spot Fleets, Amazon ECS tasks, Amazon DynamoDB tables and indexes, and Amazon Aurora Replicas. AWS Auto Scaling makes scaling simple with recommendations that allow you to optimize performance, costs, or balance between them. If you’re already using Amazon EC2 Auto Scaling to dynamically scale your Amazon EC2 instances, you can now combine it with AWS Auto Scaling to scale additional resources for other AWS services. With AWS Auto Scaling, your applications always have the right resources at the right time.
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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