Operations | Monitoring | ITSM | DevOps | Cloud

Elastic Stack provides many valuable insights for different users. Developers are interested in low-level metrics and debugging information. SREs are interested in seeing everything at once and identifying where the root cause is. Managers want reports that tell them how good service performance is and if the service level agreement (SLA) is met. In this post, we’ll focus on the service perspective and provide an overview of calculating an SLA.

IoT devices open the door to all sorts of computing potential, but they can also produce a flood of telemetry data that users need to properly collect and monitor to ensure those devices are working properly. It’s no wonder so many individuals and businesses use Grafana for IoT use cases, whether they’re starting an aquaponic farm in South Africa, managing an industrial-scale electroplating factory in Ohio, or simply keeping tabs on Pretzel the python at its home in the UK.

Before we dive into the specifics of each alternative to Datadog, let's address the most critical point: scaling. Datadog is great for users who need to do a little bit of everything, but Datadog's biggest weakness is scaling. Datadog can do logs, APM, time series and more, but scaling time-series metrics, alerts, and servers will cause your monthly bill to escalate. The graph below shows what you pay at Datadog vs. MetricFire, a leading competitor.

Internet-of-Things (IoT) devices and technologies have penetrated into our life. Nowadays, we can easily find IoT devices around us. Home appliance manufacturers produce IoT products for smart homes. Car makers invest in smart vehicles that can communicate with other smart cars and IoT objects nearby. Governments allocate huge resources to build smart cities with IoT technologies to improve the quality of life for people and promote economic growth.

Most companies overspend on cloud services. Many factors contribute to this problem, and while organizations know that their cloud budgets are bloated, they still struggle to reel in those costs. Are they implementing the tools and processes to track their cloud costs effectively? And are they adopting controls to instill discipline and accountability in their cloud spending habits? We explored these questions in our latest State of Multi-Cloud Management research report.

Monitoring an enterprise network is challenging for two reasons: the size of the network and the distributed architecture it follows. In the distributed model, branches spread across the globe, requiring a centralized monitoring solution. As the business grows, more devices will be added to the network, meaning your enterprise monitoring tool must be able to scale to match the performance requirements.