In today’s cloud-native world of microservices, containers, and Kubernetes, observability has become a top priority. Modern systems generate massive amounts of metrics, and without proper monitoring, debugging becomes guesswork.
This is where Prometheus, a Cloud Native Computing Foundation (CNCF) graduated open-source monitoring system, becomes the industry standard.
This blog will walk you through what Prometheus is, how it works, its architecture, installation, configuration, alerting, and real-world use cases — end to end.
What is Promethus?
Prometheus is an open-source systems monitoring and alerting toolkit originally built at SoundCloud. Since its inception in 2012, many companies and organizations have adopted Prometheus, and the project has a very active developer and user community. It is now a standalone open source project and maintained independently of any company. To emphasize this, and to clarify the project’s governance structure, Prometheus joined the Cloud Native Computing Foundation in 2016 as the second hosted project, after Kubernetes.
Prometheus collects and stores its metrics as time series data, i.e. metrics information is stored with the timestamp at which it was recorded, alongside optional key-value pairs called labels.
Features of Prometheus
Visualization – Prometheus offers multiple types of graphs and dashboards. Thus, users can quickly gain insights from collected metrics.
Multidimensional data model – Prometheus uses time-series data, which is identified by metric name and key-value pairs. Additionally, this model allows for highly flexible data organization.
PromQL – It provides a flexible querying language that can leverage the multi-dimensional data model. As a result, users can perform complex queries efficiently.
No reliance on distributed storage – All single server nodes remain autonomous. Therefore, there is no dependency on external storage systems.
Pull model – Prometheus can collect time-series data by actively “pulling” data over HTTP. Consequently, monitoring becomes more reliable and consistent.
Pushing time-series data – This is available through the use of an intermediary gateway. Moreover, it complements the pull model for scenarios where push is necessary.
Monitoring target discovery – Targets can be discovered through static configuration or service discovery. Hence, it adapts easily to dynamic environments.
How Does Prometheus Monitoring Work?
To get metrics, Prometheus requires an exposed HTTP endpoint. Once an endpoint is available, Prometheus can start scraping numerical data, capture it as a time series, and store it in a local database suited to time-series data. Prometheus can also be integrated with remote storage repositories.
Users can leverage queries to create temporary times series from the source. These series are defined by metric names and labels. Queries are written in PromQL, a unique language that allows users to choose and aggregate time-series data in real time. PromQL can also help you establish alert conditions, resulting in notifications to external systems like email, PagerDuty, or Slack.
Prometheus can display collected data in tabular or graph form, shown in its web-based user interface. You can also use APIs to integrate with third-party visualization solutions like Grafana.
What Can You Monitor with Prometheus?
Prometheus is a versatile monitoring tool, which you can use to monitor a variety of infrastructure and application metrics. Here are a few common use cases.
Service Metrics
Prometheus is typically used to collect numeric metrics from services that run 24/7 and allow metric data to be accessed via HTTP endpoints. This can be done manually or with various client libraries. Prometheus exposes data using a simple format, with a new line for each metric, separated with line feed characters. The file is published on an HTTP server that Prometheus can query and scrape metrics from based on the specified path, port, and hostname.
Prometheus can also be used for distributed services, which are run on multiple hosts. Each instance publishes its own metrics and has a name that Prometheus can distinguish.
Host Metrics
You can monitor the operating system to identify when a server’s hard disk is full or if a server operates constantly at 100% CPU. You can install a special exporter on the host to collect the operating system information and publish it to an HTTP-reachable location.
Website Uptime/Up Status
Prometheus doesn’t usually monitor website status, but you can use a blackbox exporter to enable this. You specify the target URL to query an endpoint, and perform an uptime check to receive information such as the website’s response time. You define the hosts to be queried in the prometheus.yml configuration file, using relabel_configs to ensure Prometheus uses the blackbox exporter.
Cronjobs
To check if a cronjob is running at the specified intervals, you can use the Push Gateway to display metrics to Prometheus through an HTTP endpoint. You can push the timestamp of the last successful job (i.e. a backup job) to the Gateway, and compare it with the current time in Prometheus. If the time exceeds the specified threshold, the monitor times out and triggers an alert
Why Use Prometheus for Kubernetes Monitoring?
Prometheus is a common choice for Kubernetes monitoring, because it was built for a cloud-native environment. Here are several key benefits of using Prometheus to monitor Kubernetes workloads:
- Multidimensional data model – The use of key-value pairs creates a similarity to how Kubernetes uses labels to organize infrastructure metadata. This similarity ensures time-series data can be collected and analyzed accurately by Prometheus.
- Accessible format and protocols – Prometheus enables easy and simple exposure of metrics. It ensures metrics are human-readable and can be published via standard HTTP transport.
- Service discovery – Prometheus server periodically scrapes targets. Services and applications do not have to constantly emit data—metrics are pulled, instead of pushed. Prometheus servers can employ several techniques to auto-discover scrape targets. You can, for example, configure the servers to filter and match container metadata.
- Modular and highly available components – Composable services are responsible for performing metric collection, graphical visualization, alerting, and more. Each of these services support sharding and redundancy.
When to use Prometheus?
Prometheus is a highly-reliable open source tool that can be used to monitor any part of your application, including microservices. Because it is vendor-neutral and has a rich open-source community of developers and contributors, you can use it to monitor almost your entire application, including the frontend and backend, servers and hardware, and even infrastructure like a service mesh, as mentioned earlier.
Many open-source tools, such as Istio (service mesh) and CoreDNS (default DNS for Kubernetes) have native Prometheus endpoints. To monitor services that don’t use HTTP endpoints, such as hardware, you can use exporters.
As an open-source tool, Prometheus has other major advantages—it’s free, its code is available on GitHub, and its toolkit is readily customizable.
Prometheus includes AlertManager, which groups and deduplicates alerts before sending out categories of alerts as a single notification. That means less alert fatigue—and you won’t be flooded with constant alerts during an outage.
It’s also highly reliable because its servers are independent, which means that servers can continue functioning even when part of your system is down. This is especially important because you need your monitoring system to remain functional during outages
When not to use Prometheus?
A good engineer knows that it’s not just about using good tools—it’s also about using the right tool for the job. Prometheus is very good at what it does, but it’s not intended to be an all-in-one platform for all of your observability needs.
Here are some examples where you’ll benefit from using another tool. Note that even when another tool is a better fit for a use case, you can still use Prometheus alongside it because it’s often the right tool for monitoring a service.
Long-term data storage:
Prometheus isn’t intended for durable long-term storage. You can use an observability platform or another storage source for long-term storage. For instance, New Relic provides extended storage for up to 13 months for dimensional metrics.
When you need 100% accuracy:
Prometheus prioritizes reliability over accuracy. According to the CAP theorem, you can only have two of three in a distributed system: consistency (accuracy), availability (reliability), and partition tolerance (data collected on separate servers). Since distributed systems always need partition tolerance, there is a tradeoff between reliability and accuracy. While the tradeoff is fairly small, when you need 100% accuracy (such as with a billing system), you’ll need to use another system.
Automatic setup for your environment:
An observability platform can automatically detect services and instrument them so you get observability in minutes. With Prometheus, you need to configure different services. That includes adding configurations to scrape specific HTTP endpoints and setting up exporters for services that don’t use HTTP endpoints. For large distributed systems, that’s a lot of work.
New Relic provides both long-term storage and helps you automatically set up your environment, so you can combine New Relic with Prometheus to benefit from both tools.
Best Practices of Promethus
Use Clear Metric Names and Labels
Always give metrics meaningful names so it’s obvious what they represent. Labels add extra context, like environment (dev, prod) or service type, which makes filtering and analyzing metrics easier later. Clear naming avoids confusion when your system grows.
Scrape Frequently but Wisely
Collect metrics often enough to detect problems quickly, but not so frequently that Prometheus becomes overloaded. Finding the right balance ensures reliable monitoring without affecting performance.
Use Exporters for Non-HTTP Services
Some services or hardware don’t expose metrics via HTTP. In such cases, use exporters like Node Exporter (for server metrics) or Blackbox Exporter (for website uptime). Exporters make these metrics accessible to Prometheus in a standard way.
Leverage PromQL Efficiently
PromQL is powerful, but poorly written queries can slow down Prometheus. Aggregate metrics properly and avoid querying raw data unnecessarily. Efficient queries ensure fast response times and reduce server load.
Set Up Alerting Thoughtfully
Configure alerts to notify only when action is needed. Use AlertManager to group and deduplicate alerts so your team isn’t overwhelmed. Thoughtful alerting prevents “alert fatigue” and ensures critical issues are addressed promptly.
Conclusion
Prometheus is a powerful, open-source monitoring and alerting tool designed for modern cloud-native environments. Its ability to collect, store, and query time-series data makes it ideal for monitoring applications, infrastructure, and Kubernetes workloads. With features like PromQL, exporters, and AlertManager, Prometheus provides flexibility, reliability, and actionable insights.
While it’s not intended for long-term storage or 100% accuracy, combining Prometheus with external tools can give you a complete observability solution. By following best practices—like using clear metric names, efficient queries, proper alerting, and service discovery—you can ensure your systems remain healthy, performant, and easy to manage.
