prometheus.io

One-line introduction

Prometheus is an open-source monitoring system and time-series database originally developed by SoundCloud and later donated to the Cloud Native Computing Foundation (CNCF). As a CNCF graduated project, it has become the de facto standard for cloud-native monitoring, used by millions of developers worldwide to collect, store, and alert on infrastructure and application metrics.

Business overview

Prometheus is not a commercial SaaS product in the traditional sense, but an open-source software project governed and supported by the CNCF ecosystem. Its core capabilities include a multidimensional data model based on label key-value pairs, the flexible PromQL query language, a standalone architecture that does not depend on distributed storage, and metrics collection via a Pull model. Historically, Prometheus was created in 2012, inspired by Google’s Borgmon monitoring system. It joined CNCF in 2016 as the foundation’s second incubating project and graduated in 2018. In terms of industry position, Prometheus is almost irreplaceable in the cloud-native world: more than 90% of Kubernetes clusters use it as the default monitoring solution. Its users range from individual developers to large enterprises, with especially high adoption among teams running containerized and microservices architectures. Because it is open source, Prometheus itself has no free-versus-paid editions, but a rich commercial ecosystem has grown around it, including managed Prometheus services from Grafana Labs, such as Grafana Cloud, and compatible offerings from major cloud providers, such as Amazon Managed Service for Prometheus from AWS.

Who it is for

Prometheus is best suited for the following users. First, teams using Kubernetes or containerized deployments, because it natively supports service discovery and monitoring for dynamic environments. Second, backend developers who need custom metrics collection: with official or third-party Exporters, Prometheus can monitor databases, message queues, hardware devices, and almost any other system. Third, small and medium-sized businesses seeking low cost and high flexibility, since it is open source, free, and backed by extensive community documentation, which lowers the barrier to entry. It is less suitable for scenarios such as enterprises with strong long-term data retention requirements of more than 30 days, because Prometheus’ single-node storage design can suffer performance degradation when data volume becomes too large; operations teams that need complex reporting or traditional IT monitoring, since alerting rules and dashboards usually require Grafana integration; and small teams that cannot afford the operational burden of self-hosting. If there are no dedicated operations staff, a managed service may be easier to maintain.

Key features and highlights

• Multidimensional data model: Uses a label-based key-value design, allowing flexible filtering, aggregation, and slicing of metrics. For example, users can query CPU usage by service name, instance ID, region, and other dimensions.
• PromQL query language: A powerful functional query language that supports arithmetic operations, aggregation, time-series forecasting such as the Holt-Winters algorithm, and complex monitoring and alerting logic.
• Pull-based collection model: Actively pulls metrics from target endpoints. Compared with the Push model, this makes it easier to detect service availability and does not require installing an Agent on the target, only exposing an HTTP endpoint.
• Service discovery and auto-registration: Natively supports multiple service discovery mechanisms, including Kubernetes, Consul, and EC2. New Pods or nodes are automatically added to monitoring when they come online, making it well suited to dynamically scaling cloud-native environments.
• Alert management with Alertmanager: An independent alerting component that supports deduplication, grouping, silencing, and routing. It can integrate with notification channels such as Slack, email, and PagerDuty to reduce alert storms.
• Rich Exporter ecosystem: The community has contributed 300+ official and third-party Exporters covering common middleware such as MySQL, Redis, Nginx, and JMX. Almost any system can be connected to monitoring through an Exporter.

Pricing analysis

The open-source version of Prometheus is completely free, with no licensing fees. Users only need to cover server hardware and operational costs. For a small-scale deployment, such as a single instance collecting up to 100,000 time series, a 4-core 8GB cloud server is usually sufficient, with a monthly cost of around $50-100 depending on the cloud provider. If long-term storage or high availability is required, Prometheus can be combined with extensions such as Thanos or VictoriaMetrics. These components are also open source and free, so the additional cost mainly comes from storage and compute resources. For managed solutions, the free tier of Grafana Cloud includes 100,000 time series and 14 days of data retention, with usage-based billing beyond that. AWS’s managed Prometheus service charges based on ingested metrics and query volume, with monthly costs starting from several dozen dollars. Overall, Prometheus is in the “low-cost” category among similar products, especially for budget-constrained teams. However, it is worth noting that if operational capabilities are limited, the hidden costs of self-hosting Prometheus, such as troubleshooting, data migration, upgrades, and maintenance, may far exceed the cost of a managed service. There is no public refund policy, since the open-source software itself does not involve paid purchases.

How users in China can use it

In terms of network accessibility, the Prometheus open-source project is hosted on GitHub. Users in China can download it through mirror sites, such as the CNCF mirror on Alibaba Cloud, or use proxies to speed up access. Community documentation and official manuals are not restricted, though some English-language resources may require network acceleration for smooth access. As for payment, Prometheus itself does not require payment. However, if using Grafana Cloud or AWS managed services, users may need to bind an international credit card or pay through a local reseller. AWS supports UnionPay cards and Chinese invoices. Chinese users can deploy Prometheus directly on domestic cloud servers without additional proxy tools, and monitored targets such as MySQL and Nginx can run entirely within an internal network. For enterprise users that require invoices, self-hosted deployments cannot provide invoices unless third-party support services are purchased, while managed services such as Alibaba Cloud Managed Service for Prometheus can issue VAT invoices normally. Domestic alternatives include Alibaba Cloud Managed Service for Prometheus, which is compatible with the open-source API and requires no operations work; Tencent Cloud Cloud Monitor, which includes built-in container monitoring; and open-source alternatives such as Thanos for high-availability storage and VictoriaMetrics for performance-optimized deployments.

Pros and cons

Pros:

• ✅ Open source and free, with no licensing restrictions, an active community, and rich documentation.
• ✅ The cloud-native monitoring standard, deeply integrated with Kubernetes and automated service discovery.
• ✅ Powerful multidimensional data model and PromQL, suitable for complex monitoring scenarios.
• ✅ Modular design, including Exporter, Alertmanager, and Pushgateway, making it easy to extend.
• ✅ Mature ecosystem, with Grafana visualization support and one-stop alerting and dashboard workflows.

Cons:

• ❌ Single-node storage architecture. Default data retention is only 15-30 days, and long-term storage requires additional deployment of Thanos or VictoriaMetrics.
• ❌ Steep learning curve. PromQL syntax is complex and takes time for beginners to master.
• ❌ No built-in UI dashboard. Grafana is usually required, which increases deployment complexity.
• ❌ High-availability setups, such as active-active or failover deployments, require manual configuration. There is no official out-of-the-box solution.
• ❌ Performance can decline in large-scale clusters with millions of time series, requiring collection interval optimization or horizontal scaling solutions.

Comparison with similar products

• Grafana Mimir: Maintained by Grafana Labs and fully compatible with Prometheus. It provides built-in long-term storage, high availability, and a global view, making it suitable for teams that need a managed service. However, the community edition of Mimir is more complex to deploy, and the commercial version is paid.
• VictoriaMetrics: A Prometheus-compatible time-series database with higher performance, capable of handling millions of metrics on a single node, better storage compression, and support for PromQL queries. It is suitable for users with extreme performance requirements, though its ecosystem is not as rich as Prometheus’.
• InfluxDB: A general-purpose time-series database that supports the SQL-style Flux query language, but it is not dedicated to cloud-native monitoring and is less deeply integrated with Kubernetes. It is suitable for scenarios that need to handle both monitoring and IoT data, but it has fallen behind Prometheus in the monitoring space.

Final recommendation

Prometheus is best suited for teams that already use Kubernetes or containerized deployments, need custom metrics monitoring, have limited budgets, and possess a certain level of operations capability. It is recommended to start with the official Docker image or Helm Chart in a development environment, get familiar with PromQL and alerting rules, and then move it into production. For small and medium-sized clusters with fewer than 100 nodes, a single Prometheus instance is usually sufficient. If the data volume is large or long-term storage is required, it can be paired with Thanos or replaced with a managed service from a cloud provider. Prometheus is less suitable for teams without dedicated operations staff, those that need an out-of-the-box UI, or organizations with strict compliance requirements for data retention, such as the financial industry. Overall, Prometheus is a cornerstone of cloud-native monitoring and is worth learning and using for every developer.