System exporters are designed to collect metrics at the operating system and hardware level. These exporters are crucial for infrastructure monitoring, giving Prometheus visibility into system performance such as CPU load, memory usage, disk I/O, and networking activity. They are typically lightweight daemons deployed on each host you want to monitor.

• Node Exporter: The most widely used exporter for Linux/Unix systems. It exposes detailed metrics such as CPU usage, memory consumption, file system status, load average, and network statistics.
• Windows Exporter: Formerly known as WMI Exporter, it collects metrics from Windows environments, including system uptime, memory and CPU utilization, and disk I/O, using native performance counters.
• IPMI Exporter: Collects hardware-level metrics directly from server baseboards through the Intelligent Platform Management Interface (IPMI). Useful for monitoring temperature, fan speed, and power supplies.
• DCGM Exporter: Provides NVIDIA GPU telemetry for systems running on GPU-accelerated environments. Ideal for machine learning or high-performance computing workloads.

Database exporters allow Prometheus to collect critical performance and health metrics from various database engines. These exporters act as intermediaries, transforming database-specific statistics into Prometheus-compatible formats. They help teams monitor query throughput, connection counts, cache hits, replication status, and general database performance over time.

• PostgreSQL Exporter: Gathers metrics from PostgreSQL databases, including active connections, cache hit ratios, replication lag, and table-level statistics. It provides deep insights into PostgreSQL's internal performance metrics.
• MySQL Exporter: Collects data from MySQL and MariaDB instances, including query operations, table locks, open file limits, and InnoDB metrics. It’s valuable for identifying slow queries and performance bottlenecks.
• MongoDB Exporter: Retrieves performance data from MongoDB clusters, such as memory usage, active connections, operation counters, replication status, and index access patterns.
• Redis Exporter: Exposes Redis-specific metrics like keyspace hits/misses, memory usage, expired keys, eviction rates, and connected clients. It helps maintain visibility into cache health and efficiency.
• Cassandra Exporter: Designed to monitor Apache Cassandra nodes, presenting metrics such as compaction activity, pending tasks, read/write latency, and heap memory utilization.

Web and application server exporters help Prometheus monitor the operational health and performance of popular web servers and application frameworks. By collecting key metrics from HTTP servers, Java applications, and other middleware, these exporters provide insights into resource usage, request rates, error rates, and request latencies. This enables teams to quickly detect bottlenecks and maintain high availability for critical web services.

• Apache Exporter: Captures vital metrics from Apache HTTP servers such as active connections, request rates, traffic volume, and server uptime. It aids in understanding user load and server performance trends.
• Nginx Exporter: Exposes status metrics from the Nginx web server, including connections, responses, uptime, and frequently accessed endpoints. Useful for monitoring reverse proxies and web server pools.
• JMX Exporter: Monitors Java Virtual Machine (JVM) applications—commonly used with Java web servers, Kafka, Cassandra, and Tomcat. It reports on memory consumption, thread activity, garbage collection, and custom application-level metrics.
• PHP-FPM Exporter: Gathers statistics from PHP FastCGI Process Manager, including active processes, requests, memory usage, and slow request counts. Ideal for tracking PHP application health.
• uWSGI Exporter: Collects metrics from uWSGI servers, covering requests, errors, active workers, and queue sizes. Valuable for web applications running Python with uWSGI.
• Gunicorn Exporter: Designed for Python web applications using the Gunicorn WSGI server, it exposes metrics like worker utilization, active connections, and request throughput.

Cloud platform and service exporters enable Prometheus to collect metrics from popular cloud providers and managed services. These exporters convert cloud-native monitoring data into Prometheus-compatible formats, giving teams unified observability across cloud infrastructure, workloads, and resources. Common scenarios include tracking uptime, usage, and performance metrics for services hosted on AWS, Azure, Google Cloud, and more.

• AWS CloudWatch Exporter: Bridges Amazon's CloudWatch monitoring service with Prometheus, exposing metrics for EC2, RDS, ELB, S3, and other AWS resources. Useful for tracking instance status, requests, errors, database throughput, and cloud-scale workloads.
• Azure Metrics Exporter: Fetches metrics from Microsoft Azure Monitor, covering VMs, storage accounts, network interfaces, functions, and additional Azure resources. Supports scraping metrics from multiple subscriptions and services for holistic Azure visibility.
• Google Stackdriver Exporter: Integrates with Google Cloud Monitoring (formerly Stackdriver), making data from Compute Engine, Kubernetes Engine (GKE), Pub/Sub, and storage buckets available to Prometheus. Lets teams monitor cloud-native workloads alongside traditional infrastructure.
• Additional Cloud Exporters: Exporters are also available for platforms like IBM Cloud, Alibaba Cloud, and managed SaaS solutions, each tailored to extract resource metrics and expose them for collection.
• Synthetic & Third-Party Service Exporters: Some exporters pull data from services like synthetic monitoring tools or managed logging/observability solutions, ensuring Prometheus can aggregate external monitoring data with internal cloud metrics in one place.

In addition to system, database, web, and cloud exporters, Prometheus supports a variety of major exporters that bridge monitoring gaps in complex environments. These exporters cover networking hardware, messaging systems, and general-purpose endpoint probing. They make it possible to monitor devices, protocols, and services that typically lack native Prometheus support, providing key observability for infrastructure reliability and availability.

• Blackbox Exporter: Enables probing of endpoints over HTTP, HTTPS, DNS, TCP, and ICMP. It is ideal for synthetic monitoring to check service availability and network reachability from the outside, tracking uptime and global health of web assets.
• SNMP Exporter: Gathers metrics from network devices such as routers, switches, and firewalls using the Simple Network Management Protocol (SNMP). It provides granular insights into device performance, interface statistics, and hardware status, which is essential for network infrastructure monitoring.
• cAdvisor: Focused on container environments, cAdvisor collects metrics related to resource usage and performance of running containers. It integrates seamlessly with Prometheus and is useful for monitoring Docker and Kubernetes workloads.
• Kafka Exporter: Exposes crucial metrics from Apache Kafka brokers and consumers, including topic offsets, partition lags, and consumer group activity. This exporter is vital for tracking the reliability and efficiency of message pipelines.
• RabbitMQ Exporter: Connects to RabbitMQ servers to collect queue lengths, message rates, connection counts, and node health. It helps teams ensure message delivery performance and detect bottlenecks in event-driven systems.
• HAProxy Exporter: Extracts metrics from HAProxy load balancers, covering frontend/backend status, session counts, error rates, and throughput. Monitoring HAProxy ensures optimal traffic distribution and high-availability setups.
• Elasticsearch Exporter: Presents indices, shards, node health, and query latencies from Elasticsearch clusters. This exporter enables search performance tuning and cluster health monitoring.

The table below provides a quick reference of widely-used Prometheus exporters categorized by their primary use cases. These exporters cover a range of targets including infrastructure, services, databases, and application platforms.

Integrating exporters with Prometheus involves deploying exporter services, configuring Prometheus to scrape their metrics endpoints, and using the collected data for visualization and alerts. This process enables unified monitoring across diverse systems and applications.

• Deploy the Exporter: Run the exporter as a separate service or sidecar alongside the application or system you want to monitor. Ensure it’s accessible from your Prometheus server, as the exporter will expose metrics on a specific HTTP endpoint (commonly /metrics).
• Configure in Prometheus: Edit the Prometheus configuration file (typically prometheus.yml) to add a new scrape job for the exporter. Specify the exporter's address and the metrics path. For example:

  scrape_configs:
    - job_name: 'my-exporter'
      static_configs:
        - targets: ['exporter-host:exporter-port']
        

  This tells Prometheus where to find and scrape the metrics.
• Restart Prometheus: After updating the configuration, restart the Prometheus server to apply changes. Prometheus will begin scraping metrics from the exporter at regular intervals specified in the configuration.
• Verify Integration: Use the Prometheus web interface’s "Targets" page to check that the exporter is correctly registered and being scraped. Metrics from the exporter will now be available to query using PromQL.
• Visualize and Alert: Leverage Prometheus or third-party tools like Grafana to build dashboards, visualize trends, and set up alert rules for the exported metrics. This completes the integration by enabling actionable insights and notifications.

Following these steps, you can effectively bridge Prometheus and any application or system via exporters, ensuring consistent observability for your infrastructure and services.

Prometheus exporters are available as both official and community-supported projects, each serving to expose vital metrics from systems, applications, and services in a Prometheus-friendly format. Understanding the differences between these two categories is essential for choosing the right exporter for your monitoring needs.

• Official Exporters: These exporters are maintained directly by the Prometheus team or within the Prometheus GitHub organization. They are vetted, well-documented, follow Prometheus best practices, and are considered production-ready. Official exporters tend to cover popular technologies and receive timely updates and bug fixes.
• Community Exporters: Community-supported exporters are created and maintained by independent developers, organizations, or open-source contributors outside the core Prometheus team. They fill important gaps by supporting a wide array of less common software, hardware, or services. While many are robust and widely adopted, their maintenance, documentation, and longevity may vary.

When selecting an exporter, consider factors such as maintenance activity, documentation quality, user adoption, and compatibility with your Prometheus version. Official exporters are preferred for critical monitoring, but community exporters often provide valuable options for unique or emerging systems not yet covered officially.

• How to Identify: Official exporters are typically listed in the Prometheus documentation or repositories, often marked as “official” in exporter catalogs. Community exporters are listed in extended directories and third-party forums.
• Mix and Match: It’s common to use a blend of both official and community exporters in a production setup to achieve complete coverage across heterogeneous environments.

Following best practices when configuring and operating Prometheus exporters helps ensure stable performance, accurate monitoring, and easier long-term maintenance. These guidelines apply broadly across different types of exporters and deployment scenarios.

• Minimize Exported Metrics: Export only the metrics you actually need. Excessive metrics collection can strain both the exporter and Prometheus, leading to increased storage and CPU usage.
• Secure Exporter Endpoints: Protect the /metrics endpoints using firewalls or reverse proxies with authentication where necessary to prevent exposing internal metrics to unauthorized parties.
• Use Meaningful Labels: Ensure metrics include helpful and standardized labels (like instance ID, environment, or data center) to make filtering, aggregation, and alerting easier and more effective.
• Organize Scrape Jobs Clearly: Keep scrape configurations logically grouped and consistently named in your Prometheus config. This reduces confusion and makes debugging easier.
• Ensure Exporter Reliability: Use monitoring and alerting on the exporters themselves to ensure they are online and performing correctly. If an exporter goes down, so does visibility into that service.
• Understand Exporter Overhead: Some exporters pull data from underlying systems that can be slow or resource-intensive. Validate how much load an exporter introduces and tune scrape intervals accordingly.
• Stay Updated: Keep exporters up to date with the latest versions to benefit from bug fixes, performance improvements, and compatibility with newer versions of Prometheus.
• Test in Staging First: Before deploying new exporters or major configuration changes to production, test everything in a staging environment to ensure expected behavior.

Discovering and selecting the right Prometheus exporter is a critical first step in extending your monitoring capabilities. With so many official, community, and niche exporters available, it’s important to know where and how to find the tools best suited to your infrastructure and workloads.

• Official Exporter Catalogs: Start by exploring Prometheus’s own documentation and official exporter lists. These catalogs are curated for quality and compatibility with the ecosystem.
• Exporter Directories and Aggregators: There are several aggregator websites and open-source directories that list both official and community exporters. These platforms often include descriptions, maintenance status, and user ratings to help you evaluate your options efficiently.
• Vendor Documentation: Many technology vendors and cloud providers maintain custom exporters for their services. Check the documentation and support resources for the software and platforms you use—they frequently provide exporter recommendations or links.
• Open Source Communities: Developer forums, GitHub repositories, and community Slack channels are excellent places to discover exporters for new or niche technologies. Community support often provides real-world usage tips and troubleshooting help.
• Evaluation Tips: When selecting an exporter, check for signs of active maintenance, clear documentation, and compatibility with your Prometheus version. Assess the list of exported metrics and ensure it meets your observability needs.
• Contributions and Requests: If you can’t find an exporter for a specific use case, consider contributing to existing projects or requesting one from open-source communities. Many exporters begin as community-driven solutions responding to shared needs.

By following these approaches, you can discover and evaluate exporters that reliably extend Prometheus into every corner of your tech stack, helping ensure comprehensive and actionable monitoring.