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In the world of cloud-native observability, Prometheus Exporters have emerged as one of the most powerful tools for developers and DevOps engineers to gain deep, real-time insights into system behavior, application performance, and infrastructure health. As we step deeper into 2025, the landscape of Prometheus monitoring has evolved, and exporters have become more versatile, efficient, and tailored for different environments, from microservices and containers to bare metal servers and edge nodes.
This blog takes a comprehensive look at the top Prometheus Exporters in 2025, diving deep into their use cases, benefits, configuration best practices, and how to use them effectively to build a scalable, resilient observability stack. If you're a developer, SRE, platform engineer, or part of an AI/ML Ops team, this guide is crafted with you in mind.
Let’s begin with the fundamentals and then dive into an exhaustive list of top exporters for various technologies and systems.
Prometheus Exporters are lightweight components that collect data from third-party systems, internal applications, or OS-level metrics, and expose them in the Prometheus exposition format on a /metrics HTTP endpoint. This endpoint is then scraped by Prometheus to collect time-series data, enabling detailed dashboards, alerts, and trend analyses.
Many systems, such as databases, message queues, operating systems, hardware components, and custom applications, don’t natively expose metrics in the Prometheus format. Exporters solve this problem by acting as translators between those systems and Prometheus.
These exporters provide metrics in formats like counters (e.g., requests served), gauges (e.g., current memory usage), histograms (e.g., request duration buckets), and summaries (e.g., quantiles like P99 latency).
Prometheus Exporters are designed to be extremely lightweight, with binaries often under 10MB in size, making them ideal for containerized environments, ephemeral workloads, and systems with minimal overhead allowance.
They are the backbone of monitoring setups where direct instrumentation is either impossible or impractical. Developers use exporters to monitor:
By relying on exporters, developers achieve:
Traditional monitoring systems like Nagios or legacy APMs often require heavy agents that consume system resources, involve complex configurations, and lack flexibility in cloud-native ecosystems. In contrast, Prometheus exporters offer:
Developers can rapidly build, test, and evolve their monitoring pipelines by combining the composability of exporters with the expressive power of PromQL, Prometheus’s powerful query language.
Let’s now look at the most widely adopted and effective Prometheus Exporters for 2025. These exporters are actively maintained, used in production at scale, and offer robust integrations with monitoring stacks.
The Node Exporter remains the most fundamental and widely deployed exporter in the Prometheus ecosystem. It collects a comprehensive set of system metrics including:
It’s ideal for:
Key Developer Benefits:
To use Node Exporter, simply download the binary or run the official container image. By default, it runs on port 9100.
If you’re operating in a Windows environment, Windows Exporter is your go-to solution. It collects system metrics from Windows hosts using the Windows Management Instrumentation (WMI) interface.
It supports monitoring:
Windows Exporter integrates easily with MSI installers and can be managed via services. It also provides granular control over enabled collectors, making it suitable for both minimal and full-stack observability setups on Windows.
Databases are critical, and PostgreSQL Exporter gives developers rich observability into their PostgreSQL instances. It connects to PostgreSQL databases and exposes metrics like:
Developer-centric highlights:
Perfect for backend engineers monitoring API latencies tied to DB performance.
Similar to PostgreSQL, this exporter provides deep insight into MySQL and MariaDB performance. Metrics include:
Deploying it alongside the database service container ensures efficient metric collection with minimal performance overhead.
Redis Exporter surfaces valuable performance indicators such as:
Developers integrating Redis for caching or session management benefit greatly from this visibility, especially when diagnosing slowdowns or evictions under load.
Kafka is integral to modern streaming applications. The Kafka Exporter exposes metrics like:
It helps developers track real-time streaming reliability and detect consumer lag or broker bottlenecks before they impact downstream systems.
Unlike other exporters, Blackbox Exporter is not tied to internal metrics but instead tests the health of external endpoints via:
This is crucial for synthetic monitoring, where SREs and developers simulate user requests to ensure uptime and availability.
For JVM-based applications like Kafka, Cassandra, and Spring Boot, JMX Exporter translates JMX metrics into Prometheus format. It’s often embedded as a Java agent or runs as a sidecar.
It collects:
A must-have for Java developers and platform teams running JVM services.
One of the most exciting new entrants in 2025, the eBPF Exporter leverages the power of Extended Berkeley Packet Filter (eBPF) for low-overhead kernel-level monitoring. It captures:
It’s ideal for:
Since it runs inside the Linux kernel, it provides extremely detailed telemetry with near-zero performance hit.
Sometimes, what you need to monitor is unique. In those cases, writing a custom exporter is both easy and rewarding.
Use cases:
Languages commonly used:
All you need is to expose an HTTP endpoint with metrics in the standard Prometheus format.
Use community-curated resources like ExporterHub, PromCat, and Awesome Prometheus Exporters to discover stable and well-documented exporters. Validate that the exporter is actively maintained and supports the features you need.
Developers moving from traditional monitoring systems or black-box APMs gain:
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