Understanding Prometheus and Its Role in Modern Observability
A deeper dive into Prometheus’s architecture, internals, and why it powers today’s cloud-native monitoring
“You cannot improve what you do not measure”
— Peter Drucker
In the world of distributed systems, measurement isn’t just helpful: it’s essential.
Every millisecond matters. Every failure leaves a trace. Every system, from sprawling microservice meshes to legacy monoliths, emits various kinds of signals: what we affectionately call metrics.
Capturing these signals with precision and interpreting them with clarity gives us something rare: an unfiltered window into the soul of our infrastructure.
This is actually where Prometheus shines.
Born out of the real-world chaos of SoundCloud’s growing architecture in 2012, Prometheus wasn’t built in a vacuum. It was forged in production fires; out of necessity, not just mere theory.
It emerged not just as another monitoring tool, but as a new way of thinking about observability. Pull-based metrics collection, dimensional labeling, a purpose-built time-series engine: these weren’t afterthoughts.
They were fundamental choices that made Prometheus both uniquely elegant and brutally effective in a vast variety of environments.
Over the years, Prometheus has become the beating heart of modern observability stacks. Kubernetes speaks its language natively. Grafana bends to its will. And entire ecosystems, from service meshes to CI/CD pipelines, anchor themselves around its powerful data model.
In this article, we’ll trace the arc of Prometheus: from its pragmatic beginnings to its robust and surprisingly minimal core.
We’ll explore how it stores and retrieves metrics, what makes its architecture so efficient (yet so hard to scale), and how it fits into the broader world of cloud-native telemetry.
We’ll also touch on where it’s going next: remote storage, federation, long-term durability, and the emerging shift toward unified observability powered by projects like Thanos, Cortex, and OpenTelemetry.
Whether you’re just discovering Prometheus or you’ve been scraping metrics since the days of node_exporter, this is a journey into the internals of a tool that changed how we see the systems we build.
SoundCloud and the Search for Scalable Monitoring
Back in 2012, SoundCloud was transitioning from a relatively simple web application into a sprawling ecosystem of microservices. With that shift came an explosion in complexity; dozens of teams, hundreds of services, and countless moving parts, all generating operational signals in real time.
Visibility into this distributed system was becoming a critical concern, not just for debugging failures, but for understanding system behavior, capacity trends, and performance bottlenecks.
“We didn’t start by thinking, ‘Let’s build a monitoring system.’ We started by needing answers”
recalled Julius Volz, one of Prometheus’s co-creators. At the time, services were failing silently. Dashboards were actually brittle. The engineering team needed something self-contained, reliable, and extensible; a system that wouldn't require constant tending.
“We had this belief that a monitoring system should just run. It shouldn’t need its own team to babysit it”
Yet the monitoring landscape they surveyed back then was ill-suited to this reality.
StatsD offered a lightweight, fire-and-forget model, where applications would send counters and timers over UDP to a central aggregation daemon.
While this worked well for simple metrics (like request counts or timing distributions), it lacked the ability to represent rich, contextualized data.
There were no labels, no dimensional filtering: just flat metric names. Worse, its UDP-based protocol introduced packet loss under load and was very difficult to secure in large, heterogeneous networks.
Graphite, on the other hand, provided a persistent time-series backend and a basic query interface. It allowed for hierarchical metric naming (e.g., webapp.prod.us-east1.api.requests.count), but this rigidity came at a cost: it couldn’t handle dynamic label sets, which are essential in modern environments.
For example, you couldn’t easily ask, “What’s the average request latency broken down by HTTP status code and method across all endpoints?” That level of dimensionality simply wasn’t expressible in Graphite’s metric model.
Moreover, both systems suffered from architectural limitations. Graphite’s write path was disk-bound and could become I/O-constrained under high cardinality.
Scaling required external solutions like HBase or Cassandra, introducing considerable operational burden and intrinsic complexity.
StatsD had no storage at all. Neither offered a native alerting language or built-in service discovery: two features critical for managing ephemeral infrastructure.
Volz and the team tested various setups, including Graphite paired with StatsD, and even explored OpenTSDB. But they quickly ran into fundamental issues.
“We started out thinking: maybe we can just use existing systems, like Graphite, OpenTSDB, but they broke down really quickly. Everything had some critical flaw: too heavy, not reliable, or required six other services to function.”
Volz later reflected.
The decision to build Prometheus from scratch wasn’t about chasing novelty. Instead, it was about mere necessity. They needed something that actually worked at SoundCloud’s scale, with zero tolerance for fragility.
What they envisioned required a few key design principles:
A multi-dimensional data model, where metrics could carry context via arbitrary key-value pairs (labels), not just a flat name.
A query language expressive enough to slice, dice, aggregate, and correlate metrics on the fly.
Efficient time-series storage, optimized for high-ingest rates, small per-sample overhead, and real-time reads, all without relying on external databases.
A standalone system, deployable in a single binary, with zero external dependencies, and fast enough to run in production without tuning a JVM or maintaining Kafka clusters.
So they built Prometheus.
It was designed from first principles to be operationally simple, resource-efficient, and opinionated about how monitoring should work. Instead of adopting a push model like StatsD, Prometheus scraped targets via HTTP on a fixed schedule, letting it control ingest behavior, measure scrapes, and apply consistent labeling.
At its heart was a custom time-series database, written in Go, built to store millions of active series in memory and persist them to disk in compact, append-only chunks.
It didn’t aim to store data forever (retention defaults were measured in days, not months) but it was blazingly fast for recent data, which is what most incidents and dashboards care about anyway.
Prometheus also boldly introduced PromQL, a functional, declarative query language tailored specifically for time-series operations.
Unlike SQL, PromQL works natively with metric vectors over time, offering operators like rate(), sum() by (label), and increase() that are intuitive for observability use cases. You could compute latency percentiles, derive error rates, and trigger alerts, all using the same syntax.
From the start, dimensionality wasn’t an afterthought; it was the core design goal.
“Labels are the soul of Prometheus”
said Brian Brazil, the project’s most prolific contributor and long-time maintainer. He often pointed to the limitations of prior systems that forced developers into rigid naming schemes like http_requests_get_200_total.
This shift made querying, aggregation, and alerting radically more flexible, and future-proof.
Finally, Prometheus embraced a single-binary philosophy. It bundled the scraper, TSDB, alerting engine, and HTTP API into one cohesive executable. This eliminated the need for external dependencies and kept operations lean, by matching the team’s original philosophy that a monitoring system should “just work”.
Prometheus didn’t try to be everything. It didn’t aim in principle to cover logs or traces. It didn't aspire to be a long-term storage engine. And that was clearly intentional.
“Prometheus is opinionated, and for good reason. We don’t aim to be all things to all people. We aim to be the right thing for a lot of people.”
— Brazil
This clarity of scope, and the willingness to make hard trade-offs: pull over push, ephemeral storage over durability, simplicity over features, enabled Prometheus to stabilize early and earn deep trust from its users.
What started as an internal experiment at SoundCloud would eventually evolve into one of the most widely adopted observability systems in the world: one that now serves as the foundation for monitoring stacks in companies large and small, and is embedded into the very fabric of Kubernetes itself.
The Prometheus Architecture
Prometheus is built around a deceptively simple core loop: scrape HTTP endpoints on a fixed interval, parse the metrics exposition format, and store the resulting time series in an efficient, purpose-built storage engine.
What seems straightforward at the surface reveals a tightly engineered system, optimized for operational resilience, low latency, and high ingestion throughput.
Let’s break down the key components of this architecture.
Core Components
At the heart of the system is the Prometheus server. This is the central process that orchestrates everything: it discovers scrape targets, pulls metrics at configured intervals, stores time series data locally, applies recording and alerting rules, and serves a rich HTTP API for querying and dashboarding.
Prometheus is a pull-based system, but it can only pull from what exists. That’s where exporters come in. Exporters are processes (or libraries embedded into your applications) that expose metrics at a /metrics HTTP endpoint in Prometheus’s exposition format.
The most popular one is node_exporter, which exposes host-level metrics like CPU, memory, disk, and network. But there are exporters for everything from databases (postgres_exporter, mysqld_exporter) to hardware sensors and message brokers.
For cases where jobs are short-lived, like a batch job that spins up, does some work, and exits, scraping isn’t feasible. These jobs won’t be alive long enough to be scraped.
For this reason, Prometheus provides the Push Gateway, a small service that accepts metrics via HTTP POST and temporarily holds them so Prometheus can scrape the gateway instead.
This should be used sparingly and is not meant for continuous metrics ingestion, but it’s a pragmatic compromise when jobs don’t align with the pull model.
On the alerting front, Prometheus delegates notification logic to the Alertmanager. The Prometheus server continuously evaluates alert rules written in PromQL.
When an alert condition becomes true, it sends alert events to the Alertmanager, which is responsible for deduplication, grouping, inhibition, and notification delivery via integrations like Slack, PagerDuty, email, or webhook endpoints.
One of the key operational strengths of Prometheus lies in its service discovery subsystem. Rather than requiring static configuration of scrape targets, Prometheus supports dynamic discovery through integrations with major platforms like Kubernetes, EC2, Consul, Azure, and others.
It can automatically detect when pods come and go, when VMs are created or terminated, or when services change state, by allowing monitoring to adapt to infrastructure changes in near real-time.
The Pull Model
Most traditional monitoring systems rely on a push model, where agents on each host periodically send data to a central collector. This model puts the burden on the clients to know where to send metrics, manage buffers, retry on failure, and ensure delivery.
Prometheus flips this around. Instead of clients pushing data, the server pulls metrics from each target on a regular interval, which is typically every 15 seconds. Each scrape returns the full state of all exported metrics, which are then processed and stored.
This design has several advantages:
Simplicity for targets: They don’t need to know about Prometheus, or whether it’s up or down. They just expose metrics on an HTTP endpoint.
Flexible scraping: You can scrape different jobs (e.g., app servers, databases, infrastructure) at different intervals, depending on the granularity you need.
Built-in observability: Since Prometheus controls the scrape, it can track when targets are down, how long scrapes take, and how much data is being ingested.
Natural integration with service discovery: Dynamic environments like Kubernetes benefit immensely from this model, where scrape targets are constantly shifting.
Of course, the pull model also comes with trade-offs:
Short-lived processes are hard to monitor because they may not live long enough to be scraped. This is partially mitigated with the Push Gateway, but that introduces statefulness.
Network topology constraints mean that targets must be accessible from the Prometheus server. In highly segmented or firewalled environments, this can require additional engineering (e.g., relabeling, reverse proxies, blackbox exporters).
Despite these drawbacks, the pull-based model has proven to be operationally robust and scalable, especially in modern containerized environments where dynamic infrastructure is the norm rather than the exception.
The Data Model: Labels Are the Core Abstraction
At the heart of Prometheus lies a deceptively simple yet remarkably expressive data model.
Each metric is stored as a time series, uniquely identified by a metric name and an arbitrary set of labels; key-value pairs that add semantic context. A single time series looks like this:
<metric_name>{label1="value1", label2="value2"} => value [@ timestamp]
This isn't a flat list of measurements. It's a multi-dimensional data model, allowing for dynamic, composable metric streams that you can filter, aggregate, or join with surgical precision. The real power lies in the labels.
For example, consider this:
http_requests_total{method="POST", handler="/api", status="500"}
This metric doesn't just count requests.
It gives you a precise breakdown of request volume by HTTP method, endpoint handler, and response code, all encoded via labels. Prometheus doesn’t treat these as just strings.
Its query language, PromQL, is built to operate on these dimensions directly: you can group by handler, filter by status, or compute rates segmented by method.
This approach liberates metrics from rigid hierarchies. Unlike legacy systems like Graphite, which enforce a dotted metric name structure (e.g., web.api.prod.server1.500.count), Prometheus enables ad hoc querying on any combination of dimensions, without re-ingesting or restructuring the data.
But this power comes with a trade-off.
High-Cardinality
Each unique combination of labels defines a distinct time series. This means that if you track a metric with 5 labels and each label has 10 possible values, you’re potentially generating 100,000 unique series.
Add a high-cardinality label, like user_id or request_id, and things can get out of hand fast.
This can lead to:
Increased memory usage, as each series is tracked in-memory by the TSDB.
Longer query times, especially for range queries over heavily fragmented label sets.
Worse compression, as sparse or short-lived series resist efficient chunking.
Operational instability, if explosion goes unbounded.
Prometheus does not prevent this; it trusts the user to apply discipline. It exposes label cardinality through APIs and provides recording rules to flatten or pre-aggregate noisy metrics. But it remains an active point of engineering judgment.
Best Practices Emerge
To balance expressiveness with performance, the Prometheus community has developed some key guidelines:
Avoid embedding high-cardinality data in labels (like
session_id,query_hash,user_id).Pre-aggregate metrics using recording rules before querying them on dashboards.
Use
job,instance, and application-specific dimensions sparingly and deliberately.Monitor series churn and label cardinality using Prometheus’s own metrics.
The label-based model is one of Prometheus’s greatest innovations ever. It turns monitoring from a rigid telemetry system into an observability language, where you can ask complex, evolving questions of your infrastructure with minimal setup.
But like all languages, it rewards those who learn its grammar, punishing those who ignore its structure.
An Opinionated Engine for an Unruly World
Prometheus wasn’t born out of academic abstraction or market opportunity.
It was created out of need. This massive project emerged in the messy, high-velocity environment of SoundCloud’s early microservices era, where traditional monitoring systems failed to keep pace with complexity.
Its creators didn’t set out to build a general-purpose observability platform. They wanted something that would work reliably at scale, require minimal overhead, and give engineers the power to understand their systems without getting lost in them.
That philosophy echoes in every part of the system:
The pull-based model gives Prometheus control over ingestion, resilience against failure, and better observability of itself.
The single-binary deployment model keeps it lean and operable by small teams.
The label-based data model enables rich, multidimensional queries—making Prometheus not just a metrics collector, but a language for interrogating systems.
And the PromQL query language turns time series into answers—fast, expressive, and deeply aligned with how engineers actually think about performance and reliability.
But Prometheus’s strength is also its constraint. It doesn’t pretend to be everything. It doesn't do logs. It doesn't store data forever. It doesn't attempt to correlate across traces, metrics, and events in a unified fabric.
Instead, it does one thing exceptionally well: it tells you, right now, what your system is doing and what might be going wrong.
That clarity of purpose is why it became the default monitoring system for Kubernetes, the foundation for larger observability platforms like Thanos and Cortex, and a critical pillar in the SRE toolkit.
Prometheus teaches us that simplicity isn’t about removing complexity: it’s about structuring it with intention.
In a world where systems are dynamic, distributed, and failure-prone by design, Prometheus gives us a reliable way to measure, reason, and respond. It’s not just a time series database. It’s a lens into the living behavior of software.
And sometimes, that's exactly what we need.