A Simple Guide to Monitoring and Optimizing Prometheus CPU Usage

Prometheus is supposed to help you monitor your stack, not become the thing you need to monitor. But if you’ve ever seen it spike in CPU and slow everything down, you know that’s not always the case.

High Prometheus CPU usage usually shows up when you're scraping too many metrics, using expensive queries, or running with default configs that don’t fit your workload. This guide covers how to track Prometheus CPU usage, what typically causes it, and how to fix it.

What Drives Prometheus CPU Consumption

Prometheus CPU usage can spike for a few common reasons, such as:

Query Complexity and Frequency

Simple queries like rate(http_requests_total[5m]) are light on CPU. But more complex ones—like
histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le, service))—
especially when run across thousands of services, use a lot more CPU.

On top of that, dashboards often refresh frequently. For example, running 20 complex queries every 5 seconds adds up to 240 queries per minute. Prometheus has to work hard to keep up with that load.

Cardinality and Series Growth

Cardinality refers to the number of unique label combinations in your metrics. The more unique combinations you have, the more time series Prometheus has to store and process, which increases CPU usage.

For example, imagine you’re tracking API response times and using labels for:

• Endpoint (100 different API endpoints)
• HTTP method (4 types: GET, POST, etc.)
• Status code (10 possible response codes)
• User ID (10,000 unique users)

Each unique combination of these labels creates a separate time series. So, the total number of series is roughly:

100 endpoints × 4 methods × 10 status codes × 10,000 users = 40 million series

Queries that scan across millions of series require much more CPU to execute, leading to higher resource consumption.

Ingestion Rate and Scrape Targets

The number of targets you scrape and how often directly impact CPU usage. More targets mean more HTTP requests, more data parsing, and more series to update.
For example, cutting your scrape interval from 30 seconds to 15 seconds doubles your ingestion workload.

Also, recording and alerting rules run on schedules and consume CPU each time they evaluate. Complex rules over large datasets can cause noticeable CPU spikes.

How to Monitor Your Prometheus CPU Usage

Before you start trying to reduce Prometheus CPU load, you need to see where the CPU is going in the first place. Monitoring your Prometheus instance well means tracking a few key metrics that reveal how it’s spending its processing time.

What to Keep an Eye On

CPU Time Used: process_cpu_seconds_total
This metric tells you the total CPU time Prometheus has used since it started. To understand how much CPU it’s using right now, you calculate the rate over a recent window—something like rate(process_cpu_seconds_total[5m]). This gives you a real-time sense of CPU usage instead of just a lifetime total.

Memory Footprint: prometheus_tsdb_symbol_table_size_bytes and prometheus_tsdb_head_series
Memory and CPU often go hand in hand. When Prometheus uses more memory, it triggers more garbage collection cycles, which can cause CPU spikes.

The prometheus_tsdb_symbol_table_size_bytes metric tracks the size of Prometheus’s internal symbol table and prometheus_tsdb_head_series tells you how many active time series it’s handling.

Query Performance: prometheus_engine_query_duration_seconds
This histogram shows how long your queries take to run. Long-running queries usually mean Prometheus is doing heavy work, which will show up as higher CPU consumption.

Dashboards That Help You Understand Prometheus Load

It’s worth putting together a dedicated dashboard for Prometheus health so you don’t have to guess what’s going on when CPU usage spikes.

• Start simple: track CPU usage over time to see trends or spikes.
• Then add layers of context: include the number of queries running at once, how fast new data is being ingested, and memory usage.

You don’t need to build dashboards from scratch. Tools like Grafana are great for visualizing your data, and our platform, Last9, offers pre-built dashboards—including a hosted Grafana designed for engineers who love the Grafana UI. These templates combine CPU, memory, query performance, and ingestion metrics, making it easy to spot patterns without digging through raw data.

How to Cut Down Prometheus CPU Usage

Once you understand where Prometheus is using CPU, you can apply focused optimizations to reduce load without losing important monitoring data.

Find and Fix Slow Queries

Use the prometheus_engine_query_duration_seconds metric to identify slow or expensive queries.

• Reduce query time ranges when possible. For example, use rate(metric[5m]) instead of rate(metric[1h]) if a shorter range gives you accurate results. This lowers the amount of data Prometheus processes.
• Use recording rules to precompute complex calculations. Instead of recalculating expensive queries each time a dashboard refreshes, dashboards can query the precomputed results.

Spot High-Cardinality Issues

High cardinality metrics often cause high CPU usage.

• Review your metrics for labels with many unique values, such as user IDs or request IDs, which can create a large number of series.
• When appropriate, replace high-cardinality labels with histograms or summaries to track distributions without creating individual series for each label.
• Drop unnecessary labels with relabeling. For example, if you only need production data, filter out other environments before ingestion.

Set the Right Scrape Intervals

Adjust scrape settings to balance load and data freshness.

• Not every metric needs to be scraped every 15 seconds. Some can be scraped every 30 or 60 seconds without losing critical information.
• Use metric_relabel_configs to filter out metrics you don’t need before they’re stored, saving CPU and disk space.
• Configure scrape timeouts carefully so slow targets don’t block scrapes, but valid data isn’t lost due to too-short timeouts.

Where Prometheus CPU Gets Stuck — And How to Fix It

Some common patterns consistently cause high CPU usage in Prometheus. Let's understand them:

Dashboard Refresh Overload

Dashboards that refresh every few seconds put constant query pressure on Prometheus. This can quickly overload your CPU and slow down monitoring for everyone.

• For most operational dashboards, set refresh intervals to about 30 seconds.
• For detailed or analytical dashboards, 5 minutes is usually enough.
• Instead of auto-refreshing every few seconds, let users refresh manually when they need real-time data.
• If you need live updates, consider streaming or push-based systems rather than polling Prometheus frequently.

Too Many Recording Rules

Recording rules precompute query results and can reduce load—if used wisely. But creating too many recording rules backfires because Prometheus must evaluate all of them regularly, adding CPU overhead.

• Focus recording rules on queries that are both slow and executed frequently.
• Avoid recording rules for queries that run rarely or save very little CPU time.
• Regularly review your recording rules and remove or combine those that aren’t providing value.

Alert Rules That Waste CPU

Alert rules that scan broad datasets without filters waste CPU and slow down Prometheus.

• Use label selectors to narrow alert scope.
• Replace broad conditions like up == 0 with more targeted ones like up{job="critical-service"} == 0.
• Review alert rules periodically to ensure they only check what’s necessary.

When and How to Scale Prometheus for Better Performance

Sometimes tweaking queries and configs isn’t enough, and you need to scale your Prometheus setup to keep up with the load. Scaling Prometheus can be a bit different from traditional databases, so let’s break down the main approaches.

Horizontal Scaling:

Prometheus doesn’t scale horizontally out of the box like some other systems. But you can still distribute the load by running multiple Prometheus instances, each handling a slice of your monitoring data.

• Functional sharding:

This means assigning different Prometheus instances to different parts of your system. For example, one instance monitors your frontend services, another handles backend APIs, and yet another watches your database cluster. Or you might split by region if you run services in multiple data centers. This way, each instance deals with less data and fewer queries, reducing CPU strain.

• Federation:

Consider federation as a way to stitch these separate Prometheus instances together. Lower-level instances hold detailed metrics and respond quickly, while a higher-level “federated” Prometheus pulls summarized data from those instances for a big-picture view. This setup balances detailed monitoring and scalability.

Remote storage:

Storing all your data locally can get expensive and slow as your metrics grow. Using remote storage solutions lets you keep recent data on your local Prometheus for fast querying while pushing older data to long-term storage systems. This reduces the CPU and disk load on your main Prometheus servers.

Vertical Scaling

Before you add more Prometheus instances, make sure you’re squeezing all you can out of your current setup.

• CPU cores:

Prometheus benefits from more CPU cores because it can process queries in parallel. Adding cores usually leads to better query performance and smoother handling of concurrent requests.

• Memory:

Having enough RAM reduces disk I/O and lessens the pressure on Go’s garbage collector, which in turn helps keep CPU usage down. As your number of time series grows, you’ll want to increase memory to avoid bottlenecks.

• Storage:

Disk speed affects how fast Prometheus can read and write data. Using SSDs instead of spinning disks makes queries faster and cuts down on CPU time spent waiting on slow storage. For long-term data, moving older metrics to remote storage frees up local resources for active data.

Advanced CPU Usage Monitoring Techniques

Beyond basic metrics, these methods give you clearer insights into Prometheus CPU usage and help with planning.

Custom Metrics for CPU Analysis

Start by creating or collecting metrics that break down CPU usage more granularly:

• Track how long different types of queries take. For example, group query durations by query name or purpose to find the most expensive ones.
• Measure ingestion rates by job or target, so you know which data sources generate the most load.
• Monitor rule evaluation times per rule group to spot expensive recording or alerting rules.

To get this data, you can use existing Prometheus metrics like prometheus_engine_query_duration_seconds and add labels or use your monitoring platform’s features to group and filter.

Also, compare CPU usage patterns with business metrics like request volume or user traffic. For example, plot CPU usage alongside web traffic to see if spikes align.

Automated Optimization Triggers

You can automate responses to high CPU usage to reduce manual work:

• Start simple: schedule Prometheus restarts during low-traffic times to clear memory pressure and reset CPU load.
• Build automation that changes scrape intervals dynamically. For instance, when CPU usage crosses a threshold, increase scrape intervals to lower load temporarily.
• Temporarily disable or throttle non-critical recording rules during high CPU periods.

These automations can be implemented using orchestration tools, Kubernetes operators, or custom scripts triggered by alerts.

Trend Monitoring and Capacity Planning

Keep an eye on CPU usage trends over days and weeks, not just spikes. If CPU usage steadily grows, it’s a sign you’ll need to optimize queries, add resources, or scale your setup.

Set up dashboards to visualize CPU over time and alerts that warn when CPU usage stays high for extended periods. This way, you can plan scaling or tuning before performance degrades.

Wrapping Up

Monitoring and optimizing Prometheus CPU usage is an ongoing process that evolves as your systems grow. High cardinality—that is, metrics with many unique label combinations—can cause CPU spikes because Prometheus needs to process and store a lot more time series.

Our platform, Last9, is designed to handle high cardinality and brings metrics, logs, and traces together in one place. This makes it easier for engineering teams to spot inefficient CPU use, reduce costs, and get real-time insights—all without switching between multiple tools.

FAQs

Q: What’s considered high CPU usage for Prometheus?
A: Sustained CPU usage above 80% usually signals performance issues. Short spikes up to 90-100% during heavy queries are normal, but if high CPU stays consistent, it’s time to optimize or scale.

Q: How do I find which queries use the most CPU?
A: Turn on query logging with --query.log-file and use the /debug/pprof/profile endpoint during high CPU periods. Query logs show execution times, while CPU profiles highlight internal bottlenecks.

Q: Should I always use recording rules to lower CPU usage?
A: Not always. Recording rules help with expensive, frequently run queries but add overhead because Prometheus reevaluates them regularly. Use them only when the benefit outweighs the cost.

Q: How does increasing scrape frequency affect CPU?
A: CPU usage generally increases roughly in proportion to scrape frequency. For example, cutting scrape intervals from 30s to 15s roughly doubles CPU overhead from ingestion. Balance your needs for data freshness with resource limits.

Q: Can high cardinality metrics crash Prometheus?
A: Yes. Extremely high cardinality can exhaust memory and starve CPU, causing Prometheus to slow down or crash. It’s important to monitor series counts and manage cardinality proactively.

Q: How are memory and CPU usage related in Prometheus?
A: High memory use can increase CPU load due to garbage collection and slower query processing. Usually, you need to scale memory and CPU together to maintain good performance.