The only agent that thinks for itself

Autonomous Monitoring with self-learning AI built-in, operating independently across your entire stack.

Unlimited Metrics & Logs
Machine learning & MCP
5% CPU, 150MB RAM
3GB disk, >1 year retention
800+ integrations, zero config
Dashboards, alerts out of the box
> Discover Netdata Agents

Centralized metrics streaming and storage

Aggregate metrics from multiple agents into centralized Parent nodes for unified monitoring across your infrastructure.

Stream from unlimited agents
Long-term data retention
High availability clustering
Data replication & backup
Scalable architecture
Enterprise-grade security
> Learn about Parents

Fully managed cloud platform

Access your monitoring data from anywhere with our SaaS platform. No infrastructure to manage, automatic updates, and global availability.

Zero infrastructure management
99.9% uptime SLA
Global data centers
Automatic updates & patches
Enterprise SSO & RBAC
SOC2 & ISO certified
> Explore Netdata Cloud

Deploy Netdata Cloud in your infrastructure

Run the full Netdata Cloud platform on-premises for complete data sovereignty and compliance with your security policies.

Complete data sovereignty
Air-gapped deployment
Custom compliance controls
Private network integration
Dedicated support team
Kubernetes & Docker support
> Learn about Cloud On-Premises

Powerful, intuitive monitoring interface

Modern, responsive UI built for real-time troubleshooting with customizable dashboards and advanced visualization capabilities.

Real-time chart updates
Customizable dashboards
Dark & light themes
Advanced filtering & search
Responsive on all devices
Collaboration features
> Explore Netdata UI

Monitor on the go

Native iOS and Android apps bring full monitoring capabilities to your mobile device with real-time alerts and notifications.

iOS & Android apps
Push notifications
Touch-optimized interface
Offline data access
Biometric authentication
Widget support
> Download apps

The future of infrastructure observability

See our strategic direction across AI-native observability, full-stack signals, operational intelligence, and enterprise platform maturity.

AI-native observability
Full-stack signal coverage
Operational intelligence
Enterprise platform maturity
Agent releases every 6 weeks
Cloud continuous delivery
> Explore Product Roadmap

Best energy efficiency

True real-time per-second

100% automated zero config

Centralized observability

Multi-year retention

High availability built-in

Zero maintenance

Always up-to-date

Enterprise security

Complete data control

Air-gap ready

Compliance certified

Millisecond responsiveness

Infinite zoom & pan

Works on any device

Native performance

Instant alerts

Monitor anywhere

AI-native observability

Continuous delivery

Open source foundation

80% Faster Incident Resolution

AI-powered troubleshooting from detection, to root cause and blast radius identification, to reporting.

True Real-Time and Simple, even at Scale

Linearly and infinitely scalable full-stack observability, that can be deployed even mid-crisis.

90% Cost Reduction, Full Fidelity

Instead of centralizing the data, Netdata distributes the code, eliminating pipelines and complexity.

See and Map Your Entire Network

Live topology, flow analytics, and SNMP device and trap monitoring — unified with your full-stack observability.

Control Without Surrender

SOC 2 Type 2 certified with every metric kept on your infrastructure.

Integrations

800+ collectors and notification channels, auto-discovered and ready out of the box.

800+ data collectors
Auto-discovery & zero config
Cloud, infra, app protocols
Notifications out of the box
> Explore integrations
Real Results
46% Cost Reduction

Reduced monitoring costs by 46% while cutting staff overhead by 67%.

— Leonardo Antunez, Codyas

Zero Pipeline

No data shipping. No central storage costs. Query at the edge.

From Our Users
"Out-of-the-Box"

So many out-of-the-box features! I mostly don't have to develop anything.

— Simon Beginn, LANCOM Systems

No Query Language

Point-and-click troubleshooting. No PromQL, no LogQL, no learning curve.

Enterprise Ready
67% Less Staff, 46% Cost Cut

Enterprise efficiency without enterprise complexity—real ROI from day one.

— Leonardo Antunez, Codyas

SOC 2 Type 2 Certified

Zero data egress. Only metadata reaches the cloud. Your metrics stay on your infrastructure.

Full Coverage
800+ Collectors

Auto-discovered and configured. No manual setup required.

Any Notification Channel

Slack, PagerDuty, Teams, email, webhooks—all built-in.

Built for the People Who Get Paged

Because 3am alerts deserve instant answers, not hour-long hunts.

Every Industry Has Rules. We Master Them.

See how healthcare, finance, and government teams cut monitoring costs 90% while staying audit-ready.

Monitor Any Technology. Configure Nothing.

Install the agent. It already knows your stack.
From Our Users
"A Rare Unicorn"

Netdata gives more than you invest in it. A rare unicorn that obeys the Pareto rule.

— Eduard Porquet Mateu, TMB Barcelona

99% Downtime Reduction

Reduced website downtime by 99% and cloud bill by 30% using Netdata alerts.

— Falkland Islands Government

Real Savings
30% Cloud Cost Reduction

Optimized resource allocation based on Netdata alerts cut cloud spending by 30%.

— Falkland Islands Government

46% Cost Cut

Reduced monitoring staff by 67% while cutting operational costs by 46%.

— Codyas

Real Coverage
"Plugin for Everything"

Netdata has agent capacity or a plugin for everything, including Windows and Kubernetes.

— Eduard Porquet Mateu, TMB Barcelona

"Out-of-the-Box"

So many out-of-the-box features! I mostly don't have to develop anything.

— Simon Beginn, LANCOM Systems

Real Speed
Troubleshooting in 30 Seconds

From 2-3 minutes to 30 seconds—instant visibility into any node issue.

— Matthew Artist, Nodecraft

20% Downtime Reduction

20% less downtime and 40% budget optimization from out-of-the-box monitoring.

— Simon Beginn, LANCOM Systems

Pay per Node. Unlimited Everything Else.

One price per node. Unlimited metrics, logs, users, and retention. No per-GB surprises.

Free tier—forever
No metric limits or caps
Retention you control
Cancel anytime
> See pricing plans

What's Your Monitoring Really Costing You?

Most teams overpay by 40-60%. Let's find out why.

Expose hidden metric charges
Calculate tool consolidation
Customers report 30-67% savings
Results in under 60 seconds
> See what you're really paying

Your Infrastructure Is Unique. Let's Talk.

Because monitoring 10 nodes is different from monitoring 10,000.

On-prem & air-gapped deployment
Volume pricing & agreements
Architecture review for your scale
Compliance & security support
> Start a conversation

Monitoring That Sells Itself

Deploy in minutes. Impress clients in hours. Earn recurring revenue for years.

30-second live demos close deals
Zero config = zero support burden
Competitive margins & deal protection
Response in 48 hours
> Apply to partner

Per-Second Metrics at Homelab Prices

Same engine, same dashboards, same ML. Just priced for tinkerers.

Community: Free forever · 5 nodes · non-commercial
Homelab: $90/yr · unlimited nodes · fair usage
> Get the Homelab Plan

$1,000 Per Referral. Unlimited Referrals.

Your colleagues get 10% off. You get 10% commission. Everyone wins.

10% of subscriptions, up to $1,000 each
Track earnings inside Netdata Cloud
PayPal/Venmo payouts in 3-4 weeks
No caps, no complexity
> Get your referral link
Cost Proof
40% Budget Optimization

"Netdata's significant positive impact" — LANCOM Systems

Calculate Your Savings

Compare vs Datadog, Grafana, Dynatrace

Savings Proof
46% Cost Reduction

"Cut costs by 46%, staff by 67%" — Codyas

30% Cloud Bill Savings

"Reduced cloud bill by 30%" — Falkland Islands Gov

Enterprise Proof
"Better Than Combined Alternatives"

"Better observability with Netdata than combining other tools." — TMB Barcelona

Real Engineers, <24h Response

DPA, SLAs, on-prem, volume pricing

Why Partners Win
Demo Live Infrastructure

One command, 30 seconds, real data—no sandbox needed

Zero Tickets, High Margins

Auto-config + per-node pricing = predictable profit

Homelab Ready
Free Video Course

8-episode Netdata tutorial by LearnLinux.tv

76k+ GitHub Stars

3rd most starred monitoring project

Worth Recommending
Product That Delivers

Customers report 40-67% cost cuts, 99% downtime reduction

Zero Risk to Your Rep

Free tier lets them try before they buy

AI Support Assistant, Available 24/7

Nedi has access to all official documentation, source code, and resources. Ask any question about Netdata—responds in your language.

Deployment & configuration
Troubleshooting & sizing
Alerts & notifications
Evidence-based answers
> Ask Nedi now

Never Fight Fires Alone

Docs, community, and expert help—pick your path to resolution.

Learn.netdata.cloud docs
Discord, Forums, GitHub
Premium support available
> Get answers now

60 Seconds to First Dashboard

One command to install. Zero config. 850+ integrations documented.

Linux, Windows, K8s, Docker
Auto-discovers your stack
> Read our documentation

76,000+ Engineers Strong

615+ contributors. 1.5M daily downloads. One mission: simplify observability.

Per-Second. 90% Cheaper. Data Stays Home.

Side-by-side comparisons: costs, real-time granularity, and data sovereignty for every major tool.

See why teams switch from Datadog, Prometheus, Grafana, and more.

> Browse all comparisons
Edge-Native Observability, Born Open Source
Per-second visibility, ML on every metric, and data that never leaves your infrastructure.
Founded in 2016
615+ contributors worldwide
Remote-first, engineering-driven
Open source first
> Read our story
Promises We Publish—and Prove
12 principles backed by open code, independent validation, and measurable outcomes.
Open source, peer-reviewed
Zero config, instant value
Data sovereignty by design
Aligned pricing, no surprises
> See all 12 principles
Edge-Native, AI-Ready, 100% Open
76k+ stars. Full ML, AI, and automation—GPLv3+, not premium add-ons.
76,000+ GitHub stars
GPLv3+ licensed forever
ML on every metric, included
Zero vendor lock-in
> Explore our open source
Build Real-Time Observability for the World
Remote-first team shipping per-second monitoring with ML on every metric.
Remote-first, fully distributed
Open source (76k+ stars)
Challenging technical problems
Your code on millions of systems
> See open roles
Meet the Team Behind Netdata
Conferences, meetups, and tradeshows where you can see Netdata in action and talk to the engineers who build it.
Live demos and deep dives
Book 1-on-1 meetings
Talks and panel sessions
Event recaps and photos
> See all events
Talk to a Netdata Human in <24 Hours
Sales, partnerships, press, or professional services—real engineers, fast answers.
Discuss your observability needs
Pricing and volume discounts
Partnership opportunities
Media and press inquiries
> Book a conversation
Your Data. Your Rules.
On-prem data, cloud control plane, transparent terms.
Trust & Scale
76,000+ GitHub Stars

One of the most popular open-source monitoring projects

SOC 2 Type 2 Certified

Enterprise-grade security and compliance

Data Sovereignty

Your metrics stay on your infrastructure

Validated
University of Amsterdam

"Most energy-efficient monitoring solution" — ICSOC 2023, peer-reviewed

ADASTEC (Autonomous Driving)

"Doesn't miss alerts—mission-critical trust for safety software"

Community Stats
615+ Contributors

Global community improving monitoring for everyone

1.5M+ Downloads/Day

Trusted by teams worldwide

GPLv3+ Licensed

Free forever, fully open source agent

Why Join?
Remote-First

Work from anywhere, async-friendly culture

Impact at Scale

Your work helps millions of systems

$ guides / ceph / ceph-mon-election-storm

Operations Guides

Ceph monitor election storm: monitors that cannot hold a stable quorum

A Ceph monitor election storm is what happens when the MON cluster cannot complete and hold an election. Each round of Paxos leader election starts, partially completes, then restarts before the new leader can commit any map updates. The election epoch counter climbs, the leader name changes moment to moment, and ceph -s itself starts taking several seconds to return because even reading the current map requires a responsive leader.

Client I/O often keeps working for a while. Existing clients hold cached OSD and CRUSH maps and continue pushing reads and writes to the OSDs they already know about. What is frozen is the control plane: no OSD up/down changes commit, no PG remaps are accepted, no pool changes take effect, no new client maps are handed out. As the storm persists, the gap between the frozen maps and reality widens until clients and OSDs begin to time out.

This page covers how to recognize the storm quickly, what the stable causes are, and how to break the loop without making the quorum loss worse. It assumes a 3-MON or 5-MON deployment on Reef (18.x) or Squid (19.x), though the mechanics are similar back through Pacific.

What this means

Ceph monitors form a Paxos quorum. A strict majority (floor(n/2) + 1 of the MON count) must agree on every cluster map update before it is committed. Elections exist to pick the single leader that runs Paxos rounds. Under normal conditions an election completes in seconds and the cluster never thinks about it again.

An election storm is a sustained failure to keep that leader. The classic signature, drawn from operator reports and the upstream MON code, is the lease timeout loop. The leader grants itself a lease, the peons (non-leader quorum members) must acknowledge that lease before it expires, and when they cannot reach the leader in time they call a new election. The new election hands the lease to another monitor, which also cannot hold it, and the cycle repeats faster than any single round can commit a map update.

Verified for Reef/Squid: mon_lease defaults to 5 seconds, and the lease-ack interval is mon_lease_ack_timeout_factor (default 2), i.e. the leader waits 10 s of lease time before peons call a new election.

flowchart TD
    A[Leader elected] --> B[Leader grants Paxos lease]
    B --> C{Peons ack lease in time?}
    C -- yes --> D[Paxos round commits map update]
    D --> A
    C -- no, timeout --> E[Peon calls new election]
    E --> F[New leader elected]
    F --> G{Leader can keep lease?}
    G -- clock skew / slow store / partition --> E
    G -- yes --> B

The visible symptoms from the CLI:

  • ceph -s takes several seconds (sometimes 10 or more) because the tool is waiting on a MON that keeps cycling.
  • ceph quorum_status shows an election_epoch that ticks upward across repeated samples.
  • The quorum_leader_name field changes between runs.
  • ceph health detail shows MON_CLOCK_SKEW or, in the worst case, never returns a clean health check.

During the storm, OSD map epoch updates stall. OSDs that fail heartbeats do not get marked down. New OSDs that join do not get marked up. PGs that need to re-peer cannot, because peering decisions require committed map updates. If an OSD is also failing at the same time, the cluster cannot respond to it, which compounds the problem.

Common causes

CauseWhat it looks likeFirst thing to check
Clock skew between MON hostsMON_CLOCK_SKEW health check active, leader keeps rotatingceph time-sync-status and chronyc tracking on each MON
MON-to-MON network partitionSubset of MONs cannot reach the leader, elections ping-pong across the partitionping between every MON pair, switch and NIC error counters
One MON on slow storageA single MON cannot keep Paxos pace, drags commit latency, peons time outceph daemon mon.<name> perf dump paxos section on each MON
MON store corruptionOne MON logs RocksDB errors, refuses to join, destabilizes quorumMON log for RocksDB errors, store size outlier
Overloaded co-located MON hostMON host CPU or I/O saturated by co-located OSDs, RGW, or VMstop, iostat, vmstat on the MON host

On recent Ceph releases, clock skew is the first suspect. The upstream troubleshooting guidance is explicit that there is no obvious reason other than clock skew that explains why an electing state would persist.

Quick checks

Run these read-only. None change cluster state.

# Time a basic status call. Several seconds indicates MON trouble.
time ceph -s

# Quorum state, election epoch, leader name. Run it twice, seconds apart,
# and compare election_epoch and quorum_leader_name.
ceph quorum_status -f json | jq '{epoch: .election_epoch, leader: .quorum_leader_name, quorum: .quorum_names}'

# Per-MON state and election_epoch from each daemon's own view.
ceph daemon mon.<name> mon_status

# Clock skew view from the lead monitor.
ceph time-sync-status

# Health detail, filtered for the clock skew check.
ceph health detail | grep -i clock

# Paxos and election counters on each MON.
ceph daemon mon.<name> perf dump | jq '{paxos: .paxos, mon: .mon}'

# MON store size on each MON host.
du -sh /var/lib/ceph/mon/ceph-$(hostname -s)/store.db

# NTP/chrony state on each MON host.
chronyc tracking        # or: ntpq -p

If ceph quorum_status itself hangs, that confirms the MON cluster is not serving requests reliably. Sample election_epoch a few times. In a healthy cluster it barely moves. In a storm it climbs across every sample.

How to diagnose it

  1. Confirm it is actually a storm. Sample ceph quorum_status three times, 5 seconds apart. If election_epoch increases between every sample and quorum_leader_name changes, you have a storm, not a one-off election.

  2. Check clock sync first. Run ceph time-sync-status and chronyc tracking (or ntpq -p) on every MON host. The default mon_clock_drift_allowed is 0.05 seconds. Anything beyond that triggers MON_CLOCK_SKEW and is enough to destabilize elections. VM-hosted MONs are particularly prone to this.

  3. Check MON-to-MON network reachability. From each MON host, ping every other MON host. Look for packet loss, not just latency. A partial partition where two MONs can reach each other but not the third produces exactly the ping-pong leader pattern of an election storm. Check switch error counters and NIC drop counters on the MON hosts.

  4. Find the slow MON. On each MON, run ceph daemon mon.<name> perf dump and look at the paxos.commit_latency and paxos.accept_latency values. One outlier with commit latency in the hundreds of milliseconds (or worse) is the one that cannot keep Paxos pace. In a 3-MON cluster the leader needs only one peon ack to commit, but a slow peon will fail to ack its Paxos lease in time and trigger a new election. One slow MON destabilizes the whole quorum.

  5. Check the MON store on each host. A bloated or corrupt store slows every Paxos round. Compare du -sh /var/lib/ceph/mon/ceph-<name>/store.db across MONs. A store that is an order of magnitude larger than the others, or that shows RocksDB errors in the MON log, is a candidate for removal and rebuild.

  6. Check MON host resource pressure. Run top, iostat -xz 1, and free -m on each MON host. A MON co-located with busy OSDs, RGW, or other VMs can be starved of CPU or disk I/O at the exact moment it needs to respond to a Paxos round.

Metrics and signals to monitor

SignalWhy it mattersWarning sign
ceph_mon_quorum_status (per MON)Tells you who is in quorum right nowAny MON flipping between 1 and 0, or a sustained 0 on any member
MON_CLOCK_SKEW health checkThe single most common storm triggerActive for more than 60 seconds
Paxos commit_latency (per MON, via perf dump)Measures whether the leader can actually commit roundsOne MON with latency 5-10x the others
election_epoch from quorum_statusDirect measure of election churnIncrementing more than once per minute
election_call, election_win, election_lose (per MON)Shows who is calling and losing electionselection_call count climbing rapidly on a non-leader
MON store size (host filesystem)Large stores slow startup and electionsStore over 10 GB, or one MON much larger than others
MON host CPU and I/OA starved MON cannot answer Paxos rounds in timeSustained high iowait or CPU on the MON host
NTP/chrony offset (host metrics)Underlying cause of clock skewOffset trending away from zero between corrections

The Netdata Ceph collector surfaces ceph_mon_quorum_status and ceph_health_detail signals directly. The host-level NTP offset, CPU, and disk I/O on each MON host come from the standard Netdata system collectors. Correlating cluster-side signals with host-side signals tells you whether the storm is a Ceph problem or a host problem.

Fixes

Clock skew

Fix the underlying time sync. Do not raise mon_clock_drift_allowed to make the warning go away. Upstream is explicit that this masks the problem and the cluster will still misbehave, just without telling you.

  1. Verify chronyd (or ntpd) is running on every MON host: systemctl status chronyd.
  2. Verify each MON can reach its time source: chronyc sources.
  3. If MONs are VMs, verify the hypervisor is not stealing time. VM-virtualized clocks are explicitly called out as unsuitable for steady timekeeping on monitor nodes. Prefer bare metal for MON hosts, or at minimum ensure the hypervisor is not oversubscribed.
  4. Once clocks are within 50ms of each other and stable, elections should settle within a minute or two.

MON-to-MON network partition

A partition that splits the MONs into two groups that cannot reach each other produces unstable elections. The fix is network-level: find the bad link, switch port, or NIC and repair it. Do not try to tune Ceph around a broken network.

If the partition is asymmetric and one MON is isolated, you can stop that MON’s daemon to let the remaining MONs form a stable quorum. In a 3-MON cluster this leaves you with 2 MONs and zero fault tolerance, so treat it as a stopgap while you repair the network, not a permanent state.

One MON on slow storage

A MON whose store sits on spinning rust, or whose DB device is shared and saturated, cannot keep up with Paxos rounds. The symptom is one MON with paxos.commit_latency far higher than its peers.

  1. Move the MON store to SSD-backed storage. The path is /var/lib/ceph/mon/ceph-<name>/store.db.
  2. If the store has bloated, trigger a manual compaction with ceph daemon mon.<name> compact. This causes a brief latency spike during compaction, so run it during a maintenance window if possible.
  3. If compaction does not help and the store is corrupt, rebuild the MON from a healthy peer.

MON store corruption

If a MON is logging RocksDB errors or refusing to join quorum, it may have a corrupt store. The safe recovery path is to remove that MON from the quorum, wipe its store, and let it rejoin from a healthy peer.

Current official recovery path (see “Removing Monitors from an Unhealthy Cluster” in the docs): stop the MON daemons, extract the monmap with ceph-mon -i <id> --extract-monmap, edit it with monmaptool --rm, re-inject it with ceph-mon -i <id> --inject-monmap, and let the surviving monitors rebuild quorum; ceph-monstore-tool is no longer the documented mechanism.

As a general pattern: stop the daemon, remove the MON from the MON map if needed, wipe the data directory, and redeploy the MON so it bootstraps from an existing quorum member. This is disruptive to that one MON but does not risk the quorum as long as the other MONs are healthy.

In extreme cases where all MONs are stuck in electing state and no quorum can form, operators have recovered by reducing to a single MON (removing all others from the MON map) so the survivor boots without waiting for quorum, then re-adding the others. This is a last resort with real risk: a single MON has no redundancy. Do it only when the alternative is a permanently frozen cluster, and re-add the second and third MONs immediately.

Overloaded co-located MON host

A MON that shares a host with busy OSDs, RGW instances, or other workloads can be starved at the wrong moment. The clean fix is to give MONs dedicated hosts, or at minimum dedicated CPU and disk resources. If that is not possible in the short term, reduce the load on the co-located services during the storm: throttle recovery, pause scrubs, or move workloads off the MON host.

Prevention

  • Run MONs on dedicated, bare-metal hosts. Co-location with OSDs or RGW is the most common preventable cause of MON starvation. VMs are workable but riskier.
  • Put the MON store on SSD. Spinning rust is not fast enough for reliable Paxos rounds on a busy cluster.
  • Monitor NTP offset on every MON host continuously. Catch drift before it crosses the 50ms threshold.
  • Monitor election_epoch rate. A healthy cluster barely increments it. More than a handful of elections per hour warrants investigation.
  • Monitor MON store size and Paxos commit latency per MON. Outliers are the ones that will eventually destabilize quorum.
  • Keep the MON network simple and redundant. Avoid asymmetric routing or flaky links between MON hosts. The classic election algorithm is known to misbehave under netsplit conditions.

Verified: on Reef and Squid the default election strategy is still classic (the docs recommend staying in it), connectivity remains opt-in for conventional clusters, and the documented stretch-mode procedure requires running monitors in connectivity mode.

On Reef and later, the connectivity election strategy (ceph mon set election_strategy connectivity) is available as an alternative to the classic lowest-rank algorithm and is designed to handle netsplit conditions better. It remains opt-in for conventional clusters, but the documented stretch-mode procedure requires it. If you operate a stretch cluster or one with historically flaky MON networking, evaluate it in a test environment before relying on it in production.

How Netdata helps

  • ceph_mon_quorum_status per MON shows immediately which monitors are in and out of quorum, and whether membership is flapping. A graph that toggles every few seconds is the visual fingerprint of a storm.
  • ceph_health_detail{name="MON_CLOCK_SKEW"} surfaces the most common root cause directly, with per-second resolution so you can see when skew started and whether your NTP fix took hold.
  • Host-level CPU, I/O, and memory collectors on each MON host let you correlate a storm with co-located workload pressure. If commit latency spikes line up with OSD disk utilization spikes on the same host, you have your cause.
  • Anomaly detection on election_epoch rate and per-MON quorum membership flags an emerging storm before operators notice that ceph -s is slow.