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 / lvm / lvm-reached-low-water-mark-data-device ▌

Operations Guides

LVM reached low water mark for data device: the thin pool warning before the freeze

You found this line in dmesg or the journal:

device-mapper: thin: 253:4: reached low water mark for data device: sending event

This is not an error. It is the dm-thin kernel target reporting that a thin pool’s data device has crossed its low water mark and that a device-mapper event has been sent to userspace. dmeventd listens for that event and can auto-extend the pool. On a correctly configured system, you may see this message once, dmeventd extends the pool, and usage drops back below the threshold.

On a misconfigured system, this message is the last warning before data usage reaches 100%. With the default queue_if_no_space behavior, writes to every thin LV in the pool can then queue in the kernel until space becomes available. Processes pile up in D state, latency climbs, and applications stop making progress. If the pool is configured to error instead, writes fail directly. If the root filesystem or a database journal lives on that pool, the machine progressively freezes.

The message matters because thin pool auto-extend is disabled by default. thin_pool_autoextend_threshold in lvm.conf defaults to 100, which means “never extend.” If the low water mark message repeats and the pool never grows, the automatic handoff is broken and the pool is still filling.

What this means

A thin pool consists of two internal LVs:

  • A data LV that stores allocated blocks.
  • A metadata LV that tracks which blocks belong to which thin volume and snapshot.

Thin LVs are overprovisioned against the data LV. Their virtual sizes do not consume space; only written and retained blocks do. Snapshots increase retention because blocks shared with an origin cannot be freed until every snapshot that references them is removed.

When pool data usage crosses the low water mark, dm-thin emits the kernel message and raises a device-mapper event. If dmeventd is running and monitoring the pool, it receives the event and performs the configured policy extension, effectively lvextend --use-policies. The pool grows by thin_pool_autoextend_percent of its current size if the VG has enough free extents.

The failure cascade looks like this:

flowchart TD
  A["Pool data usage crosses low water mark"] --> B["Kernel: reached low water mark, event sent"]
  B --> C{"dmeventd running, threshold below 100, VG has free space?"}
  C -- yes --> D["Auto-extend succeeds, pool grows, warning clears"]
  C -- no --> E["No extension, usage keeps climbing"]
  E --> F["data_percent reaches 100%, lv_attr shows D"]
  F --> G["Writes queue under queue_if_no_space, D-state processes grow"]
  G --> H["Writes stall or fail, applications hang or crash"]

The message itself is informational. The operational question is whether anything acted on the event. Answer that by correlating the message with the data_percent trend.

Common causes

CauseWhat it looks likeFirst thing to check
Auto-extend disabled by the default thresholdWarning repeats, pool never grows, data_percent climbslvmconfig activation/thin_pool_autoextend_threshold
dmeventd not runningEvent is sent, but no listener acts on itsystemctl is-active lvm2-monitor.service and pgrep -x dmeventd
Pool not monitored by dmeventddmeventd is alive, but this pool has no monitored segmentlvs -o lv_name,vg_name,seg_monitor
VG has no free extentsExtension cannot allocate space, so usage continues risingvgs -o vg_name,vg_free
Runaway writer or bulk importdata_percent jumps quickly after the warningRepeated dmsetup status samples
Thin snapshots accumulatingOld snapshots retain blocks and increase metadata uselvs -o lv_name,origin,data_percent,metadata_percent
No working discard/TRIM pathUsage stays high after large filesystem deletionsRun fstrim on supported thin LV filesystems, then re-check

Quick checks

These checks are read-only and safe during an incident.

# 1. Identify the pool and current usage.
lvs -o lv_name,vg_name,lv_attr,data_percent,metadata_percent

# 2. Read kernel pool state without going through the higher-level LVM tools.
#    This is useful when the system is already slow.
dmsetup status --target thin-pool
# Look for used and total metadata/data fields and the no-space policy.

# 3. Check lv_attr position 9 for pool health.
#    D = out of data space, F = failed, metadata flags indicate metadata trouble.
lvs -o lv_name,vg_name,lv_attr

# 4. Verify auto-extend configuration.
lvmconfig activation/thin_pool_autoextend_threshold activation/thin_pool_autoextend_percent
# A threshold of 100 means auto-extend is disabled.

# 5. Verify dmeventd is alive.
systemctl is-active lvm2-monitor.service
pgrep -x dmeventd

# 6. Check whether dmeventd is monitoring the pool.
#    Use seg_monitor; do not infer monitoring from lv_attr.
lvs -o lv_name,vg_name,lv_attr,seg_monitor

# 7. Check VG headroom for automatic or manual extension.
vgs -o vg_name,vg_size,vg_free

# 8. Look for processes stuck in uninterruptible I/O.
ps -eo pid,stat,wchan:30,comm | awk '$2 ~ /D/'

# 9. Check whether the warning is repeating.
journalctl -k | grep "low water mark" | tail -20

A single data_percent reading does not show burst rate. Take multiple readings and use the delta before deciding whether you have hours or minutes.

How to diagnose it

  1. Measure the trajectory, not just the value. Take three dmsetup status --target thin-pool samples one minute apart and calculate the change in used data blocks. A pool at 88% growing 0.5% per hour is a capacity ticket. A pool at 88% growing 5% per hour is an incident.

  2. Determine whether auto-extend was supposed to fire. A threshold of 100 disables it. A lower threshold still does nothing if dmeventd is stopped or the pool is not monitored. If both are configured correctly, check VG free space. Repeated warnings with no pool growth are the signature of a broken handoff.

  3. Check the pool health flags. If lv_attr position 9 already shows D, the pool is out of data space. Skip further trend analysis and extend the pool immediately.

  4. Check the no-space policy. The dmsetup status output shows whether the pool queues or errors when full. Queueing turns exhaustion into application hangs; erroring turns it into write failures.

  5. Check metadata independently. Data exhaustion stops new allocations. Metadata exhaustion is more dangerous and can make recovery significantly harder. Treat metadata usage above 90% as urgent.

  6. Identify what consumes space. Compare total thin LV virtual size with pool size, list snapshots, and check whether deletions return space. A pool that remains full after large file deletions usually lacks a working discard path. fstrim can reclaim space on supported filesystems, but it can add noticeable I/O load during an incident.

Metrics and signals to monitor

SignalWhy it mattersWarning sign
Thin pool data_percentCountdown to write stalls across every thin LVAbove 85%, or any growth rate projecting exhaustion within 48 hours
Thin pool metadata_percentMetadata exhaustion can require pool repairAbove 75%; urgent above 90%
lv_attr position 9Kernel-reported pool health, including D and failed statesAny health flag other than normal
No-space policyDetermines whether exhaustion causes hangs or immediate I/O errorsPolicy does not match application failure tolerance
VG free spaceDetermines whether automatic or manual extension can succeedLess than two auto-extend cycles of headroom
dmeventd state and pool monitoringThe safety net for the low water mark eventDaemon absent or pool unmonitored
Allocation rate from repeated samplesConverts a percentage into a runway estimateProjected exhaustion within hours
D-state process countShows that queued writes are already stalling applicationsCount growing or a process stuck for more than 120 seconds

Fixes

If the pool has not hit 100% yet

Extend the thin pool from VG free space:

# Grow the pool LV, not an individual thin LV.
# Choose the increment from the measured allocation rate and available VG space.
lvextend -L +50G <vg>/<thinpool>

This is normally an online operation. After the immediate risk clears, fix the automatic extension path so the next event does not require manual intervention.

If the VG is full

Create VG headroom or free space inside the pool:

  1. Add a new PV, extend the VG, and then extend the pool:
# pvcreate writes LVM metadata to the device. Verify that <new_disk> is the
# intended, unused device before running it.
pvcreate /dev/<new_disk>
vgextend <vg> /dev/<new_disk>
lvextend -L +50G <vg>/<thinpool>
  1. Delete unneeded thin snapshots. This frees only blocks no longer referenced by another snapshot or origin. Deletion is destructive to the snapshot.

  2. Run fstrim on supported thin LV filesystems if discarded blocks were not previously returned to the pool. Expect additional storage load while it runs.

  3. Remove a sacrificial thin LV only after the service owner confirms that its data can be destroyed.

If the pool already hit 100%

Extend the pool first. The extension often still succeeds when data space is exhausted, and queued writes resume when space becomes available. If metadata is also exhausted or lv_attr shows F, repair may be required.

# DISRUPTIVE AND DATA-RISKING:
# Stop applications and unmount filesystems on the affected thin LVs first.
# This takes the pool out of service and the repair is not guaranteed.
lvchange -an <vg>/<pool>
lvconvert --repair <vg>/<pool>

lvconvert --repair uses the thin repair tooling and spare metadata. Treat it as best-effort recovery, not as a substitute for restoring from a verified backup.

If XFS shut itself down after I/O errors, restore pool space first, then unmount and remount the filesystem if it can be unmounted cleanly. If errors remain, follow the normal filesystem recovery process before returning it to production. If the root filesystem was on the pool, a reboot may be unavoidable.

Prevention

  • Enable auto-extend. Set thin_pool_autoextend_threshold to 70-80 and thin_pool_autoextend_percent to at least 20 in /etc/lvm/lvm.conf. Verify the effective configuration with lvmconfig, confirm dmeventd is running, and check seg_monitor for the pool.
  • Reserve VG headroom. Auto-extend consumes VG extents every time it fires. Keep enough free VG space for at least two extension cycles.
  • Alert on usage and rate. Create a ticket alert above 85%, an urgent alert above 95%, and an earlier alert when the projected exhaustion window is under 48 hours. Lower those thresholds when dmeventd is absent or the VG has little free space.
  • Monitor metadata separately. Metadata grows with mapping churn and snapshot complexity, not only with data volume. It can become the bottleneck while data usage still looks acceptable.
  • Manage snapshot lifecycles. Assign owners and expiration dates to thin snapshots. A forgotten snapshot can retain data indefinitely.
  • Keep discard working. Ensure the filesystem, thin LV, and pool discard behavior are compatible so deletions can return blocks to the pool.

How Netdata helps

  • Thin pool data_percent and metadata_percent trends show whether the low water mark event was followed by an extension or ignored.
  • Allocation-rate history turns the kernel message into a runway estimate instead of a one-off log entry.
  • Device latency and inflight I/O reveal the slowdown that often appears before writes fail.
  • D-state process counts corroborate that queued thin-pool writes are already stalling applications.
  • VG free-space trends answer the immediate follow-up question: if dmeventd fires, can an extension actually allocate space?
  • Alerting on both absolute usage and projected exhaustion catches slow leaks and runaway writers without waiting for the 100% freeze.