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 / php-fpm / php-fpm-nginx-timeout-mismatch ▌

Operations Guides

PHP-FPM and nginx timeout mismatch: phantom workers and confusing 504s

Users see 504 Gateway Timeout. You check PHP-FPM and the pool is not saturated, or it is saturated but the active workers are running requests that should have finished minutes ago. The nginx error log shows upstream timeouts. The FPM error log shows nothing unusual.

nginx and PHP-FPM each have their own notion of how long a request may run. When those notions disagree, you get two failure modes: phantom workers (nginx gave up, FPM kept going) and confusing 502/504 patterns (FPM killed a worker nginx was still waiting on). The configuration is incoherent across the request path.

A related mismatch: nginx worker_connections is typically far higher than FPM pm.max_children. nginx accepts traffic bursts that FPM cannot serve, so nginx’s connection acceptance rate has no relationship to FPM’s actual processing capacity.

What this means

PHP-FPM workers handle exactly one request at a time. When nginx forwards a request to FPM via FastCGI, two independent clocks start:

  • nginx starts the fastcgi_read_timeout clock (default 60s). This is the time between successive read operations from the FastCGI upstream, not the total response time.
  • FPM starts the request_terminate_timeout clock (default 0, meaning disabled). When set, the worker is killed after this wall-clock duration.

If these clocks disagree, one side gives up before the other. The side that gives up first determines what the user sees.

Phantom workers: nginx times out first (fastcgi_read_timeout < request_terminate_timeout, or request_terminate_timeout is 0). nginx returns 504 and closes its side of the FastCGI connection. The FPM worker does not know the client is gone. It keeps running the request, holding a worker slot for a response nobody will read. If request_terminate_timeout is 0 (the default), that worker runs until the script finishes naturally, which could be never (infinite loop, hung database call, deadlocked external API).

Confusing 502s: FPM times out first (request_terminate_timeout < fastcgi_read_timeout). FPM kills the worker. nginx is still waiting on the FastCGI socket and gets an unexpected connection close. nginx logs “upstream prematurely closed connection” and returns 502. The user sees a 502, not a 504, even though the root cause is a timeout.

A third clock exists but is less useful as a safety net: max_execution_time (PHP ini, default 30s). On Linux, this timer uses ITIMER_PROF, which counts CPU time, not wall-clock time. A script blocked on I/O (database, HTTP, sleep) does not accumulate CPU time and will not trigger max_execution_time. This is why request_terminate_timeout (wall-clock) is the more reliable backstop for stuck workers.

flowchart TD
    A["Request arrives at nginx"] --> B["nginx forwards to FPM worker"]
    B --> C{"Which timeout fires first?"}
    C -->|"nginx fastcgi_read_timeout"| D["nginx returns 504 to client"]
    D --> E["FPM worker still running"]
    E --> F["Phantom worker holds slot"]
    C -->|"FPM request_terminate_timeout"| G["FPM kills worker"]
    G --> H["nginx still waiting on socket"]
    H --> I["nginx logs upstream closed, returns 502"]

Common causes

CauseWhat it looks likeFirst thing to check
request_terminate_timeout = 0 (default)Workers stuck in Running state for minutes; nginx returns 504 but FPM shows active workers with no errorsgrep request_terminate_timeout in pool config
fastcgi_read_timeout left at default 60snginx returns 504 at 60s even though FPM would finish at 65snginx -T 2>/dev/null | grep fastcgi_read_timeout
nginx timeout much higher than FPM timeoutnginx logs “upstream prematurely closed”; users see 502 not 504Compare both timeout values directly
worker_connections » max_children with no rate limitingnginx accepts burst, FPM listen queue fills, 502s appear under loadCompare worker_connections against pm.max_children
Streaming responses with long pausesnginx fires fastcgi_read_timeout between chunks even though total time is under limitCheck if application sends output in chunks with gaps

Quick checks

# Check FPM request_terminate_timeout for each pool
grep -r "request_terminate_timeout" /etc/php/*/fpm/pool.d/

# Check nginx fastcgi timeouts
nginx -T 2>/dev/null | grep "fastcgi_.*timeout"

# Compare worker_connections vs max_children
nginx -T 2>/dev/null | grep worker_connections
grep -r "pm.max_children" /etc/php/*/fpm/pool.d/

# Look for phantom workers: Running workers with very long durations.
# NOTE: the status path (here /fpm-status) must match pm.status_path in your FPM pool config.
curl -s "http://127.0.0.1/fpm-status?json&full" | python3 -c "
import sys, json
data = json.load(sys.stdin)
for p in data['processes']:
    if p['state'] == 'Running' and p['request duration'] > 30000000:
        print(f\"PID {p['pid']}: {p['request duration']/1e6:.1f}s - {p['request uri']}\")"

# jq equivalent (if python3 is unavailable):
# curl -s "http://127.0.0.1/fpm-status?json&full" | jq -r \
#   '.processes[] | select(.state=="Running" and .["request duration"]>30000000) \
#   | "PID \(.pid): \(.["request duration"]/1e6|round)s - \(.["request uri"])"'

# Check nginx for upstream timeout and premature close errors
grep "upstream timed out\|upstream prematurely closed" /var/log/nginx/error.log | tail -20

# Check FPM listen queue (are bursts already queuing?)
curl -s http://127.0.0.1/fpm-status | grep "listen queue"

How to diagnose it

  1. Pull both timeout values. Get request_terminate_timeout from every FPM pool config and fastcgi_read_timeout from nginx config. If either is at its default (0 for FPM, 60s for nginx), that is likely the mismatch.

  2. Determine which side gives up first. If request_terminate_timeout is 0 or greater than fastcgi_read_timeout, nginx gives up first and you will see phantom workers. If request_terminate_timeout is set and less than fastcgi_read_timeout, FPM gives up first and you will see 502s with “upstream prematurely closed” in nginx logs.

  3. Check for phantom workers. Pull the full FPM status page and look for workers in Running state with request duration exceeding your nginx timeout. These are workers processing responses that nginx has already abandoned. The request duration field is in microseconds: 60,000,000 is 60 seconds.

  4. Correlate nginx 502/504 timestamps with FPM worker state. If nginx logs a 504 at time T, check whether FPM still shows that request running at time T+5s. If yes, it is a phantom worker. If nginx logs a 502 with “upstream prematurely closed” at time T, check whether FPM logged a worker kill around the same time.

  5. Check the listen queue and max_children. If worker_connections is 1024 and max_children is 20, nginx can accept 50x more concurrent connections than FPM can process. Under burst, the listen queue fills and connections are dropped at the kernel level. Check ss -xlnp | grep php (Unix socket) or ss -tlnp | grep 9000 (TCP) for Recv-Q approaching the backlog limit.

  6. Verify the streaming edge case. nginx’s fastcgi_read_timeout is between successive read operations, not total response time. A script that sends a byte every 59 seconds can run for hours without triggering a 60s fastcgi_read_timeout, but request_terminate_timeout would kill it at its configured limit. If your application streams output with pauses, this asymmetry matters.

Metrics and signals to monitor

SignalWhy it mattersWarning sign
FPM active processes with long request durationIdentifies phantom workers still running after nginx gave upWorkers in Running state with duration > fastcgi_read_timeout
nginx 504 ratenginx gave up waiting for FPMSustained non-zero rate during normal traffic
nginx 502 rate with “upstream prematurely closed”FPM killed a worker nginx was still readingCorrelates with request_terminate_timeout firing
FPM listen queue depthBursts are queuing because max_children is too low relative to nginx acceptanceSustained non-zero value
FPM max children reached counterPool hit its ceiling under burstCounter incrementing during normal traffic
Kernel ListenOverflows / ListenDropsConnections dropped at kernel level when backlog is fullCounters increasing in /proc/net/netstat

Fixes

Make the timeout chain coherent

The goal is for both sides to agree on when a request should end. There are two coherent strategies:

Strategy A: nginx gives up first, FPM cleans up. Set fastcgi_read_timeout slightly less than request_terminate_timeout. For example, fastcgi_read_timeout 55s and request_terminate_timeout = 60s. nginx returns a clean 504 at 55s. FPM kills the worker at 60s, closing the phantom worker window to 5 seconds. This is the common operator preference because users get a predictable 504 and phantom workers are short-lived.

Strategy B: FPM gives up first, nginx reports it. Set request_terminate_timeout less than fastcgi_read_timeout. For example, request_terminate_timeout = 55s and fastcgi_read_timeout 60s. FPM kills the worker at 55s. nginx sees the connection close and returns 502. Users see a 502, not a 504. This is less common because 502 is a worse user experience, but it ensures FPM is the authority on request lifetime.

Either strategy requires request_terminate_timeout to be set (not 0). With the default of 0, there is no FPM-side cleanup at all.

Set request_terminate_timeout_track_finished on PHP 7.3+

request_terminate_timeout_track_finished (default “no”) controls whether the timeout applies after fastcgi_finish_request() or during shutdown functions. If your application calls fastcgi_finish_request() to send a response early and then does background work, the default “no” means the timeout does not cover that background work. Set it to “yes” if you want the timeout to cover the full worker lifecycle.

Address the worker_connections mismatch

nginx worker_connections is per worker. Effective max connections is worker_connections * worker_processes, which is typically 2048-8192 in production. FPM max_children is typically 20-50. Under burst, nginx accepts connections that FPM cannot serve, filling the listen queue and eventually triggering kernel-level drops.

Options:

  • Add rate limiting at the nginx layer (limit_req) to cap the request rate to what FPM can handle.
  • Increase pm.max_children if memory allows. A rough budget: avg_worker_RSS * new_max_children + OS_overhead should be less than ~70% of total RAM.
  • Use pm = static to eliminate dynamic scaling lag under bursts.

Kill existing phantom workers

If phantom workers have accumulated and are holding slots, you can target them individually:

# Identify phantom workers (Running with duration > fastcgi_read_timeout)
curl -s "http://127.0.0.1/fpm-status?json&full" | python3 -c "
import sys, json
data = json.load(sys.stdin)
for p in data['processes']:
    if p['state'] == 'Running' and p['request duration'] > 60000000:
        print(p['pid'])"

SIGQUIT is the worker-level graceful signal: FPM installs a worker handler that closes the accepted FastCGI socket and asks PHP to stop accepting new work. If a busy worker exits this way, the master normally forks a replacement. Prefer request_terminate_timeout for automated cleanup; direct signals are incident-only tools.

kill -SIGQUIT <pid>

Do not use kill -9 or kill -SIGKILL unless the worker is completely unresponsive. Restarting the entire FPM pool terminates all workers including phantoms, but causes a brief no-worker window during the reload. Use systemctl reload <php-fpm-service> (service name varies by distribution and PHP version: php8.1-fpm, php-fpm, etc.) or send SIGUSR2 to the FPM master process (kill -SIGUSR2 <master-pid>).

Prevention

  • Set request_terminate_timeout on every pool. The default of 0 means a single stuck request permanently removes a worker. A value of 30-60 seconds (application-dependent) is the safety net.
  • Document the timeout chain. Record the relationship between fastcgi_read_timeout, request_terminate_timeout, max_execution_time, and any upstream API timeouts. Outer layers should have longer timeouts than inner layers, and the gap between fastcgi_read_timeout and request_terminate_timeout should be small enough to avoid phantom workers.
  • Alert on phantom workers. Alert on workers in Running state with durations exceeding fastcgi_read_timeout. That means the timeout chain is broken.
  • Review after every config change. Timeout mismatches are often introduced when one team changes nginx config and another changes FPM config without coordination.
  • Enable the slow log. Set request_slowlog_timeout (e.g., 5s). The slow log captures stack traces that show where workers are blocked, which is essential for distinguishing “stuck because of a timeout mismatch” from “stuck because of a slow database query.”

How Netdata helps

  • Per-second FPM status polling catches phantom workers as they form, including transient ones in the narrow window between nginx timeout and FPM cleanup. Coarse polling intervals (10s or higher) can miss short-lived phantoms, though persistent phantoms (from request_terminate_timeout = 0) are visible at any interval.
  • Active processes with request duration lets you correlate worker duration against your configured fastcgi_read_timeout. Workers exceeding that threshold are phantoms.
  • nginx 502 and 504 rate metrics alongside FPM active/idle process counts let you see whether nginx is giving up before or after FPM.
  • Listen queue depth from the FPM status page, correlated with nginx connection rates, shows whether the worker_connections » max_children gap is causing kernel-level drops under burst.
  • ML anomaly detection on active process count and request duration surfaces the slow drift toward phantom worker accumulation before it triggers max_children exhaustion.