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 / varnish / varnish-unauthorized-purge-ban ▌

Operations Guides

Varnish unauthorized PURGE/BAN: cache invalidation as an attack surface

Varnish treats PURGE and BAN as ordinary HTTP methods. It has no built-in VCL that handles, gates, or rejects them. If your custom VCL handles these methods but does not check an ACL first, anyone who can reach your Varnish listener can invalidate cached objects. Unauthenticated Varnish PURGE has been reported as a valid security finding against major platforms through bug bounty programs.

The consequences fall into two categories. Mass invalidation forces a cache stampede: concurrent requests miss simultaneously and hit backends sized for cached traffic. Selective invalidation creates a cache poisoning window: an attacker purges a specific URL, then races to have a malicious response cached before legitimate traffic refills it.

What makes PURGE and BAN an attack surface

Varnish does not ship with default VCL for PURGE or BAN. What happens when either request arrives depends entirely on your custom VCL.

If your VCL does not mention PURGE or BAN, the request follows normal caching logic. On Varnish 7.x, unknown methods are piped to the backend; Varnish 8.0 changed the built-in VCL to return a synthetic 501 and close the connection instead of piping. In neither case does Varnish’s own cache get invalidated.

The exposed state happens when your VCL handles PURGE or BAN but does not check client.ip against an ACL. The invalidation succeeds for anyone who sends the request. This typically occurs when a developer copies a purge snippet from documentation or a blog post, integrates it into VCL, and deploys without adding the ACL gate. The VCL compiles, the purge works in testing because testing comes from localhost, and the missing gate goes unnoticed.

flowchart TD
    A["Attacker sends PURGE/BAN"] --> B{"VCL checks client.ip?"}
    B -- "No ACL" --> C["Objects invalidated"]
    B -- "ACL gates it" --> D["405 or 403 rejected"]
    C --> E["Cache miss spike"]
    E --> F["Backend request spike"]
    F --> G["Backend overload"]
    C --> H["Cache slot emptied"]
    H --> I["Attacker refills"]
    I --> J["Poisoned response cached"]

Why BAN is more dangerous than PURGE

PURGE evicts a single cached object by its exact hash (URL plus Host plus any Vary dimensions). It cannot use wildcards. To purge multiple objects, the caller must issue one PURGE per URL. An attacker using PURGE must enumerate URLs, which limits the blast radius per request.

BAN is fundamentally different. A ban is a regex-based invalidation rule evaluated against cached objects. A single ban expression can invalidate every object in cache with one request.

Even with an ACL, broad BAN expressions carry operational risk. A misconfigured CI/CD pipeline or a buggy application can achieve the same mass invalidation as an intentional attack. Downstream effects (ban list growth and lurker contention) are covered in the related guides on ban list growing and ban lurker not keeping up.

One additional BAN-specific concern: bans that reference req.* variables cannot be processed by the ban lurker background thread. They persist in the ban list until every cached object is checked at lookup time, contributing to O(n) lookup overhead until fully processed. Bans referencing obj.* variables are lurker-friendly and get cleaned up proactively.

The X-Forwarded-For trap

Even when VCL checks an ACL, the most common mistake is checking the wrong IP. The ACL must compare against client.ip, the TCP peer address Varnish sees. It must never compare against X-Forwarded-For, X-Real-IP, or any other client-supplied header. These are trivially spoofed: any HTTP client can set X-Forwarded-For: 127.0.0.1 and bypass an ACL that trusts it.

The harder problem arises when a reverse proxy (HAProxy, NGINX, an AWS ALB) sits in front of Varnish. In that topology, client.ip is the proxy’s IP, not the original client’s. If the proxy’s IP is in your purge ACL, every client behind the proxy appears authorized and the ACL provides no protection.

Three solutions:

PROXY protocol on the listener. Configure Varnish to accept PROXY protocol (-a :6081,PROXY) and configure the upstream proxy to send it. Varnish then sees the original client IP in client.ip. Requires both sides to support PROXY protocol.

Trusted header with std.ip(). If the proxy reliably sets a header like X-Real-IP and strips client-supplied values, match with std.ip(req.http.x-real-ip, "0.0.0.0") ~ purge. This shifts trust to the proxy configuration.

Separate purge-only listener. Bind a second Varnish listener on a port reachable only from authorized networks via firewall rules, and handle PURGE/BAN only on that listener. Network-level isolation is the gate, not VCL logic.

VCL patterns: exposed vs hardened

The exposed pattern, missing the ACL gate entirely:

sub vcl_recv {
    if (req.method == "PURGE") {
        return(purge);
    }
}

The hardened PURGE pattern, following the structure in the official Varnish documentation:

acl purge {
    "localhost";
    "192.168.0.0"/24;
}

sub vcl_recv {
    if (req.method == "PURGE") {
        if (!client.ip ~ purge) {
            return(synth(405, "Not allowed."));
        }
        return(purge);
    }
}

The hardened BAN pattern:

sub vcl_recv {
    if (req.method == "BAN") {
        if (!client.ip ~ purge) {
            return(synth(403, "Not allowed."));
        }
        ban("req.url ~ " + req.url);
        return(synth(200, "Ban added"));
    }
}

Note: the BAN example uses req.url in the ban expression, which makes it lurker-unfriendly (see the req.* vs obj.* distinction above). For production, prefer banning on obj.http.x-url (set it in vcl_backend_response) so the lurker can process bans asynchronously.

The +log ACL flag was introduced in Varnish 7.0, and from that release VCL_acl records are no longer emitted by default. On Varnish 7.0+, ACL match events are not logged by default. To see ACL rejections in varnishlog, declare the ACL with the +log flag:

acl purge +log {
    "localhost";
    "192.168.0.0"/24;
}

Without +log, unauthorized PURGE or BAN attempts that hit the ACL and receive a 405 or 403 are invisible in the shared memory log.

Auditing your VCL for coverage gaps

Check what VCL is currently active:

varnishadm vcl.list

Retrieve the active VCL source and search for PURGE and BAN handling:

# Replace <configname> with the active config from vcl.list
varnishadm vcl.show <configname>

Look for three things:

  1. Does the VCL match req.method == "PURGE" or req.method == "BAN"? If not, invalidation is not handled by VCL at all.
  2. Is there an acl definition referenced by the PURGE/BAN handler? If not, invalidation is unauthenticated.
  3. Does the handler check client.ip ~ <acl_name> before return(purge) or ban()? If not, invalidation is unauthenticated.

Also check the management CLI bind address, which is a separate but related exposure:

# Check management interface bind address (-T flag, requires GNU grep for -P)
ps aux | grep varnishd | grep -oP '\-T\s+\S+'

# Check what is listening
ss -tlnp | grep varnishd

The management CLI (default port 6082, set with -T) has full administrative control: loading VCL, banning cache, changing runtime parameters, stopping the service. If it is bound to 0.0.0.0 or a public IP, the PURGE/BAN ACL is irrelevant because anyone with CLI access can issue ban commands directly, bypassing VCL entirely.

Detection: signals to watch

SignalWhy it mattersWarning sign
MAIN.bans_added rateCounts ban operations injected into the listSpike above 10x baseline
MAIN.n_purges rateCounts purge operations executedSpike above 10x baseline
MAIN.bans (gauge)Current outstanding ban countGrowing without shrinking
MAIN.cache_hit rateInvalidation causes miss spikesSudden drop correlating with bans_added or n_purges spike
MAIN.backend_req rateMisses generate backend fetchesSpike mirroring cache_miss spike
MAIN.bans_lurker_contentionLurker cannot keep up with ban floodSustained nonzero rate
varnishstat -1 -f MAIN.bans_added -f MAIN.n_purges

Find PURGE or BAN requests in the shared memory log:

varnishlog -q 'ReqMethod eq "PURGE"' -g request
varnishlog -q 'ReqMethod eq "BAN"' -g request

# Find ACL rejections (requires +log on the ACL)
varnishlog -q 'VCL_acl ~ "NO_MATCH"' -g request

Ban issuance produces no VSL record — the only ban-related VSL tag is ExpBan, which logs evicted objects — so CLI-issued bans are invisible in the shared memory log. To inspect them, list current bans with timestamps:

varnishadm ban.list

A spike in bans_added or n_purges above 10x baseline can indicate one of three things: an application bug issuing excessive invalidations, a CI/CD pipeline sending purges to the wrong environment, or an active attack. These are indistinguishable from the counter alone. You need the source IP from varnishlog to determine which.

Hardening checklist

  • ACL on every invalidation method. Without a client.ip check before return(purge) or ban(), anyone who reaches Varnish can invalidate cached objects.
  • Gate on client.ip, never headers. X-Forwarded-For and X-Real-IP are client-supplied and trivially spoofed.
  • Resolve the proxy problem. When a proxy sits in front of Varnish, client.ip is the proxy IP. Use PROXY protocol, trusted-header matching, or a separate port.
  • Add +log to ACLs on Varnish 7.0+. Without it, ACL rejections are invisible in the shared memory log.
  • Restrict management CLI access. The management interface (-T, default port 6082) bypasses VCL entirely and allows direct ban commands.
  • Prefer obj.* over req.* bans. Object-level bans are cleaned up by the ban lurker; request-level bans persist and add O(n) lookup overhead on every cache hit.
  • Consider xkey VMOD for invalidation. Surrogate-key-based purging is more efficient than regex bans and limits blast radius per invalidation.
  • Monitor bans_added and n_purges rates. Spikes above 10x baseline indicate an application bug, a misdirected pipeline, or an attack.
  • Patch for VCL bypass vulnerabilities. HTTP/2 request smuggling (VSV00007, CVE-2021-36740) can skip VCL processing entirely, including ACL checks on invalidation methods.

How Netdata helps

Per-second collection of MAIN.bans_added, MAIN.n_purges, and MAIN.bans makes invalidation spikes visible without polling delay. Correlating these counters with MAIN.cache_hit drops and MAIN.backend_req spikes on a single timeline shows the stampede cascade as one connected event: the invalidation, the miss burst, and the backend load. MAIN.bans_lurker_contention monitoring reveals when a ban flood overwhelms the lurker and transitions a security event into a sustained performance problem. ML-based anomaly detection on these counters catches deviations without manual threshold tuning, which matters because baseline invalidation rates vary widely across deployments and a static threshold is difficult to set correctly.