I'll look into alternative UI options for Netdata that do not require login, as well as alternative system monitoring tools that are open source, web-based, lightweight, and capable of storing and analyzing data later. I will get back to you with the best options.

Lightweight Open-Source System Monitoring Tools

Keeping systems healthy doesn’t have to mean heavy software. Below are several open-source, web-based monitoring tools that are lightweight yet robust, with historical data retention. We compare their features, data storage, ease of use, and community support. Setup tips and docs links are included where possible for quick deployment.

Netdata

Netdata is a popular real-time monitoring agent with per-second granularity and hundreds of preconfigured metrics. It runs on virtually any system (physical, VM, container, IoT) and auto-detects system stats, containers (Docker/K8s), and many applications without manual configurationgithub.com github.com. Interactive dashboards allow you to drill into CPU, memory, disk, network, processes, and more with zero setup.

Features: Provides “every metric, every second” with interactive charts for 3000+ metrics out-of-the-boxgithub.com. It includes health alarms (with default thresholds), and even ML-based anomaly detection for metrics correlationsgithub.com. Netdata can also act as a StatsD server and export metrics to external time-series databasesstackshare.io.
Data Storage: By default, Netdata stores recent metrics in memory (for high-resolution data). For longer retention it uses an efficient tiered database engine on disk, needing ~0.5 byte per metric sample with automatic downsampling for older datagithub.com. This means you can keep historical data for capacity planning or root-cause analysis without a huge footprint. Netdata’s built-in DB supports years of retention in a compressed formatgithub.com.
Resource Use: Written in C, Netdata is optimized to run with minimal overhead. The developers note it is “a lot faster, requires significantly fewer resources and puts almost no stress on the server it runs”github.com. In practice, Netdata typically consumes a few percent of CPU and a small amount of RAM even while collecting per-second metrics on a busy system.
Ease of Use: Netdata is famed for its easy installation and zero-configuration setup. A single command installer (`bash 90% for 5m triggers an email). Its data model is highly dimensional – every metric can have labels (like host, mountpoint, etc.), enabling rich filtering and aggregationprometheus.io. Grafana complements this by providing a beautiful web UI for creating interactive dashboards, charts, and panels. Grafana supports not only Prometheus but many data sources, so you can unify metrics from different systems.
Data Storage: Prometheus stores data locally on the Prometheus server’s disk in an efficient custom format (a time-series oriented database)prometheus.io. It’s designed for speed and uses compression; however, by default Prometheus is not long-term storage — you configure a retention period (e.g., 15 days). For longer history, you can use remote storage integrations or continuous exports. Scaling horizontally is done via federation or sharding (each Prom instance is standalone)prometheus.io. Grafana itself doesn’t store the metrics (it queries Prometheus or other backends on the fly), but Grafana does store its dashboard definitions and settings (usually in a small SQLite or other DB). In summary, Prometheus gives you full control of how much data to keep, and you can fine-tune retention vs. resource use. All data is on your servers – no external cloud needed.
Ease of Use: Setting up Prometheus + Grafana requires more steps than one-stop tools. You’ll need to deploy the Prometheus server (a single Go binary) and install exporters on each machine. The Node Exporter is also a simple binary that you run (or set up as a service) to expose machine metrics. Then you configure Prometheus (via a YAML file) with the targets (the addresses of your exporters). Finally, install Grafana for the web UI. While this is a manual process, it’s well-documented and many guides existbetterstack.com medium.com. Grafana comes with pre-built dashboard templates – for example, there are community dashboards for Node Exporter metrics that you can import to get a comprehensive system overview instantlywww.reddit.com. Once running, this stack is quite user-friendly: Grafana’s UI allows clicking and editing graphs, setting alerts, and managing users/permissions for team access. The learning curve is in the initial setup and learning PromQL for advanced queries. For those willing to invest a bit of time, the payoff is a very powerful, flexible monitoring system that can grow with your needs.
Resource Use: A basic Prometheus+Grafana deployment can be lightweight if tuned for a small environment. Prometheus is written in Go and is efficient; on a single server scraping a few hosts at moderate intervals, it won’t use a lot of CPU. It will consume memory roughly proportional to the number of time-series (metrics * hosts * retention period). Grafana’s resource usage is modest (it’s also Go-based); typically a few hundred MB of RAM when running. However, as you add many dashboards or run lots of heavy queries, Grafana can use more memory and CPU. Compared to single-agent tools, Prom+Grafana is heavier mainly due to running two services and storing more historical data. It’s still quite feasible on modern hardware or even a Raspberry Pi for a handful of nodes (community members have done so).
Community Support: Both Prometheus and Grafana are extremely popular in the industry. Prometheus is a graduated project of the Cloud Native Computing Foundation and has a vast community of users and contributorsprometheus.io. Grafana is the go-to dashboard tool for many and has an extensive plugin and dashboard ecosystem. You’ll find thousands of community dashboards, tutorials, and forums for help. Updates and improvements are frequent. If you run into issues, there is a wealth of Q&A on sites like Stack Overflow, and official documentation is comprehensive. In short, community support is excellent – one of the reasons to choose this stack for a long-term solution. Setup Documentation: To get started, see the official Prometheus Getting Started guideprometheus.ioand Grafana’s documentation on Installing Grafana. In brief: install Prometheus, install Node Exporter on each node, update Prometheus config to scrape those nodes, and run Grafana to visualize. Grafana’s website also provides a Node Exporter Quickstart with preconfigured dashboards and alerts for common Linux metricsgrafana.com, which can jump-start your deployment.

Munin

Munin is a classic monitoring tool (around since 2002) known for its simplicity and low footprint. It follows a plug-in architecture and focuses on gathering historical trends rather than real-time interactivity. Munin is ideal if you want a “set it and forget it” system that collects data every few minutes and generates graphs to view via a web page.

Features: Munin is described as “a networked resource monitoring tool that can help analyze resource trends and ‘what just happened to kill our performance?’ problems”munin-monitoring.org. It comes with 500+ plugins to monitor everything from basic system resources (CPU, memory, disk, network) to services like Apache, MySQL, sensors, etc.munin-monitoring.org. A default install will autodetect and enable many plugins, yielding a lot of graphs with almost no manual workmunin-monitoring.org. Munin’s emphasis is on plug-and-play – if a plugin finds relevant data on your system, it will start collecting it. It also supports custom plugins (you can write scripts in any language) to extend its metricsmunin-monitoring.org. Munin has a basic alerting mechanism (you can configure threshold alarms that trigger commands or emails), though it’s not as elaborate as others.
Data Storage & UI: Munin uses RRDTool under the hood to store metrics in round-robin databasesmunin-monitoring.org. Data is typically collected every 5 minutes (by default)munin-monitoring.organd appended to RRD files. These files store historical data with consolidation (high-detail recent data, and progressively coarser data for older timestamps). Munin then generates static HTML pages with graphs (usually PNG images) to visualize the data. The web interface is simple: basically a set of pages per host and per metric category with timestamped graphs (hourly, daily, weekly, yearly views). It’s not interactive (no zooming or dynamic queries), but it’s effective for spotting trends and anomalies over time. Because the data is on disk, Munin can help with capacity planning (e.g., you can see growth of disk usage over months)wiki.gentoo.org. All data stays on your server; there’s no external dependency.
Ease of Use: Munin is extremely easy to install and maintain. Typically, you install the munin master (which includes the web UI generation and RRD storage) on one server, and munin-node (agent) on each server you want to monitor. Configuration is minimal – on the master, you list the nodes to poll; on each node, the plugins you want can be enabled via symlinks (many are enabled by default). The master connects to each node every 5 minutes to gather data. As the official site says, “install & configure Munin in less than 10 minutes”munin-monitoring.org. It’s largely fire-and-forget – once set up, it keeps collecting and updating graphs. The only maintenance is adding new plugins or nodes as needed. Munin’s output can be served by any web server (it just creates static files), so viewing the results is as easy as opening the local webpage.
Resource Use: Munin is very lightweight. The node agents are simple scripts (mostly Perl or shell) that execute quickly to gather data, then go idle. The master does a bit of work every 5 minutes to update RRD files and draw graphs, but this is usually negligible on modern systems. Users often note that a properly configured Munin setup has minimal impact on system performanceserverfault.com. It’s suitable even on low-end hardware or many small VPS instances. The trade-off is that it’s not real-time (5-minute granularity by default) and the interface is not fancy – but for many, that’s acceptable to get a low-resource solution.
Community Support: Munin has been around a long time and is actively maintained (first release in 2002 and still evolvingmunin-monitoring.org). Its community is smaller than newer tools, but it has a dedicated user base. Documentation is available on the official site and wiki, and there are plenty of user-contributed plugins in the repository. Being older, you might not find as many recent blog posts or hype, but on the flip side the software is mature and stable. If issues arise, you can often troubleshoot by looking at plugin logs or the straightforward code. For basic server metrics, Munin “just works” – many sysadmins still use it for its reliability and simplicity. Setup: Refer to the Munin Documentation for installation steps. On Debian/Ubuntu, for example, installing Munin is as easy as apt-get install munin munin-node. Enable the desired plugins in /etc/munin/plugins/, ensure the master can reach the nodes (usually via SSH or open port 4949 for munin-node), and that’s it. The Munin master will start populating /var/www/html/munin (or similar) with monitoring pages. You can view a demo of Munin on their site to see the style of outputmunin-monitoring.org– it’s spartan but very clear. This tool is great when you need something ultra-lightweight to track historical usage without complex setup.

Glances

Glances is another lightweight monitoring option, which can run in a terminal or in web mode. It’s like an enhanced curses-based dashboard (think of an interactive top) that can also export data for historical analysis. Glances is written in Python and is cross-platform, making it quite versatile.

Features: Glances provides an “at a glance” view of your system. It monitors CPU, memory, swap, disk I/O, filesystem usage, network bandwidth, processes, and more in a single consolidated viewgithub.com. It can also show temperatures, fan speeds, and other sensor data if availablegithub.com. Glances has support for container monitoring (Docker, LXC) and even can display information about running VMs or certain applications via pluginsgithub.com. The interface (in web or terminal) is color-coded and can highlight values that are reaching defined thresholds (for example, CPU usage turning orange/red if high). While Glances primarily focuses on real-time display, it’s extendable – you can develop plugins or outputs to suit your needs.
Data Storage: By default, Glances itself does not retain long-term history – it’s meant for real-time observation. However, it can export metrics to various destinations for historical storagegithub.com. For example, Glances supports exporting data to CSV files, InfluxDB, OpenTSDB, Redis, or even to a message bus (MQTT, RabbitMQ) for further processing. In a simple setup, you might configure Glances to periodically append to a CSV or log file, which you could analyze later. For more robust historical analysis, sending data to a time-series database like InfluxDB is commonwww.redhat.com. This way, you could use Grafana on top of that database to graph the metrics over time. Essentially, Glances can be a collector that feeds another system for long-term metrics, if needed.
Ease of Use: Glances is extremely easy to get running. You can install it via package manager (apt install glances on Debian/Ubuntu) or via pip (pip install glances). To start the web UI, just run glances -w and by default it will start a local web server (usually on port 61208). Open that in a browser and you’ll see the live dashboard updating every few seconds. No configuration is required for basic usage – it will auto-detect all it can. If you want to enable exports or tweak thresholds, you edit a simple config file (glances.conf). It also has a client/server mode: you can run Glances in server mode on each machine, and from a central machine run Glances in client mode to view remote stats in your terminal or web interfacegithub.com. This isn’t a single unified dashboard for multiple hosts, but it makes it easy to switch and view different machines’ stats remotely.
Resource Use: Since it’s Python-based, Glances will consume more CPU than a C program for the same work, but it’s still quite lightweight. When idle it sleeps, and when refreshing, it gathers info via the psutil library. Typically it might use a few percent of CPU while the UI is open. If you run it as a background service exporting data, its footprint is small. One advantage is that if you only run it on demand (e.g., when troubleshooting), it has zero impact the rest of the time. On a Raspberry Pi or similar, Glances can run fine for monitoring, although for constant use exporting high-frequency data, something like Prometheus+NodeExporter (written in Go) might be a bit more efficient in the long run.
Community Support: Glances has been around for quite a while and is fairly popular (almost 28k GitHub starsgithub.com). It’s well-documented (see the Glances ReadTheDocs site) and has an active maintainer. Its user community includes a lot of home lab and sysadmin folks who appreciate its simplicity. You can find it mentioned in many blog posts and forums for quick monitoring solutions. Being written in Python, it’s also easy to extend or troubleshoot by reading the source. Community support may not be as extensive as Prometheus/Grafana, but it’s definitely present and Glances integrates with many other tools (Home Assistant, for instance, can ingest Glances data). Setup: Running Glances is as simple as installing and executing it. For example, after installation, try glances (for CLI) or glances -w (for web). The web dashboard will update in real-time with no further configurationgithub.com. If you want Glances to start at boot and continuously run, you can set it up as a systemd service. To store data, edit the config to enable an export module – e.g., to log to a file or send to InfluxDB. The official docs have a section “Export and Logging” showing how to enable each output. This flexibility means you can keep Glances lean (just use it for eyeballing live stats), or turn it into a part of a larger monitoring pipeline.

Zabbix

_(Optional: If you’re looking for an enterprise-grade open-source monitoring solution with a web interface and long history, Zabbix is worth mentioning. It’s more heavyweight than the above tools, but provides a complete all-in-one monitoring server.)_Zabbix is a veteran in the monitoring space, known for its comprehensive features. It can monitor not just servers but network devices, VMs, cloud services, applications, and more. It’s not as “lightweight” as others (since it requires a dedicated server with a database), but it’s very powerful and scalable.

Features: Zabbix can “monitor your entire infrastructure” – from hardware health to application performancewww.webfx.com. It supports agent-based and agentless monitoring (e.g., via SNMP, IPMI, or SSH). Zabbix’s web UI allows you to set up dashboards, graphs, maps of your network, and detailed reports. It has a robust alerting system, supporting email, SMS, Slack, etc., with escalation and deduplication rules. Zabbix comes with many pre-defined templates for common systems (Linux OS, MySQL, Apache, cloud AWS/Azure metrics, etc.), so you can import those to quickly get monitoring for those components. It also offers user management (with roles and permissions), which is useful in team environments.
Data Storage: Zabbix uses a traditional SQL database (MySQL/PostgreSQL, etc.) to store configuration and historical data. All metrics collected are inserted into the DB. This allows long-term storage limited only by your database size. Zabbix can handle quite large volumes by using partitioning and house-keeping (it has settings to groom or aggregate older data). Because it’s a central DB, you can run SQL queries to extract data as well. For very large scale, maintaining the DB performance is the main challenge, but for small to medium installations it works well. Recent versions also introduced TimescaleDB support (PostgreSQL extension for time-series) to better handle large data history.
Ease of Use: Deploying Zabbix is more involved: you need to set up the Zabbix server (usually on Linux), install the database and web server (Apache/Nginx with PHP for the frontend), and optionally deploy Zabbix agents on hosts. Many distros have Zabbix packages, and there are appliances and Docker images that bundle everything to simplify this. Once up, you use the web interface to configure hosts and link them with monitoring templates. The learning curve is moderate – the UI has a lot of options and terminology (items, triggers, actions, etc.), but the official documentation is thorough. Zabbix’s strength is after the initial setup: it’s a one-stop system where you can see metrics, configure triggers (alerts), view logs, and even execute remote scripts on hosts. For someone who needs a full monitoring and management platform, Zabbix is a strong candidate. But for a small personal setup or if you just need basic resource graphs, it can be overkill.
Resource Use: Running a Zabbix server requires more resources than the other tools mentioned. The server process itself can use significant CPU/RAM if monitoring hundreds of hosts with tens of thousands of metrics. The database will also consume resources proportionate to the data stored. That said, for a handful of hosts, you can run Zabbix on a modest VM. The Zabbix agents are lightweight (written in C, similar footprint to Netdata’s agent). If you have an existing SQL server, piggybacking the Zabbix DB on it might be an option. Zabbix is known to be very stable even under heavy load, so it’s a trade-off of more initial resource usage for a system that can grow to large scale.
Community Support: Zabbix has been around for decades and has a large, active community. There are official forums, an IRC channel, and plenty of community scripts and templates shared on the Zabbix Exchange. Because it’s often used in enterprise, a lot of knowledge exists in blogs, and many professionals are familiar with it. The company behind Zabbix provides support and regular releases (it’s fully open-source, no paid tier for features). If you need help, you’ll find plenty of resources. Zabbix’s community might not be as “trendy” as the cloud-native Prometheus crowd, but it’s very solid. Documentation: For installation and setup, see the official Zabbix Documentation. They provide step-by-step guides for various platforms. A quick way to test it out is using their Docker containers or AWS images. Once running, the Zabbix frontend web UI will be your main interface to configure and monitor. Given its capabilities, Zabbix is best when you require a broad monitoring scope (network devices, applications) and a centralized solution, and are willing to allocate a bit more system resources to it.

Comparison Summary

To choose the right tool, consider the trade-offs:

Netdata: Offers extreme detail and real-time insight, great for troubleshooting and live monitoring. It’s easy to set up and has low runtime overheadgithub.com. However, its newer versions push towards cloud integration for advanced featureswww.netdata.cloud. Use Netdata if you want rich, per-second metrics and a polished local UI for a few nodes, and you’re okay with its default dashboard (or don’t mind the 5-node limit if staying local). It’s backed by a huge community and constant development.
Beszel: A lightweight Netdata alternative focused on essential metrics and self-hosted simplicity. It has a modern web UI and supports multi-node setups with users/roles out-of-box (no external cloud needed)github.com github.com. Beszel is ideal if you want an easy, minimal solution for monitoring multiple servers (and Docker containers) with historical data, and prefer something leaner than Netdata. Community is growing, and setup is quick with Docker or binaries.
Prometheus + Grafana: A best-in-class combo for custom monitoring. This is suited for those who need flexibility and extensibility. It’s more work to deploy, but you get complete control: you define what to collect (via exporters or instrumenting your code), how long to retain data, and you design dashboards as you like. It scales from one Raspberry Pi to thousands of servers. Choose this if you have the time to configure it and want a solution that can grow and integrate with other systems. The community and ecosystem (exporters, Grafana plugins) are unmatched in size.
Munin: Great for a no-frills, low-impact solution. If you just need to keep an eye on resource trends and don’t require fancy visuals or second-by-second updates, Munin is perfect. It’s extremely stable and light on resources, and once set up it needs almost no attention. Munin is especially popular for small servers or clusters where you want to “graph all the things” for later analysis without running heavy services.
Glances: Perfect for quick, real-time monitoring in a pinch. If you often find yourself SSHing into a box and running top or htop, Glances can be a drop-in replacement that gives more info at once. It’s also useful if you want a single dashboard on a wall or second screen to observe a system’s current state. While not a full historical monitoring system on its own, Glances can be part of one (sending data to a DB). Use Glances for monitoring on the go or lightweight setups, and consider exporting data if you need to analyze it later.
Zabbix: A heavy but feature-rich option. If your environment is more complex (mix of servers, network gear, applications) and you want a one-stop solution with alerting, user management, and rich dashboards, Zabbix might be worth the setup. It’s not as lightweight as others, but it’s robust and time-tested. Organizations with compliance or centralized control needs often prefer Zabbix. For a homelab or small deployment focused on system metrics, though, it’s usually not necessary – the above lighter tools will do the job. In summary, all these tools are open-source and capable of monitoring your system with historical data, but they vary in complexity and footprint. Netdata and Beszel shine for quick web-based insights with minimal setup. Prometheus/Grafana offers power and flexibility at the cost of more management. Munin and Glances excel in being lightweight and easy, targeting different monitoring styles (trend graphs vs. real-time). And Zabbix stands as an all-encompassing solution if you need that level of coverage. Assess your requirements for data resolution, retention, ease-of-use, and resource availability – there’s likely a tool above that fits your needs. Happy monitoring!