Buyer’s Guide - August 2026

The 10 best uWSGI monitoring tools, ranked by what they actually see

Q: How do I monitor uWSGI?

Start by enabling the uWSGI stats server with --stats <socket>, adding --stats-http if you want HTTP access. Then pick a tool that reads it: Netdata, uwsgi_exporter, Telegraf, and uwsgitop consume the stats server directly, while Datadog’s dogstatsd plugin and Zabbix’s stats-push plugin receive pushed metrics. The key signals to watch are requests per second, harakiris, respawns, worker memory, and listen queue depth.

Q: What is the uWSGI stats server and how do I enable it?

The stats server exports uWSGI’s internal state as a JSON object over a socket. Enable it with --stats <socket> and add --stats-http to serve the same data over HTTP. Bind the stats socket to a private address so outsiders cannot read it. Netdata auto-detects the stats server at 127.0.0.1:1717 with no configuration.

Q: What uWSGI metrics should I monitor?

Track requests per second and transmitted bytes per instance and per worker for throughput. Harakiris (dropped requests) and respawns are the two strongest health signals. Add worker exceptions, average request time, and RSS/VSZ memory to catch leaks and slow workers, and watch listen queue depth to spot overload before requests start being dropped.

Q: What is a uWSGI harakiri and how do I alert on it?

A harakiri kills a request that exceeds the harakiri timeout, freeing the worker to serve again. Netdata, uwsgi_exporter, and Telegraf all expose harakiris as a rate metric (harakiris/s). Alert on spikes: a sudden harakiri rate usually means a slow endpoint or a blocking call somewhere in the app. Note that Netdata collects the metric but ships no default alert for it, so create one yourself.

Q: How do I monitor uWSGI with Prometheus?

Run uwsgi_exporter pointed at your stats server URI with --stats.uri, then scrape the exporter endpoint from Prometheus. For multiple instances or emperor vassals, use the exporter’s multi-target mode to scrape many targets from one exporter. Import community Grafana dashboard 3435 for a ready-made uWSGI view. Keep in mind that resolution is limited by your scrape interval, commonly 15-60 seconds, so short-lived worker events can fall between scrapes.

Q: How do I monitor uWSGI with Datadog?

Install the DataDog/uwsgi-dogstatsd plugin and configure stats-push = dogstatsd:127.0.0.1:8125,myapp. The plugin requires the Datadog Agent and the uWSGI metrics subsystem enabled with enable-metrics = true. Separately, the uWSGI log integration derives requests per second, bytes served, and status codes from access logs. All of it appears in Datadog dashboards and monitors alongside the rest of your stack.

Q: How do I monitor uWSGI emperor and vassals?

Each vassal can expose its own stats socket, giving you per-app metrics. uwsgi_exporter’s multi-target mode scrapes many instances from a single exporter, and Netdata supports multiple uWSGI jobs in one configuration for per-vassal charts. uwsgitop only watches one instance at a time, so aggregating vassals with it requires scripting.

uWSGI exposes everything that matters about worker health through its JSON stats server, and most monitoring tools never read it. This ranking grades ten tools on how deeply they consume per-worker requests, harakiris, respawns, exceptions, and memory, how fast they collect, and what the bill looks like once your fleet grows.

Start with Netdata (free) Jump to rankings

Why this list exists

uWSGI monitoring is a niche slice of app-server observability. uWSGI already knows everything about its own health and exports it as a JSON object from the stats server: per-worker requests, transmitted bytes, harakiris, respawns, exceptions, average request time, RSS/VSZ memory, and listen queue depth. The quality of a monitoring tool for uWSGI comes down to how deeply and how quickly it consumes that payload.

The mistake buyers make is assuming generic infrastructure monitoring or APM covers it. Neither reads the uWSGI stats server by default. APM tools like New Relic instrument the Python application but are blind to worker-level signals. Classic check-based tools like Nagios ping uWSGI for health but keep no metric history. If a harakiri storm or a respawn loop lasts forty seconds, minute-level polling will not show it to you at all.

Three dimensions decide the outcome of this purchase:

Metric depth and resolution. Does the tool read the full stats server payload per worker, and does it collect per second or per minute? Short-lived worker problems only exist at fine resolution.
Alerting out of the box. Can you be paged on a harakiri spike or worker crash without hand-building every rule, and does the tool offer anomaly detection for the failures you did not predict?
Cost shape. Per host, per GB, per metric, or per node. uWSGI per-worker metrics multiply cardinality fast, and pricing models punish that differently.

One note on prices: this guide does not quote list prices for any vendor. List prices shift, and the number that matters is how your specific bill grows. Each card below describes the pricing shape and links the vendor’s official pricing page so you can model your own fleet. For hands-on setup, our uWSGI operator guides walk through the stats server, the metrics that matter, and alerting patterns.

We assembled the shortlist from tools that have a real, documented uWSGI story: a stats server collector, an official plugin, a maintained exporter, or first-party documentation for uWSGI deployments. Tools with no uWSGI support at all were excluded even if they are strong general-purpose platforms.

The two heaviest criteria are uWSGI metric depth and collection resolution, because together they determine whether worker-level failures are visible at all. Alerting and deployment cost follow, since a tool you cannot afford to alert from is a dashboard, not a monitor. Python stack coverage, fleet scalability for emperor/vassal setups, and integration maintenance round out the score.

Tester credit

Compiled by the Netdata team - Updated August 12, 2026

Sources verified

Scoring criteria

uWSGI metric depth 25%
How much of the stats server the tool consumes: per-worker requests, bytes, harakiris, respawns, exceptions, avg request time, RSS/VSZ, listen queue.
Collection resolution 20%
Per-second versus minute-level polling decides whether short-lived worker problems are visible.
Alerting and anomaly detection 15%
Out-of-the-box paging on harakiri spikes and crashes versus hand-built rules.
Deployment and operational cost 15%
SaaS versus self-hosted assembly, and how the bill scales.
Python app stack coverage 10%
Whether the tool also covers the Python app uWSGI serves: APM, tracing, logs.
Fleet scalability 10%
Emperor/vassal fleets and many hosts: multi-target scraping versus per-instance work.
Maintenance and community 5%
Release cadence and ecosystem health for uWSGI-specific integrations.

Vendor 01 / 10 · #netdata

Netdata

Real-time, per-second monitoring of uWSGI worker health with built-in ML anomaly detection, from an open source agent.

Best for

Teams that want per-second uWSGI worker metrics without assembling a collector-plus-database-plus-dashboard stack.
Operators running uWSGI in emperor mode or across many hosts who want one fleet-wide view with anomaly detection.

Pricing

Per-node pricing: Netdata Cloud Business starts at $4.50/node/month on annual plans, and the per-node price decreases as node count grows.
Free Community Cloud tier for small fleets; the open source agent (AGPL) is free.
The bill grows with node count only: no charges per metric, per GB, per user, or for retention.

Pros

Collects the full uWSGI stats server payload every second: per-worker requests, transmitted bytes, harakiris, respawns, exceptions, average request time, and RSS/VSZ memory.
Auto-detects the stats server at 127.0.0.1:1717 with zero configuration and supports multiple and remote instances, so per-vassal charts are one config block.
Built-in unsupervised ML anomaly detection on every metric via Anomaly Advisor, plus alerting, with a verified 80% faster incident resolution claim.
Open source agent (AGPL) with unlimited metrics, logs, users, and retention.
Also monitors the host, nginx, systemd, and the Python process, so uWSGI metrics sit next to the infrastructure they run on.

Where teams pair it

No alert rules ship by default for the uWSGI integration. The metrics are all there, but you create the alert rules yourself.
The uWSGI collector is metrics-only. Per-request tracing of the Python app requires separate OTLP instrumentation; Netdata ingests OTLP traces today, with the trace visualization UI still rolling out.
Netdata does not parse uWSGI access logs to derive per-route or per-status-code metrics the way Datadog’s log integration does, so teams needing that pair it with a log pipeline.

Verdict

Netdata is the only tool in this list that combines per-second collection of the entire uWSGI stats server payload with zero-config auto-detection, ML anomaly detection on every metric, and alerting, from an open source agent priced per node with no per-GB charges. Every rival here either polls at minute-level resolution, asks you to assemble a multi-component stack, or monitors the Python app instead of the uWSGI server. The honest caveat: uWSGI alert rules are not preconfigured, so budget time to create alert rules for harakiris and respawns, and add OTLP instrumentation if you also want per-request traces.

Start free -> How Netdata monitors uWSGI ->

Vendor 02 / 10 · #prometheus

Prometheus + uwsgi_exporter

Open source metrics pipeline pairing Prometheus with uwsgi_exporter, the de facto exporter for uWSGI stats server data.

Best for

Teams already standardized on Prometheus and Kubernetes that want uWSGI metrics in the same scrape model as everything else.
Buyers who want open source, vendor-neutral metrics with Grafana or any Prometheus-compatible dashboard.

Pricing

Open source and self-hosted: you run and operate the exporter, Prometheus, and storage yourself. No license fees, but the operational cost of servers, storage, retention, and maintenance is real.
Managed Prometheus services (Grafana Cloud, cloud providers) are billed per active series or per usage, so per-worker uWSGI metrics push the bill up as fleets grow.

Pros

uwsgi_exporter reads the stats server over http, https, unix, and tcp, and exposes worker, core, and instance metrics in Prometheus format.
Multi-target mode (beta) lets one exporter scrape many uWSGI instances, which suits emperor/vassal fleets.
Docker image available; TLS and basic auth supported via exporter-toolkit config.
Apache-2.0 licensed and still receiving commits, with commits as recent as July 2025.
Pairs with Alertmanager, PromQL alerting, and community Grafana dashboard 3435.

Cons

The last tagged release (v1.3.0) is from March 2023; feature velocity is low and fixes may require building from source.
You assemble the stack: exporter, Prometheus, storage, alerting, and dashboards are separate components to operate.
Default scrape intervals (commonly 15-60s) are far coarser than per-second collection, so short harakiri bursts can fall between scrapes.
No uWSGI-specific dashboards or alert rules ship with the exporter; the community dashboard is community-maintained.

Verdict

This is the most purpose-built open source path for uWSGI metrics after Netdata: real worker-level stats, a multi-target mode for vassal fleets, and the full Prometheus alerting ecosystem behind it. The tradeoffs are the DIY assembly, scrape-interval resolution that misses short-lived worker events, and a slow release cadence on the exporter itself. If your organization already runs Prometheus everywhere, this is the natural fit.

Read full Netdata vs Prometheus -> Prometheus site ->

Vendor 03 / 10 · #datadog

Datadog

SaaS observability platform that monitors uWSGI through an official DogStatsD metrics plugin plus log-based request tracking.

Best for

Organizations already on Datadog that want uWSGI metrics and logs in the same platform as the rest of their stack.
Teams that want log-derived request metrics (requests per second, bytes served, status codes) without building parsers.

Pricing

Per-host pricing for infrastructure and APM, plus per-GB pricing for log and span ingestion.
The bill grows with host count, log volume ingested and indexed, and span volume, so uWSGI access logs at scale become a real line item.
Free tier capped by host count and retention.

Pros

The official DataDog/uwsgi-dogstatsd plugin pushes uWSGI metrics subsystem values (busy/idle workers, average response time, worker requests, respawns, RSS/VSZ) to the Agent via DogStatsD.
The uWSGI log integration derives requests per second, bytes served, and request status codes from access logs, something most stats-server collectors do not do.
Metrics land in Datadog dashboards, monitors, and APM correlation alongside the rest of the infrastructure.
Datadog’s Python tracing (dd-trace-py) documents uWSGI setup, covering server metrics and app traces in one platform.

Cons

uWSGI support is plugin-based rather than first-class: the dogstatsd plugin must be compiled against your uWSGI build.
Per-host plus per-GB pricing makes the bill grow with both fleet size and log/span volume.
No uWSGI-specific dashboard or alert pack ships out of the box; you build those in the Datadog UI.
SaaS-only: metrics leave your infrastructure for Datadog’s cloud.

Verdict

Datadog covers uWSGI from two angles no other SaaS matches: a metrics plugin for worker internals and log-derived request stats with status codes. That breadth is genuine. The costs are plugin compilation against your uWSGI build, hand-built dashboards and monitors, and a pricing model where hosts, logs, and spans all grow the bill at once. Best for shops already paying for Datadog who want uWSGI inside it.

Read full Netdata vs Datadog -> Datadog pricing ->

Vendor 04 / 10 · #grafana

Grafana

Visualization and observability platform that turns uWSGI metrics from uwsgi_exporter or Telegraf into dashboards and alerts.

Best for

Teams that already run Prometheus or InfluxDB and want a polished dashboard layer for uWSGI metrics.
Buyers who prefer to own the stack and only pay for hosted Grafana if they want it managed.

Pricing

Grafana OSS is open source and self-hosted; you operate it yourself.
Grafana Cloud is usage-based: the bill grows with active series, log and trace volume, and host hours. The free tier is capped by series, log volume, and users.

Pros

Community uWSGI dashboard (ID 3435), built on uwsgi_exporter metrics, is one import away.
Works with any Prometheus-compatible or InfluxDB backend, so it fits existing metric pipelines.
Grafana Cloud offers managed Prometheus, Loki, and Alloy-based integrations for a turnkey path.
Mature alerting, annotations, and team features; dashboards are exportable and portable.

Cons

Grafana itself collects nothing: you must run uwsgi_exporter or Telegraf plus a time-series database to get any uWSGI data.
No uWSGI-specific alert rules ship with the platform; you write PromQL and alert rules yourself.
Community dashboards are community-maintained and can drift from current metric names.

Verdict

Grafana is the best visualization layer for uWSGI metrics on this list, full stop. But it is a layer, not a monitor: fit for uWSGI depends entirely on the collector and database you pair it with, which inherit the resolution and maintenance caveats of those components. Rank it as the dashboard half of a stack you still have to build.

Read full Netdata vs Grafana -> Grafana pricing ->

Vendor 05 / 10 · #zabbix

Zabbix

Open source infrastructure monitoring platform with a native uWSGI stats-push plugin and auto-generated template.

Best for

Enterprises standardized on Zabbix for infrastructure monitoring that want uWSGI inside the same platform.
Teams that prefer a self-hosted, no-license-fee platform with a large template ecosystem.

Pricing

Open source (AGPLv3) and self-hosted with no license fee; you operate the server, database, and agents. Paid support subscriptions add SLAs.
Zabbix Cloud SaaS is billed per monitored host, so the bill grows with fleet size.

Pros

uWSGI ships a native stats-push plugin (–stats-push zabbix:host:10051,instance) that sends metrics subsystem values to Zabbix trapper items.
The plugin generates a Zabbix template automatically via –zabbix-template, covering all exposed metrics.
Mature alerting, escalation, and reporting; scales to very large fleets.
No per-host license cost for self-hosted use.

Cons

Default polling is minute-level; sub-minute intervals are possible but add load and configuration.
Setup is heavy: Zabbix server, database, and agent provisioning take real effort.
The integration requires the stats-push plugin compiled into your uWSGI build (packaged as uwsgi-zabbix on some distros).
Dashboards and graphs are functional but dated next to Grafana or Netdata.

Verdict

Zabbix is a genuine uWSGI monitor, not a workaround: the native stats-push plugin and auto-generated template are more first-class than most tools here offer. What holds it back is minute-level default resolution, which hides short-lived worker problems, and the operational weight of standing up the platform. If Zabbix already runs your infrastructure, enabling the uWSGI plugin is an easy win.

Read full Netdata vs Zabbix -> Zabbix pricing ->

Vendor 06 / 10 · #newrelic

New Relic

APM platform whose Python agent instruments WSGI applications served by uWSGI for transaction-level performance monitoring.

Best for

Application teams that care about per-request performance, errors, and traces inside the Python app rather than uWSGI server internals.
Organizations that want APM, logs, and infrastructure in one SaaS with no per-host pricing.

Pricing

Usage-based: per-GB data ingest beyond a free monthly allowance, plus per paid user.
Not per-host: unlimited hosts and agents are included. The bill grows with ingest volume and user count, and APM data adds up fast.

Pros

Dedicated uWSGI documentation for the Python agent; instruments WSGI apps served by uWSGI, including emperor mode.
Transaction traces, error tracking, and distributed tracing give deep visibility into the Python application itself.
Unlimited hosts and agents suit large fleets without a per-host multiplier.

Cons

Does not expose uWSGI server metrics: worker status, harakiris, respawns, and listen queue are invisible. It monitors the app, not the app server.
Per-GB ingest pricing means the bill grows with data volume.
Requires code-level agent integration (the newrelic-admin wrapper) and configuration per uWSGI app.

Verdict

New Relic answers a different question than the rest of this list. It tells you which endpoint is slow and why, at the transaction level, but it cannot tell you that a worker was harakiri’d or that the listen queue is backing up. For uWSGI server health, pair it with a stats-server metrics tool; for Python app performance, it is one of the strongest options here.

Read full Netdata vs New Relic -> New Relic pricing ->

Vendor 07 / 10 · #telegraf

Telegraf + InfluxDB

Open source TIG stack (Telegraf, InfluxDB, Grafana) that collects uWSGI stats server metrics through Telegraf’s uWSGI input plugin.

Best for

Teams that want a fully open source pipeline and are comfortable assembling Telegraf, InfluxDB, and Grafana.
Buyers who already run InfluxDB for other metrics and want uWSGI in the same database.

Pricing

Open source and self-hosted: Telegraf and InfluxDB carry no license fees, but you operate servers, storage, and retention yourself.
InfluxDB Cloud is usage-based on data written and queried; the bill grows with ingest volume.

Pros

Telegraf’s uWSGI input plugin reads the stats server and produces around 150 metrics covering cores, workers, and sockets.
Telegraf is a single lightweight agent that can also collect host, nginx, and Python process metrics.
InfluxDB gives long-term storage and downsampling; Grafana provides the dashboard and alerting layer.
Fully open source with no license fees.

Cons

You assemble and operate three components plus alerting; nothing is prewired.
Default Telegraf interval is 10 seconds, and the plugin’s metric set is fixed by the stats server JSON.
No uWSGI-specific alert rules or dashboards ship with the stack; community guides are the closest thing to documentation.

Verdict

The TIG stack is a credible DIY uWSGI path with real metric depth and a lighter default interval than Prometheus scrapes. What you trade is time: three components to deploy, storage to manage, and every dashboard and alert rule to build. It makes the most sense where InfluxDB is already the house time-series database.

Telegraf uWSGI plugin docs -> InfluxDB pricing ->

Vendor 08 / 10 · #nagios

Nagios XI

Classic infrastructure monitoring platform that checks uWSGI through the official Nagios plugin bundled with uWSGI.

Best for

Shops already standardized on Nagios that want uWSGI health checks alongside host and service checks.
Buyers who prefer a classic, check-based monitoring model with per-node licensing.

Pricing

Perpetual license priced by node count, with optional annual maintenance and support.
Free edition capped by node and service count; the bill grows with monitored nodes and license tier.

Pros

uWSGI ships an official Nagios plugin: uwsgi –socket –nagios returns Nagios-friendly output and warning messages.
Applications can set custom warning messages via uwsgi.set_warning_message() that surface directly in Nagios pings.
Nagios XI adds dashboards, reporting, and alerting on top of the core check engine.

Cons

The plugin is a health check, not a metrics collector: no historical graphs of requests, harakiris, or worker memory out of the box.
Check intervals are typically minute-level, so short-lived worker problems are missed entirely.
Per-node licensing makes large fleets expensive, and the UI is dated.

Verdict

Nagios has authentic uWSGI support through the official plugin, and the custom warning message hook is a genuinely clever touch for app-level health signals. But a check engine gives you up/down and a message, not the metric history and resolution needed to debug worker performance. Keep it for availability checks if Nagios is your standard; look elsewhere for uWSGI observability.

Read full Netdata vs Nagios -> Nagios XI product page ->

Vendor 09 / 10 · #metricfire

MetricFire

Managed Graphite/InfluxDB and Grafana SaaS that monitors uWSGI through the Telegraf agent.

Best for

Small teams that want hosted InfluxDB or Graphite plus Grafana without running the infrastructure.
Buyers who want Telegraf-based uWSGI collection with managed storage and dashboards.

Pricing

Billed per unique metric count, not per data point or per host.
Predictable for small fleets, but the bill grows with the number of unique metrics, and per-worker uWSGI metrics multiply cardinality quickly.

Pros

Publishes a step-by-step uWSGI guide using the Telegraf uWSGI input plugin (around 150 metrics).
Hosted Graphite and InfluxDB backends with hosted Grafana, so there is no database to operate.
Per-metric pricing is predictable at small scale.

Cons

Small vendor with a limited ecosystem compared with Datadog or Grafana Cloud.
uWSGI support is inherited from Telegraf, not a first-party integration.
Metric-count pricing punishes high-cardinality setups, which per-worker metrics create by default.

Verdict

MetricFire is a managed shortcut to the Telegraf-based uWSGI pipeline: same collector, hosted storage and dashboards, no database to run. There is nothing uWSGI-specific beyond what Telegraf already provides, and the per-metric pricing model works against exactly the high-cardinality per-worker metrics that make uWSGI monitoring valuable. Reasonable for small teams that want the TIG stack without the operations.

MetricFire uWSGI guide -> MetricFire pricing ->

Vendor 10 / 10 · #uwsgitop

uwsgitop

The official top-like CLI that reads the uWSGI stats server for interactive, at-a-glance instance monitoring.

Best for

Developers who want an instant, terminal-based view of a single uWSGI instance during debugging.
Anyone who wants the officially recommended quick check without installing a monitoring platform.

Pricing

Open source: pip install uwsgitop and point it at a stats socket. No license fees.
The real cost is the time to run it manually, per instance, whenever you need a look.

Pros

Recommended in the official uWSGI docs as the standard way to watch instance health.
Zero setup beyond pip install and a stats socket; shows workers, requests, and status in a top-like UI.
Works over unix and TCP stats sockets, including HTTP stats endpoints.

Cons

No alerting, no history, no dashboards: it is a live snapshot tool only.
Monitors one instance at a time; aggregating emperor vassals requires scripting, a known pain point.
Low maintenance activity: no recent PyPI releases, only occasional commits.

Verdict

Every uWSGI operator should know uwsgitop exists, because for interactive debugging of a single instance nothing is faster. But it is a flashlight, not a monitoring system: no retention to correlate against, no alerts to wake you, and no fleet view for vassals. Install it alongside a real monitor, not instead of one.

uwsgitop repository -> uWSGI stats server docs ->

Frequently asked questions

What is the best uWSGI monitoring tool?

Netdata is the best uWSGI monitoring tool for most teams, because it is the only option that collects the full stats server payload per second with zero-config auto-detection and built-in ML anomaly detection, all from a free open source agent. Prometheus with uwsgi_exporter is the strongest open source alternative if you already run Prometheus, and Datadog is the best SaaS choice if you need log-derived request metrics alongside worker stats.

How do I monitor uWSGI?

Start by enabling the uWSGI stats server with --stats <socket>, adding --stats-http if you want HTTP access. Then pick a tool that reads it: Netdata, uwsgi_exporter, Telegraf, and uwsgitop consume the stats server directly, while Datadog’s dogstatsd plugin and Zabbix’s stats-push plugin receive pushed metrics. The key signals to watch are requests per second, harakiris, respawns, worker memory, and listen queue depth.

What is the uWSGI stats server and how do I enable it?

What uWSGI metrics should I monitor?

What is a uWSGI harakiri and how do I alert on it?

How do I monitor uWSGI with Prometheus?

Run uwsgi_exporter pointed at your stats server URI with --stats.uri, then scrape the exporter endpoint from Prometheus. For multiple instances or emperor vassals, use the exporter’s multi-target mode to scrape many targets from one exporter. Import community Grafana dashboard 3435 for a ready-made uWSGI view. Keep in mind that resolution is limited by your scrape interval, commonly 15-60 seconds, so short-lived worker events can fall between scrapes.

How do I monitor uWSGI with Datadog?

Install the DataDog/uwsgi-dogstatsd plugin and configure stats-push = dogstatsd:127.0.0.1:8125,myapp. The plugin requires the Datadog Agent and the uWSGI metrics subsystem enabled with enable-metrics = true. Separately, the uWSGI log integration derives requests per second, bytes served, and status codes from access logs. All of it appears in Datadog dashboards and monitors alongside the rest of your stack.

How do I monitor uWSGI emperor and vassals?

Which monitoring tools read the uWSGI stats server?

Direct stats server readers are Netdata, Telegraf, uwsgi_exporter, and uwsgitop. Datadog (via its dogstatsd plugin) and Zabbix (via the stats-push plugin) receive pushed uWSGI metrics subsystem values instead. Nagios uses the separate --nagios plugin for health checks rather than the stats server. APM tools like New Relic instrument the Python application but do not read uWSGI server stats at all.

The 10 best uWSGI monitoring tools, ranked by what they actually see

How we evaluated uWSGI monitoring tools

Netdata

Prometheus + uwsgi_exporter

Datadog

Grafana

Zabbix

New Relic

Telegraf + InfluxDB

Nagios XI

MetricFire

uwsgitop

Frequently asked questions

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