With over 31,000 GitHub stars and native OpenTelemetry support that eliminates vendor lock-in from day one, SigNoz delivers full-stack observability covering metrics, traces, and logs in a single pane of glass without the per-host pricing model of commercial APM platforms. The platform ingests telemetry data through the OpenTelemetry Collector, supporting auto-instrumentation for Java, Python, Node.js, Go, Ruby, PHP, and .NET applications with zero code changes required for basic tracing. ClickHouse serves as the columnar storage backend, providing fast aggregation queries over billions of spans and log lines with configurable retention policies and tiered storage. The distributed tracing view renders flame graphs and Gantt charts showing request flow across microservices with latency breakdowns, error rates, and p99 percentile calculations. Custom dashboards support PromQL and ClickHouse SQL queries with time-series charts, bar graphs, tables, and value widgets. The log management pipeline supports structured and unstructured logs with full-text search, log pipelines for parsing and enrichment, and correlation with traces via trace IDs. Alert rules can be configured on any metric or log query with notification channels including Slack, PagerDuty, OpsGenie, webhooks, and email. The exceptions monitoring module automatically groups and tracks application errors with stack traces, occurrence counts, and first-seen timestamps. Service maps visualize inter-service dependencies with real-time latency and error rate overlays. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed with an enterprise edition available.

Pinpoint distributed system bottlenecks across polyglot microservices using Uptrace, an open telemetry monitoring platform that correlates traces, metrics, and logs inside a unified diagnostic dashboard. Engineers can track end-to-end request flows across polyglot microservices, inspecting database queries, third-party API calls, and internal function calls with sub-millisecond precision. The platform automatically aggregates spans into high-level latency percentiles, highlighting p50, p90, and p99 bottlenecks while calculating baseline error rates across production deployments. Integrated log correlation links log entries directly to parent trace spans, eliminating the friction of manual search across separate monitoring tools during active outages. You can construct custom dashboard views using a visual query builder, monitor cluster nodes, and map out upstream and downstream service dependencies with interactive topological graphs. Automated alerting channels notify on-call teams via Slack, Telegram, webhooks, or email whenever error budgets or metric thresholds trigger an incident. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

With 20,500+ GitHub stars and over 6,000 organizations running it in production — including a Fortune 100 company ingesting more than 4 PB per day — OpenObserve is the open-source observability platform that replaces your entire Datadog, Splunk, or ELK stack with a single Rust binary deploying in under two minutes. Apache Parquet columnar storage with zstd compression on S3-compatible object storage delivers 140x lower storage costs than Elasticsearch while providing better query performance on a quarter of the hardware. Ingest logs, metrics, and distributed traces via native OpenTelemetry OTLP endpoints with no vendor lock-in. Query logs and traces with standard SQL, metrics with SQL or PromQL — no proprietary query language to learn. The built-in dashboard builder offers 19 chart types including time-series graphs, heatmaps, gauges, tables, and top-K lists with drag-and-drop layout combining data from all signal types. Data pipelines process, enrich, redact, or normalize ingestion streams using Vector Remap Language for real-time transformations including PII redaction and logs-to-metrics conversion. Real User Monitoring captures frontend performance with session replay. The Service Catalog provides topology-based trace analysis with side-panel drill-downs into database queries and error details. Alerting supports real-time and scheduled rules with SQL and PromQL conditions. Native multi-tenancy isolates organizations and streams with complete data separation. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPLv3 licensed.

HyperDX correlates logs, metrics, traces, session replays, and errors in a single interface so engineers can resolve production incidents in minutes instead of hours. Nearly 10,000 GitHub stars reflect its role as the integrated UI layer for the ClickStack blueprint endorsed by ClickHouse. The platform connects to any ClickHouse cluster as its storage backend, working with existing table structures without requiring data migration or proprietary ingestion formats. An intuitive Lucene-like search syntax supports full-text queries and property filtering like level:err or service.name:api without needing SQL, while native JSON string querying and event delta analysis surface anomalies in high-cardinality datasets. One-click cross-signal correlation lets you jump from a log line to its distributed trace, from a slow span to associated logs, or from a frontend session replay to the backend errors it triggered. The OpenTelemetry Collector accepts telemetry via OTLP on gRPC port 4317 and HTTP port 4318, supporting automatic instrumentation for Node.js, Python, Java, Go, Ruby, and browser applications. APM tracks HTTP request latency, database query duration, and external service calls with trace waterfall visualizations. Configurable alerts trigger via webhook, Slack, PagerDuty, or email when thresholds are breached. Deploys via Docker Compose with ClickHouse, MongoDB, Redis, and the OpenTelemetry Collector. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Across logs, traces, and metrics, Monoscope gives engineering teams one self-hosted workspace for finding what broke, asking questions in plain English, and hearing about problems before users do. Start in the Explorer: search events with KQL or an Ask AI prompt, flip between log rows, bar and line charts, log patterns, and sessions, and narrow results with facets for service, level, and HTTP status. Open any span to read its attributes, then follow the full trace across services in a waterfall, timeline, or map view. Live Tail streams new events as they arrive, and the Service Map draws every dependency with its error rate, latency, and requests per second. Build dashboards from widgets with copyable KQL and SQL, then set scheduled monitors with warn and alert thresholds, cron monitors, and uptime checks. An Issues inbox gathers API changes, errors, and log patterns so each one can be acknowledged, archived, or marked as spam, while Reports send scheduled summaries by email. Telemetry arrives over OTLP gRPC on port 4317 from OpenTelemetry SDKs and collectors. The monoscope CLI emits JSON for scripts, and an MCP server at /api/v1/mcp lets Claude, Cursor, and Cline search events and triage issues. Natural-language search uses the OpenAI-compatible model you configure. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

Coroot uses eBPF to capture metrics, distributed traces, logs, and continuous CPU profiles directly from the Linux kernel, delivering full observability without any application code changes, SDKs, or sidecars. From the first minute of deployment, an automatically generated service map covers every microservice, database, message queue, and external dependency with request rate, error rate, and latency measurements. When a service breaches its SLO, AI-powered inspections analyze telemetry across all dimensions to pinpoint the root cause and send a single consolidated alert with findings, replacing the flood of fragmented notifications typical of traditional monitoring. Deployment tracking automatically discovers Kubernetes rollouts and compares each release against the previous one to detect performance regressions, resource spikes, and cost impacts without CI/CD pipeline integration. Continuous profiling captures CPU flame graphs down to the line of code with negligible overhead. Integrated cost monitoring tracks cloud spending across AWS, GCP, and Azure, attributing expenses to individual services and deployments. Coroot supports Prometheus, OpenTelemetry, and ClickHouse as data sources and works identically on Kubernetes clusters, virtual machines, and bare-metal hosts. 7,700+ GitHub stars. Apache-2.0 licensed.

Instead of deploying proprietary background agents across dozens of target nodes, engineers rely on Apache HertzBeat to monitor real-time infrastructure health, metrics gathering, threshold alerting, and public status pages from a central operations platform. Operations teams can poll hundreds of target services without deploying proprietary background daemons, gathering performance data across Linux hosts, Kubernetes clusters, SQL databases, and network switches using native connection protocols. Engineers can define custom monitoring targets directly within the web dashboard by composing declarative YAML templates that specify polling intervals, parsing expressions, and metric extraction rules. The centralized alert engine processes inbound threshold events, suppresses cascading alert storms during maintenance windows, and dispatches actionable incident notifications to Discord channels, Slack rooms, Telegram groups, and webhook endpoints. Telemetry streams flow into interactive charts with customizable refresh cadences, enabling site reliability engineers to inspect latency waterfalls, correlate log spikes against CPU exhaustion, and track disk capacity trends over extended timeframes. Administrators can also publish real-time public status pages that inform external stakeholders about service availability, scheduled downtime, and ongoing incident resolutions. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.

Production debugging slows to a crawl when logs, distributed traces, metrics, and session replays live in four disconnected dashboards, which is why Traceway consolidates every OpenTelemetry signal into a single self-hosted observability platform. Point any standard OTLP exporter directly at the ingestion endpoints without maintaining an intermediary collector or installing proprietary vendor SDKs. When an unhandled exception fires in production, automatic SHA-256 stack trace fingerprinting groups duplicates into prioritized issues complete with source-mapped frames and attached browser or Flutter session replays showing exact user clicks before the crash. Clicking any structured log entry jumps straight to its corresponding distributed span waterfall so engineers can trace slow database queries or failing downstream HTTP calls across microservices. Route performance bottlenecks surface immediately through per-endpoint P50, P95, and P99 latency percentiles ranked by Apdex and a five-factor impact score. Teams shipping AI features can also inspect LLM token consumption, model costs, tool calls, and multi-turn conversations grouped by customer identifier. Built-in on-call rotation schedules, escalation policies, and multi-channel alerts via Slack, Telegram, Pushover, email, and webhooks ensure incidents reach the right responder with one-click acknowledgment links. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Created by Uber Technologies and graduated as the seventh CNCF top-level project in October 2019 with over 23,000 GitHub stars, Jaeger has become one of the most widely deployed open-source distributed tracing platforms, processing billions of spans per day in production environments at organizations including Uber, Red Hat, and Shopify. Version 2 rebuilt the platform on the OpenTelemetry Collector framework, inheriting its extensible pipeline architecture while implementing Jaeger-specific features as extensions and components, enabling seamless integration with the OpenTelemetry ecosystem through native OTLP protocol support. The platform stores traces in Cassandra 4.0+, Elasticsearch 7.x/8.x, OpenSearch 1.0+, ClickHouse, or the embedded Badger database for development setups. Three sampling strategies control trace volume: head-based sampling with constant, probabilistic, and rate-limiting modes, tail-based sampling using the OpenTelemetry Collector processor that evaluates complete traces before storage decisions, and adaptive sampling that dynamically adjusts probabilities based on observed traffic patterns. Service Performance Monitoring computes RED metrics directly from spans, displaying request rates, error rates, and latency percentiles in the Monitor tab with drill-down from aggregate service views to individual traces. The web UI provides trace search with multi-field filtering, trace detail views with span timeline visualization, trace comparison across services, and dependency graphs mapping service relationships from actual traffic. Deployment options range from a single all-in-one binary for development to distributed collector-ingester-query configurations with Kafka intermediate buffering for production scale. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.

The de facto dashboard of observability: Grafana is the open-source frontend that turns the data stores you already run into interactive graphs. It does not store metrics itself; it connects to the data stores you already run and turns their contents into interactive dashboards. Supported sources number over 150 via plugins: Prometheus, Loki, Tempo, InfluxDB, Elasticsearch, MySQL, PostgreSQL, Microsoft SQL Server, AWS CloudWatch, Azure Monitor, Google Cloud Monitoring, and many more. Dashboards are built from a large library of panel types (time series, heatmaps, tables, gauges, logs) with template variables for reusable, parameterized views. Unified alerting evaluates rules against any connected data source, not just Prometheus, and routes notifications to Slack, PagerDuty, email, and other channels with grouping and silencing - unlike Prometheus Alertmanager, a single rule can combine a Loki log pattern, a PostgreSQL query result, and a CloudWatch metric. Dashboards serialize to JSON and data sources configure via provisioning files, so the entire observability setup can live in Git and deploy repeatably across environments. Explore mode adds ad-hoc querying outside dashboards, with split view for correlating a metric spike against the matching log lines, and access control spans organizations, teams, folder permissions, and OAuth, LDAP, and SAML integration. Written in Go and TypeScript, AGPL-licensed. Self-hosting gives you unlimited users, dashboards, and queries at flat hosting cost, without Grafana Cloud's usage-based pricing.

Trusted by millions of engineers and deployed on over 80,000 GitHub stars worth of community confidence, Netdata delivers true real-time monitoring at per-second granularity — 10-60x faster than Prometheus, Datadog, or any conventional monitoring stack that averages away the transient anomalies lasting 2-10 seconds where most production incidents originate. A single installation command deploys the agent with zero configuration, automatically discovering every running process, container, systemd service, network connection, disk, and application on the host within seconds. Unsupervised machine learning trains multiple models per metric directly at the edge, detecting anomalies without thresholds, baselines, or manual tuning. The distributed Parent-Child architecture scales horizontally from a single Raspberry Pi to fleets exceeding 100,000 nodes while maintaining sub-2-second visualization latency and storing metrics at approximately 0.5 bytes per sample through tiered compression. Native network monitoring provides live topology maps, NetFlow and sFlow analytics, SNMP device polling across 200+ profiles, and trap handling — capabilities that typically require a separate NPM product. Hundreds of pre-configured alerts cover systems and applications out of the box, with AI-powered root cause analysis surfacing correlated metrics through natural language via MCP-compatible AI assistants. The agent supports Linux, macOS, FreeBSD, Kubernetes, and Docker with eBPF-based kernel observability requiring no application instrumentation. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. GPL v3+ licensed.

Moneat is the open-source observability platform that unifies error tracking, session replay, performance monitoring, logging, uptime checks, synthetics, product analytics, and AI observability into a single self-hosted application — replacing Sentry, Datadog, and Statuspage with one deployment. The Sentry SDK compatibility layer accepts data from @sentry/browser, @sentry/node, @sentry/react, @sentry/nextjs, sentry-sdk for Python, sentry-kotlin, sentry-java, sentry-android, sentry-cocoa, sentry-go, sentry-ruby, and Sentry.NET by updating one DSN endpoint. Datadog Agent compatibility redirects existing fleets by setting dd_url, and native OpenTelemetry OTLP ingestion accepts logs, traces, and metrics from any exporter or Collector. Error monitoring groups exceptions with smart deduplication, session replay records DOM-based user interactions linked to errors, distributed tracing visualizes transaction and span breakdowns with live service maps, and continuous profiling renders flamegraphs in pprof, JFR, and Sentry formats. Uptime monitoring runs HTTP, TCP, and ping checks with public status pages, while synthetics executes API tests, multi-step workflows, SSL checks, and DNS probes. Custom dashboards support drag-and-drop widgets with Grafana import, product analytics provides funnels and retention cohorts, release tracking surfaces crash-free rates with source map upload, and AI observability traces LLM calls end to end. Built on Kotlin and Java with ClickHouse for analytical storage, PostgreSQL for relational data, and Redis for caching, deployment uses Docker Compose with an interactive installer automating secrets and service orchestration. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

Trusted by thousands of organizations processing billions of time series data points and backed by 17,000+ GitHub stars, VictoriaMetrics delivers a monitoring and time series database that outperforms Prometheus by 16x on query speed while consuming 2.5x less disk space through its optimized compression and storage engine. The architecture supports both single-node deployments handling 10M+ active time series and a horizontally scalable cluster version with vminsert, vmstorage, and vmselect components providing multi-tenancy, replication, and independent namespace isolation. Data ingestion accepts both push protocols including InfluxDB line protocol, Graphite plaintext, OpenTSDB HTTP, CSV, and OpenTelemetry OTLP alongside pull-based Prometheus scraping and remote write, enabling drop-in replacement of existing monitoring stacks without reconfiguring exporters. MetricsQL extends standard PromQL with additional functions, subqueries, and implicit time range alignment while maintaining full backward compatibility with existing Prometheus alerts and Grafana dashboards. The vmalert component processes recording and alerting rules with Alertmanager integration, while vmbackup and vmrestore enable point-in-time snapshots to S3, GCS, and Azure Blob Storage. Stream aggregation operates as a StatsD alternative for pre-aggregating high-cardinality metrics before storage. NFS-compatible storage backends including Amazon EFS and Google Filestore allow shared persistent volumes across cluster nodes. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

Backed by 44,000 GitHub stars and trusted by over four million developers, Sentry is the debugging platform that captures errors, traces, replays, profiles, and metrics from your applications and connects them all through distributed tracing. The error tracking engine captures full stack traces with source context, breadcrumbs, and automatic demangling for native crashes, while intelligent grouping consolidates duplicate events into actionable issues with regression detection and automatic assignment. Performance monitoring instruments your frameworks automatically, capturing every database query, API call, cache hit, and queue operation as spans within distributed traces that flow across frontend, backend, and mobile boundaries. Session Replay produces video-like recordings of real user sessions showing DOM interactions, network requests, console messages, and errors on a synchronized timeline, with AI-powered summaries that describe what happened without watching the full replay. Continuous profiling captures CPU execution data at the function and line level for Node.js, Python, iOS, and Android, linking slow spans directly to the exact code responsible. Cron monitoring tracks scheduled jobs for failures, missed runs, and duration anomalies. The alerting engine fires notifications through Slack, PagerDuty, Opsgenie, and webhooks on new issues, regressions, error spikes, or when latency and crash-free session rate thresholds are crossed. Self-hosted deployment runs as a Docker Compose stack with PostgreSQL, ClickHouse, Kafka, Redis, and Relay. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. FSL licensed.

Backed by 1,080+ GitHub stars and maintained by Percona with the latest release v3.8.1 in June 2026, Percona Monitoring and Management delivers the open-source database observability platform that provides a single pane of glass across MySQL, PostgreSQL, MongoDB, Valkey, and Redis databases deployed on-premises, cloud, or hybrid environments. The Go-powered PMM Server collects metrics from lightweight PMM Client agents with minimal performance impact, storing time-series data in ClickHouse for fast querying across configurable retention periods. Query Analytics ranks every query by load across all database engines from one unified dashboard, drilling from fleet-level performance down to individual problematic queries with explain plans, per-query metrics, and anomaly detection. Real-time Query Analytics streams live MongoDB operations updated every 1-5 seconds for immediate troubleshooting of lock contention and long-running queries. Built-in Percona Advisors continuously scan connected databases for security gaps, misconfigurations, and performance problems, distilling decades of DBA expertise into automated actionable recommendations. Percona Alerting integrates with 15+ notification channels including Slack, PagerDuty, email, and webhooks to trigger on custom metric thresholds. Database-specific dashboards visualize InnoDB storage engine details, WiredTiger cache metrics, PostgreSQL tuple activity, replication lag, and cluster health with annotations for root-cause correlation. Deployment options include Docker single-container setup, Podman rootless execution, and Helm charts for Kubernetes with Ingress controller support and ConfigMap management. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

With over 28,600 GitHub stars and 450 contributors, Grafana Loki is the log aggregation system that takes the Prometheus approach to logging — indexing only metadata labels instead of full log content, making it dramatically cheaper and simpler to operate than traditional log management platforms. The label-based indexing strategy groups log streams using the same labels already applied to Prometheus metrics, enabling seamless switching between metrics and logs in Grafana dashboards without maintaining separate indexing infrastructure. Grafana Alloy, the telemetry collector replacing Promtail, scrapes and pushes logs with Prometheus-style service discovery, automatic Kubernetes Pod label extraction, and pipeline stages for parsing, filtering, and relabeling before ingestion. LogQL, the query language, combines label matchers for stream selection with regex line filters and aggregation functions, supporting rate calculations, pattern parsing, and metric generation from log data for alerting and dashboard panels. The storage architecture writes compressed log chunks and TSDB indexes to S3, GCS, Azure Blob Storage, or MinIO-compatible object stores, with configurable retention and compaction policies. Deployment modes scale from a single binary for development through monolithic high-availability mode with multiple replicas to full microservices decomposition with separate ingester, distributor, querier, query-frontend, compactor, and ruler components on Kubernetes via Helm charts. Multi-tenancy isolates data and query paths per tenant through header-based tenant ID assignment. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

Instead of running an Elasticsearch or Loki cluster, VictoriaLogs stores and searches your logs from a single zero-config server. Ship from Filebeat, Fluent Bit, Fluentd, Logstash, Vector, Promtail, Telegraf, the OpenTelemetry Collector, or the Datadog Agent. Syslog, journald, and Splunk endpoints work too. Query with LogsQL: word, phrase, and prefix filters combine with pipes that extract fields from raw lines and compute stats, top values, and facets. See results as a table, grouped by stream, as raw JSON, or as a live tail. Chart them in Grafana, or work from a terminal with vlogscli and pipe output into grep, less, or jq. Events with hundreds of fields are supported. So are high-cardinality fields like trace_id and user_id. Late, out-of-order backfills are accepted. During an incident, stream_context pulls the lines around any match. Tenants are keyed by AccountID and ProjectID headers. vmalert evaluates LogsQL queries as alerting and recording rules. CEF messages sent over Syslog are parsed automatically for SIEM work. Retention defaults to 7 days and can be capped by disk space. Per-day partitions snapshot and copy out with rsync or rclone. Cluster mode splits ingestion, querying, and storage across nodes for logs that outgrow one server. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.

A year of searchable logs and traces no longer needs a metered logging service, because Rootprint keeps the whole index in a storage bucket you already own. A search matches any word on any line, since every log is indexed in full and no service name, stream, or label is needed first. A frequency histogram, one-click field filters, and saved views narrow the results, a fold mode collapses repeated lines, and exports arrive as NDJSON, CSV, or plain text, or as a share link. Open a trace from the log that emitted it, or paste a trace ID, and a waterfall shows per-span timing, attributes, events, and database calls grouped by query, with each span linking back to its logs. The trace explorer charts span volume, error rate, and p50, p95, and p99 latency, ranks the busiest operations, and filters spans by service, duration, and status. Every service gets its own page with request rate, error rate, operations, dependencies, and errors. Data arrives over OpenTelemetry from the Collector, Vector, or Fluent Bit, through a plain HTTP gateway, or by pulling from Kafka, Kinesis, or SQS, with guided setup for nine integrations. Indexes can live in S3, Google Cloud Storage, R2, MinIO, or Azure Blob. Teammates sign in with Google, GitHub, or any OpenID Connect provider, and scoped API keys serve scripts. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

Monitoring everything from network switches to Kubernetes clusters since 2001 with over 6,200 GitHub stars and deployments exceeding 100,000 devices per installation, Zabbix has established itself as one of the most mature and feature-rich open-source monitoring platforms available, trusted by organizations including Dell, Salesforce, ICANN, and T-Mobile. The platform collects metrics from virtually any source using Zabbix Agent written in C, Zabbix Agent 2 written in Go with native plugin support, SNMP v1/v2c/v3 polling and trapping, IPMI for hardware health, JMX for Java applications, SSH and Telnet checks, HTTP/HTTPS polling, and ODBC database queries. Version 7.0 LTS introduced synthetic browser monitoring that executes user-defined JavaScript via WebDriver to simulate multi-step user interactions on websites, proxy load balancing with automatic host redistribution across proxy groups for high availability, in-memory proxy data buffering delivering up to 100x performance improvement, native multi-factor authentication with TOTP and Duo support, and just-in-time user provisioning from SAML and LDAP. Low-level discovery automatically detects file systems, network interfaces, SNMP OIDs, VMware resources, and Kubernetes pods, creating monitoring items and triggers dynamically. The alerting engine correlates events with configurable escalation chains, sending notifications through Slack, Microsoft Teams, PagerDuty, Jira, email, and SMS with customizable message templates. Over 1,000 official templates provide instant monitoring for Linux, Windows, VMware, AWS, Azure, Docker, PostgreSQL, MySQL, Apache, Nginx, and hundreds more. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.