Keep is an open-source AIOps and alert management platform built with Python FastAPI and Next.js. It provides a single pane of glass for monitoring alerts from 110+ integrations, alert deduplication, correlation, enrichment, and filtering, YAML-based workflow automation similar to GitHub Actions, AI-powered correlation and summarization, and customizable dashboards for incident management. With 12,100+ GitHub stars, Y Combinator backing, and an Elastic partnership, Keep is the open-source AIOps platform that centralizes alert management across your entire monitoring stack into a single customizable dashboard. Alert deduplication identifies duplicate notifications across providers, correlation groups related alerts into incidents based on rules or AI-powered semantic analysis using pluggable LLM backends supporting OpenAI, Anthropic, and local models via Ollama, and enrichment adds context from external sources like CMDBs and databases. Workflow automation follows a GitHub Actions paradigm with declarative YAML files defining triggers, conditions, and actions that can query MySQL, update Jira tickets, send Slack messages, execute Python scripts, or call REST APIs. Authentication supports no-auth, database, Auth0, Keycloak, OAuth2 Proxy, Okta, and OneLogin. The Common Expression Language enables advanced alert querying, slicing, and rule-based grouping to reduce noise. On RepoCloud, deploy Keep on a dedicated VPS with Docker Compose, root SSH access, and complete control over your alert infrastructure, all under the MIT license.

With 3,900 GitHub stars, 140 contributors, and 380 releases since its 2022 launch, Grafana OnCall delivers developer-friendly incident response that routes alerts from any monitoring system to the right engineer at the right time through the right channel. The platform accepts alerts via unique API URLs from Alertmanager, Grafana Alerting, Zabbix, Datadog, Pagerduty-compatible sources, Jira, inbound email, and generic HTTP webhooks, then applies routing templates to direct each alert to the appropriate escalation chain. Escalation chains define notification sequences — notify the primary on-call via Slack, wait 5 minutes, escalate to SMS and phone, wait 10 minutes, page the secondary on-call and notify the engineering manager — continuing until acknowledgment or resolution. On-call schedules support multi-layer rotations with overrides, shift swaps, and timezone-aware handoffs rendered directly inside Grafana dashboards. ChatOps integration publishes alert groups to Slack channels and Telegram groups with interactive buttons for acknowledge, resolve, and silence actions. Template engines based on Jinja2 control alert grouping, appearance rendering, and behavioral automation. The REST API enables programmatic management of integrations, schedules, and escalation policies. Deploy via Docker Compose with PostgreSQL, Redis, and Celery workers alongside your existing Grafana instance. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. GNU AGPL v3 licensed.

When a query crosses its threshold, Nightingale, a self-hosted alerting engine, decides who hears about it and how, without moving data out of the stores you already run. Rules use PromQL, log queries, or SQL against Prometheus, VictoriaMetrics, Elasticsearch, Loki, ClickHouse, MySQL, or PostgreSQL, graded S1, S2, or S3 with a for-duration that absorbs brief spikes. Existing Prometheus alert rules import from YAML. Business groups give each team its own rules and dashboards, and sign-in works through OIDC, OAuth2, LDAP, or CAS. Once a rule fires, mute rules silence the noise, subscriptions add recipients, and event pipelines rewrite labels or route conditionally. Delivery reaches Slack, PagerDuty, Discord, Telegram, Jira, email, DingTalk, Feishu, WeCom, SMS, and voice calls, plus HTTP webhooks and scripts for anything else. Evaluation records, active and historical events, and notification results show what happened at each step. If an alert misbehaves, the built-in AI agent explains why it fired or never arrived, and it drafts rules, dashboards, and mutes from plain-language requests. Claude Code, Cursor, and other MCP clients can query alerts and manage alerting through /mcp, read-only by default and bound by the caller's own permissions. At remote sites, n9e-edge keeps evaluating and notifying when the link to the center drops. 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.

With 7,400+ GitHub stars and a feature set that replaces seven separate SaaS subscriptions — Pingdom for monitoring, StatusPage.io for status pages, PagerDuty for on-call, Incident.io for incident management, Datadog for APM, Loggly for logs, and Sentry for error tracking — OneUptime delivers every tool your reliability team needs in a single open-source platform that is genuinely 100% open source under Apache 2.0 (not open-core). Uptime monitoring runs synthetic checks against websites, APIs, ports, SSL certificates, and DNS records from distributed global probes with configurable intervals and thresholds. Branded status pages publish automatically when monitors detect issues, notifying subscribers via email, SMS, webhook, or RSS without manual intervention during an outage. On-call scheduling routes alerts through escalation policies to the right engineer via phone call, SMS, push notification, Slack, or Microsoft Teams. The incident management workflow handles declaration, triage, communication, resolution, and post-mortem generation in a unified timeline. APM collects traces and metrics via native OpenTelemetry integration — no proprietary agents required — while log management provides full-text search and alerting. An AI agent continuously monitors telemetry data, identifies root causes, and opens GitHub pull requests with proposed fixes for review. Deploy via Docker Compose or Kubernetes Helm charts with a Terraform provider for infrastructure-as-code configuration. 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.

RocketplaneIO is a self-hosted AI SRE platform that gives Kubernetes clusters zero-instrumentation eBPF observability plus a copilot capable of safely diagnosing and fixing issues without your telemetry ever leaving your infrastructure. Point it at any cluster, and an eBPF DaemonSet starts capturing HTTP, gRPC, SQL, Redis, and Kafka spans across every service, including compiled binaries, with cross-service context propagation and no code changes required. The live service map draws itself from actual network traffic, matching technology logos from container images and coloring each node's health from RED metrics. Every log line sits two clicks from its parent distributed trace, and a PromQL query engine, embedded from the real Prometheus evaluator, runs over ClickHouse for long-term metric retention. The complete Kubernetes inventory (Services, Ingress, ConfigMaps, network policies, persistent volumes, CRDs) syncs continuously and is searchable alongside traces and logs. When the copilot identifies a problem, it picks from a catalog of roughly 30 risk-classified safe actions; each action verifies its preconditions, captures a before-state snapshot, executes, checks the result, and rolls back automatically on failure. Disruptive operations pause for explicit human approval before proceeding. An MCP endpoint exposes the identical guardrailed toolbox to external AI agents, so Claude Code or Cursor can operate the cluster through the same safety boundary the browser copilot uses. Complex remediations compose as searchable, forkable Starlark workflows that compile deterministically at save. 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.

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.

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.

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.

When an alert fires on any of your Prometheus Alertmanagers, Karma shows it once, on one filterable dashboard, and lets you acknowledge or silence it from there. Identical alerts from different servers merge into one entry tagged with every server that saw it, HA pairs included. Groups follow the group_by settings of your Alertmanager routes, and shared labels and annotations collapse into a footer. Multi-grid mode gives each value of a label, or each Alertmanager, cluster, or receiver, its own grid, keeping production and staging apart. Configurable label colors make hosts and severities easy to spot. Optional history draws 24 hourly blocks per group from the Prometheus servers that fired it, and an inhibited alert's muted button lists whatever is suppressing it. One click acknowledges an alert with a short-lived silence, and the separate kthxbye daemon can keep extending it until the alert resolves. The silence modal creates, edits, and expires silences, and ticket IDs in comments can become links. A Dead Man's Switch check raises a visible error when the alerting pipeline goes quiet. Basic auth or a proxy's header identifies each user, silence ACLs stop one team from silencing another's alerts, and read-only mode disables silence management. Default filters open the view your team triages first, and light and dark themes follow the browser. 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.

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.

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.

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.

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.

Teams that cannot send messages through someone else's cloud run Mattermost - the open-core, self-hosted alternative to Slack. It provides public and private channels, threaded discussions, unlimited search history, file sharing with previews, one-to-one audio calls, and screen sharing, with desktop clients for Windows, macOS, and Linux plus iOS and Android apps. Messages support full Markdown, which suits engineering conversations with code blocks and logs. Playbooks turn repeatable processes such as incident response and release management into checklist-driven workflows with automated triggers and retrospectives. Integration is a core strength: prebuilt connectors for GitHub, GitLab, Jira, ServiceNow, and PagerDuty, plus webhooks, slash commands, bots, a REST API, and a plugin marketplace with 700+ entries - together making it a working surface for ChatOps rather than just a chat room. Playbooks add keyword and event triggers, task assignment, status broadcasting, and post-incident retrospectives, so operational knowledge is not trapped in individuals' heads. The server is a single Go binary backed by PostgreSQL, with React clients, released monthly under MIT license and deployable fully air-gapped - which is why governments and defense organizations run it inside closed networks, and why the same control applies to any team with confidentiality requirements. The compiled Team Edition is free for unlimited users with no message history cutoff, so costs stay flat as the team grows.