Website analytics, uptime monitoring, and server status - three tools most teams run separately - combined in Tianji, an open-source observability platform. The analytics layer tracks page views, unique visitors, referrers, and UTM parameters with a lightweight cookie-less script, which keeps collection GDPR and CCPA friendly. The uptime monitor checks availability and latency on configurable intervals, accepts passively reported results, and publishes public status pages for incident communication. Server status agents report CPU, memory, disk, and network metrics with threshold-based alerts, and notifications route through webhooks, Slack, Telegram, and other channels with noise control. It also includes anonymous telemetry for tracking deployments of your own open-source projects, surveys, waitlists, team collaboration, and an OpenAPI interface for integrations and exports. The consolidation is the point: traffic analytics, uptime checks, and server metrics share one interface and one alerting layer, so diagnosing an incident does not mean hopping between Google Analytics, Uptime Kuma, and Prometheus - and the built-in public status pages replace a separate paid Statuspage-style subscription. Because collection uses no cookies with IP truncation and aggregation by default, basic traffic measurement requires no consent banner. Built in TypeScript under the Apache 2.0 license and inspired by Umami and Uptime Kuma, it is deliberately right-sized for independent developers and small SaaS teams whose monitoring needs are real but lightweight.

Trusted by over 60,000 organizations worldwide with more than 8,100 GitHub stars since 2010, Graylog has established itself as one of the fastest paths from raw log data to operational visibility, delivering centralized log management, security analytics, and compliance auditing through a purpose-built web interface with sub-second search at scale. The platform ingests logs from virtually any source via syslog, GELF, Beats, raw TCP/UDP, HTTP, CEF, IPFIX, and Netflow protocols, processing each message through configurable pipelines that parse fields, apply transformations, enrich events with GeoIP data from MaxMind or IPinfo lookup tables, and route messages to appropriate streams based on content rules. OpenSearch handles full-text indexing and storage with dynamic shard sizing that automatically calculates appropriate sizes from available node memory, while MongoDB stores configuration metadata including user accounts, roles, dashboards, alert rules, and pipeline definitions. The alerting system integrates with Slack, PagerDuty, and email with customizable notification templates and Replay Search links for immediate investigation context. Version 7.0 introduced MCP server integration for connecting preferred LLMs to perform AI-assisted log analysis and automation, while version 7.1 added Sigma detection rule import from private GitHub, GitLab, and Bitbucket repositories for detection-as-code workflows. The Sidecar agent management system centrally configures and deploys Filebeat, Winlogbeat, and nxlog collectors across infrastructure from the Graylog web interface. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. SSPL licensed.

Exceptionless has earned over 2,400 GitHub stars and has been processing production errors since 2014 as the real-time event monitoring platform that captures far more than crashes. Built with ASP.NET Core on Elasticsearch for storage and Redis for caching, Exceptionless ingests exceptions, log messages, feature usage events, broken links, and custom event types through official SDKs for JavaScript, Node.js, .NET Core, ASP.NET, WPF, Web API, WebForms, Console apps, and React Native. Automatic event stacking groups related occurrences by exception type, message, and call stack into single actionable items, while manual stacking keys let developers create custom groupings for specific features or workflows. The real-time dashboard displays Most Frequent, Most Recent, and New event views with filtering by project, date range, environment, and custom tags. Stack management tracks resolution status with version-aware regression detection that automatically reopens resolved issues when the same error surfaces in a newer release. Webhook integrations connect to Slack, Discord, and external services through Zapier for automated issue tracking in GitHub Issues and Jira. Per-project notification settings control email and chat alerts for new errors, regressions, and critical events. OpenTelemetry support captures distributed traces alongside error data. The v8.6.0 release introduced a hosted Model Context Protocol server at the /mcp endpoint, enabling AI tools to query error data via OAuth-authenticated access. Deploy via Docker with the exceptionless/exceptionless image alongside Elasticsearch and Redis. 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.

Bugsink has earned over 1,800 GitHub stars as the lightweight self-hosted error tracking platform that replaces Sentry without per-event billing by accepting error reports from any Sentry-compatible SDK across Python, JavaScript, Node.js, Ruby, Java, PHP, Go, and every other language Sentry supports. Simply update the DSN in your existing Sentry configuration and Bugsink captures the same stack traces, local variables, request context, and breadcrumbs that Sentry processes, displayed through a focused interface designed for debugging rather than dashboarding. Automatic issue grouping collapses duplicate error events into single actionable issues based on exception type, message, and stack frame context, turning thousands of raw events into a manageable list. Issue status tracking supports resolved, resolved-in-next-release, and muted states with automatic regression detection when resolved issues recur. Release tracking associates events with deploy versions to correlate error spikes with specific rollouts. Tag-based search filters issues by environment, release, user, browser, operating system, or any custom key-value pair sent by the SDK. Alerting notifies your team through Slack, Discord, Mattermost, and email when new issues appear or resolved issues regress, with per-project webhook configuration and user-level notification preferences. Per-project retention policies with automatic event eviction manage storage growth. A REST API with OpenAPI documentation enables custom integrations and dashboards. Source map support links minified JavaScript to original source. Deploy via Docker with the bugsink/bugsink image. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. PolyForm Shield licensed.

With nearly 16,000 GitHub stars, 5.7 million monthly PyPI downloads, and 400+ contributors, Dagster is the most widely adopted asset-centric data orchestration platform — replacing task-oriented schedulers like Apache Airflow with a declarative model where every pipeline is defined as Python functions producing data assets such as tables, datasets, machine learning models, and reports. The built-in asset graph provides automatic lineage tracking across your entire data platform, showing exactly how data flows from ingestion through transformation to downstream consumption in a single unified view. Declarative Automation goes beyond cron scheduling with event-driven conditions that intelligently trigger materializations based on upstream freshness, data quality signals, and dependency state. The integrated data catalog auto-generates documentation from asset metadata, ensuring it never drifts out of sync with production. Native first-class integrations connect dbt, Snowflake, BigQuery, Databricks, Fivetran, Airbyte, Spark, Great Expectations, Tableau, Power BI, AWS, GCP, and Azure without custom glue code. The web UI visualizes asset graphs, run history, schedules, sensors, and partitioned materializations with built-in alerting via Slack and PagerDuty. Dagster Pipes enables executing arbitrary code in external environments including Spark clusters, Kubernetes Jobs, and cloud functions. Deploy via Docker Compose on a single VM with separate containers for the webserver, daemon, and code locations, or use official Helm charts for production Kubernetes with K8sRunLauncher scaling each run as an independent Job. 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 Cal.com, WhiteBIT, and Documenso and backed by 8,800+ GitHub stars, OpenStatus delivers the open-source status page and uptime monitoring platform that replaces Atlassian Statuspage, Better Stack, and Instatus with a single self-hosted deployment. The monitoring engine runs Go-based probes across 28 global regions on three cloud providers checking HTTP, TCP, and DNS endpoints in parallel with configurable intervals and multi-region consensus to eliminate false-positive alerts. Status pages ship with custom domain support, password and email-domain access controls, maintenance windows, grouped monitor components, and subscriber notifications via email and RSS. Incident management provides structured status reports with investigating, identified, monitoring, and resolved timeline phases that publish automatically to affected status pages. The monitoring-as-code workflow supports YAML configuration synced through the CLI, a GitHub Actions integration for CI/CD pipelines, and a Terraform provider for infrastructure-as-code deployments. The typed ConnectRPC JSON-over-HTTP API exposes a published OpenAPI specification with a Node SDK for programmatic access, while an MCP server connects AI assistants like Claude, ChatGPT, and Cursor directly to workspace data. The tech stack combines Next.js with shadcn/ui for the dashboard, Hono for the API server, Drizzle ORM over Turso for application data, and Tinybird for analytics. Private monitoring locations deploy as a single 8.5MB Docker image behind firewalls to check internal services. 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.

Maintenant replaces three to five separate monitoring tools with a single Go binary that consolidates container discovery, endpoint monitoring, SSL tracking, resource metrics, and public status pages without requiring any external database. The embedded Vue 3 frontend serves on port 8080 immediately after deployment, auto-discovering Docker containers and Kubernetes pods through direct socket and API access without configuration. HTTP and TCP endpoint monitoring validates availability with configurable intervals, while TLS certificate tracking alerts before expiration across all monitored domains. Resource metrics collect CPU, RAM, network throughput, and disk usage per container with real-time Server-Sent Events streaming to the dashboard. Heartbeat and cron monitoring accepts pings from external scheduled jobs, triggering alerts on missed check-ins via webhook callbacks and Discord notifications. The built-in alert engine supports escalation rules and notification batching. Public status pages expose component health to end users without authentication, customizable per monitored service. Network security insights analyze exposed ports, container privilege levels, and host configuration to produce a posture score. Update intelligence scans OCI registries to detect available container image updates with digest comparison. The REST API with SSE broker enables automation, and the integrated MCP server provides tooling for AI assistant integration. SQLite in WAL mode stores all data with zero operational overhead. 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.

Backed by Y Combinator and trusted by over 2,300 companies processing billions of observations monthly, Langfuse is the most widely adopted open-source platform for building, monitoring, evaluating, and debugging LLM applications. The hierarchical tracing engine captures every LLM call, tool invocation, retrieval step, and agent action as nested spans based on OpenTelemetry, with automatic cost calculation, latency tracking, and token usage attribution across sessions and users. Prompt Management separates prompts from code with versioned artifacts, label-based deployments, one-click rollbacks, and runtime SDK fetching with server-side caching, while linking every generation back to its exact prompt version for attribution analytics. The evaluation system supports LLM-as-a-judge scoring, heuristic code evaluators, user feedback collection, and manual annotation workflows that run automatically on production traces or against curated datasets. The Playground enables interactive prompt testing on real production inputs with side-by-side model comparison across providers. Datasets and Experiments define test cases for systematic benchmarking with comparative result visualization. Native SDKs for Python and TypeScript provide decorator-based instrumentation, while 100+ integrations cover LangChain, LlamaIndex, OpenAI SDK, LiteLLM, Vercel AI SDK, and any OpenTelemetry-instrumented framework. The analytics dashboard surfaces cost breakdowns, quality scores, latency percentiles, and usage trends across models and prompt versions. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Agenta delivers a comprehensive open-source LLMOps workspace that covers the full lifecycle of AI application development — from prompt engineering through production monitoring. The platform supports 15+ model providers including OpenAI, Anthropic, Google Gemini, Mistral, Groq, Together AI, Azure, AWS Bedrock, and self-hosted models via Ollama, enabling teams to switch between providers without code changes. The prompt playground allows side-by-side comparison of different configurations, while the evaluation system offers LLM-as-a-Judge assessment, 20+ pre-built evaluators covering semantic similarity, regex matching, and factual accuracy, plus custom Python evaluators for domain-specific requirements. Teams run evaluations through both the web UI for subject matter experts and the Evaluation SDK for programmatic CI/CD integration. The observability layer captures full trace visibility across complex agentic workflows, flagging quality issues like hallucinations and off-topic responses in real time. Human annotation workflows let domain experts review and annotate LLM outputs, feeding corrections back into the evaluation loop. The architecture supports Chain of Prompts, RAG pipelines, and multi-step agent workflows, integrating with frameworks like LangChain and LlamaIndex. Self-hosting deploys via Docker Compose with Traefik for routing, requiring only a clone, environment configuration, and a single docker compose command. On RepoCloud, deploy Agenta on a dedicated VPS with root SSH access, persistent storage for evaluation datasets and traces, and complete control over model provider credentials, all under the MIT license with no usage restrictions.

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.

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.

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.

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.

BitRouter is a context-aware LLM router that learns which model delivers the cheapest successful outcome per workflow step, cutting agent costs by up to 80% while maintaining 96% quality versus all-frontier baselines. Point any agent runtime at http://localhost:4356 with a one-line OPENAI_BASE_URL change and BitRouter routes to OpenAI, Anthropic, Google, Groq, DeepSeek, Mistral, Moonshot, MiniMax, Nvidia, and any OpenAI-compatible endpoint simultaneously, normalizing authentication, streaming, and cross-protocol translation between wire formats. The act-observe-evaluate-learn loop traces every hop with cost, tokens, and latency attribution, scores each decision against a versioned policy-lock.yaml, then tightens routes automatically with no LLM judge in the path. Native MCP gateway auto-discovers tools from connected servers and makes them routable and governed alongside model calls. Agent Client Protocol integration enables the TUI to manage Claude Code, Codex, OpenCode, OpenClaw, Gemini, and Copilot sessions in real time with inline tool-call approval and live streaming. Built-in guardrails inspect, redact, or block risky content before requests leave your network. Virtual keys scope API access per agent or user without exposing upstream credentials. Per-agent spend caps and loop guards contain runaway cost automatically. Multi-account failover reroutes mid-run so rate limits never re-pay completed work. Ships as a single Rust binary via npm or Cargo. 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 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.

GlitchTip speaks Sentry's protocol without Sentry's operational weight - open-source error tracking that your existing SDKs already understand. The pitch is pragmatic: instrument your application with the official Sentry SDKs you already know - any language they cover - and point the DSN at your own GlitchTip instance instead. Errors, exceptions, log messages, and Content Security Policy violations flow into one place for triage, grouped into issues with stack traces, with alerts delivered by email or webhook the moment things break. Where self-hosted Sentry has ballooned into a docker-compose stack of twenty-plus containers, GlitchTip is a deliberately lean Django and PostgreSQL application a small team can actually run. Beyond errors, it bundles three more monitoring concerns: performance monitoring takes a works-out-of-the-box approach - no dashboard building, just your slowest web requests, database queries, and transactions surfaced automatically; uptime monitoring pings your sites and alerts on failures, or runs in reverse as a dead-man's-switch heartbeat for cron jobs that must check in on schedule; and log search puts application logs alongside errors for faster debugging. Unlimited projects and team members, MIT-licensed, built by Burke Software - your event volume is limited only by your own hardware.