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.

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.

With over 9,000 GitHub stars and a focus on developer experience over legacy monitoring complexity, Highlight.io delivers a unified observability platform that correlates frontend user behavior with backend performance data in a single interface. The session replay engine captures high-fidelity DOM recordings showing exactly what users experienced, including console logs, network requests, page transitions, and user interactions, with configurable privacy redaction for sensitive content. Error monitoring automatically groups and deduplicates errors, surfaces affected user sessions, and provides full stack traces with source map support for minified production code. The logging pipeline ingests structured and unstructured logs from any backend service with automatic property extraction, full-text search, and configurable alerting thresholds. Distributed tracing tracks request flows across microservices with embedded links to associated sessions, errors, and logs for complete request lifecycle visibility. The metrics system collects custom application metrics alongside built-in web vitals and performance data for trend analysis and anomaly detection. Search across all telemetry types uses a unified query language with automatic attribute discovery and saved views for recurring investigations. Integrations connect with Slack, Discord, Linear, Jira, Vercel, and dozens of other developer tools for notification routing and workflow automation. SDKs cover React, Next.js, Vue, Angular, Python, Go, Ruby, Java, PHP, and Elixir with framework-specific instrumentation. Self-hosted deployment runs via Docker Compose with ClickHouse for analytics storage. 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 12,400+ GitHub stars and positioned as the self-hosted alternative to FullStory and Hotjar, OpenReplay delivers the open-source session replay platform that keeps every byte of user behavior data on your own infrastructure. The JavaScript tracker captures pixel-perfect recordings of clicks, scrolls, form inputs, and navigation with automatic sensitive data masking, while simultaneously logging network requests, console errors, JavaScript exceptions, and Redux, VueX, MobX, NgRx, Pinia, and Zustand store state changes for complete technical context. DevTools mode reconstructs each session with full stack traces, GraphQL queries from Apollo and Relay, Fetch and Axios request payloads, CPU and memory metrics, and page speed waterfall charts — effectively giving developers a browser inspector tied to any user session. Product analytics surfaces conversion funnels, user journeys, click heatmaps, web vitals trends, and retention cohorts without requiring custom instrumentation. Co-browsing connects support agents to live user sessions with cursor control and WebRTC audio, enabling real-time assistance without third-party screen-sharing software. Integrations push session context into Sentry, Datadog, CloudWatch, Stackdriver, and Elastic for front-to-back debugging. Feature flags enable gradual rollouts with session-level targeting. The platform deploys to any cloud via Docker and Kubernetes with auto-scaling ingestion handling up to 50,000 sessions per month on the open-source edition. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPLv3 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.

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.

Parseable replaces expensive Elasticsearch clusters and fragmented monitoring stacks with a single Rust binary that ingests, queries, and stores logs, metrics, and traces on commodity object storage at a fraction of the cost. The data lake architecture decouples stateless compute from S3-compatible storage, enabling independent scaling of ingestion throughput and query capacity while cutting storage costs by up to 90% compared to indexed alternatives. OpenTelemetry-native OTLP ingestion accepts telemetry from existing OTel collector pipelines, Prometheus Remote Write endpoints, Kafka consumers, eBPF probes, and popular logging agents including Fluentd, Fluent Bit, and Vector without proprietary format conversions. The SQL-first query interface enables cross-signal analysis across all telemetry types, while native PromQL support with 50+ functions and 12 aggregation operators provides Prometheus-compatible metrics querying that works directly with Grafana dashboards. Built-in features include customizable dashboards, real-time alerting with Webhook, Slack, and Alertmanager targets, role-based access control, OpenID single sign-on integration, LogIQ automatic unstructured-to-structured log transformation, smart caching for frequently accessed data, and retention policies for lifecycle management. AI-powered Keystone Q&A provides natural language to SQL conversion and dataset summarization. All data stored as standard Apache Parquet on object storage remains accessible to any Parquet-compatible engine (DuckDB, Spark, Trino), ensuring zero vendor lock-in. Deploys on AWS S3, Azure Blob, Google Cloud Storage, MinIO, Wasabi, and DigitalOcean Spaces. 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.

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.