DevTrack turns your GitHub activity into a living dashboard of commit streaks, pull-request analytics, and goals that quietly update themselves. Sign in with GitHub and your last year appears as a contribution heatmap you can filter by repository and language, next to the streak you are protecting and the freeze you are saving for a holiday. Pull requests get the honest treatment: merge rate, average review time, and open-versus-closed breakdowns that show whether reviews or authors are the bottleneck. Weekly and monthly goals fill themselves in from live activity, while the consistency score and activity rings tell you whether your rhythm is steady or heroic but rare. When you want the story told back to you, an AI mentor who knows your history offers guidance, a résumé generator builds an ATS-friendly CV from merged pull requests for the role you name, and a weekly summary explains your own graphs in plain words. Opt into the public leaderboard to rank your streak, commits, and PRs against other developers, or keep everything private and share only the profile page and embeddable badges you choose. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

GitHub forgets your traffic after fourteen days; ghstats remembers it for years. This self-hosted dashboard polls the GitHub API every hour and keeps views, clones, stars, forks, issues, and pull requests for every repository in its own database, long after the built-in traffic tab has rolled over. Open a repository to see its charts for stars, clones, and views next to top referrers and popular paths, filter the list by owner, name, forks, or archived status, and include private repositories when your token allows it. Public stats pages and portfolios reuse the same numbers through a JSON API, embeddable SVG badges for views, clones, and star velocity, and cards ranking your top repositories or showing star history across projects. Release download counts accumulate as daily snapshots too, so a launch spike stays visible months later. The whole service is one small Docker image on port 8080 with a single file for storage, easy to back up and easy to move. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Its author, a student and WakaTime fan, didn't want to pay $9/month for data about his own keystrokes - so Wakapi was born, a self-hosted, WakaTime-compatible backend for coding statistics. The compatibility is the killer design decision: the official WakaTime plugins for VS Code, JetBrains IDEs, Vim, and dozens of other editors work unmodified - just point the plugin's API URL at your Wakapi instance with your personal key, and heartbeats flow to your server instead of a third party's. Duration inference matches WakaTime's own algorithm, with a configurable timeout (10 minutes by default). From that stream Wakapi builds statistics and plots across projects, languages, editors, hosts, and operating systems, plus the fun extras: public leaderboards (optionally login-gated, with configurable aggregation windows), badges for GitHub readmes, and weekly email reports. A REST API serves your data programmatically, Prometheus export feeds your existing Grafana, and a WakaTime relay mode can mirror heartbeats to both services during migration - with one-click import of historical WakaTime data. Written in Go, it is lightning fast and light enough for the smallest instance, storing to SQLite, PostgreSQL, or MySQL, with configurable data retention for GDPR peace of mind. Deliberately smaller than WakaTime, deliberately yours.

Ziit tracks where your coding hours actually go and shows the answer in a dashboard you host yourself. Install the extension in VS Code, any of its forks, or a JetBrains IDE, and every editing session streams to your instance with its project, language, editor, file, branch, and operating system attached. The dashboard filters by time range and breaks your work down by project and language, so a week of effort becomes a readable chart instead of a vague memory. You sign in with GitHub or email and password, import existing history from WakaTime or a WakAPI instance, and keep building on years of data instead of starting over. README badges embed live coding time for a project into any repository page, turning your profile into a quiet proof of work. A public stats page summarizes the whole instance, and a public leaderboard ranks who logged the most coding hours, which makes self-hosted instances double as community scoreboards. Because the server, the database, and the extensions are all yours, no third-party service sees which files you opened or how long a private project took. Teams can run one instance for everyone and compare effort across projects with the same filters. 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.

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.

BI tools, customer-facing apps, and AI agents all receive the same governed answer from Cube Core, an open-source semantic layer where every metric is defined once, in code. Models are written in YAML or JavaScript. Cubes describe business entities with measures, dimensions, and joins. Views curate datasets for consumers. Git holds every version, with code review and CI like any other codebase. Queries arrive through a Postgres-compatible SQL API with a MEASURE function, through REST returning JSON, or through GraphQL, which means any tool that speaks Postgres connects without custom drivers. Sources include Snowflake, BigQuery, Databricks, Amazon Athena, Presto, and Postgres. Pre-aggregations declared in the model refresh in the background and live in Cube Store. An aggregate-aware planner routes matching queries to those rollups, and dashboards answer in milliseconds without rescanning the warehouse. Access policies set member-level visibility, row-level filters, and data masking from the security context. Multi-tenant SaaS analytics becomes practical: each customer sees only their own rows, whichever API asks. A development-mode Playground, meant for local use and never for public exposure, composes queries from measures and dimensions, shows the generated SQL, charts the results, and copies ready-made SQL, REST, and GraphQL calls. 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 most widely deployed open-source BI tool, Metabase is a visualization and query layer that sits on top of your existing databases without ingesting or copying data. Non-technical users ask questions through a visual query builder with drill-through menus that answer follow-ups like "broken down by month" without writing a new query, while analysts use the native SQL editor with variables and templates for complex work. Questions assemble into interactive dashboards with filters, auto-refresh, fullscreen mode, and custom click behavior, and dashboard subscriptions email or Slack scheduled reports to stakeholders. It connects to 20+ data sources including PostgreSQL, MySQL, MongoDB, SQL Server, BigQuery, Snowflake, Redshift, and ClickHouse - always querying in place, so there is no second data store to secure, sync, or pay for, and results are always current. Models and metrics let a data team define official, reusable starting points so self-service stays consistent, collections with permissions organize content, and alerts fire when a metric crosses a threshold. The practical effect is cutting the ad-hoc query queue that lands on the data team, since non-technical staff can answer their own questions. Written in Clojure, licensed AGPL, and shipped as a single JAR or Docker image with an embedded application database - a working BI instance runs before most tools finish their installer - the open-source edition has no limits on users, dashboards, or connected databases, where commercial BI platforms price per viewer as well as per creator.

Chartbrew transforms raw database queries and API responses into polished, shareable dashboards without requiring a data engineering team. Connect to MySQL, PostgreSQL, MongoDB, Firestore, or any REST API, then build datasets using the visual query editor with syntax highlighting and auto-completion. Charts render through Chart.js with line, bar, pie, donut, radar, polar, KPI card, and table visualizations, each customizable with colors, legends, filters, and goal indicators. An AI assistant accelerates dashboard creation by generating queries and suggesting chart configurations from natural language descriptions of the metrics you want to track. Reusable datasets let teams prepare data transformations once and share them across multiple charts, while automatic scheduling through BullMQ and Redis refreshes data at intervals from every 10 minutes to monthly. The embed feature generates standalone chart URLs for insertion into external websites, internal tools, or customer portals, and the Reporting API enables programmatic dashboard management and automated report delivery. Team workspaces with role-based permissions control who can view, edit, or manage data connections. Nearly 4,000 GitHub stars reflect steady community adoption. The platform runs on Node.js with Express and Sequelize ORM, supporting MySQL or PostgreSQL as its application database. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. FSL-1.1-MIT licensed (converts to MIT two years after each release).

Your dashboards deserve version control, code review, and reproducible builds, just like the rest of your stack. DAC lets you define interactive data dashboards in YAML or TSX, validate them in CI, and serve them from a single Go binary that embeds a full React frontend. Choose from 21 chart types including line, bar, area, pie, scatter, bubble, funnel, sankey, heatmap, calendar, sparkline, waterfall, gauge, treemap, radar, and candlestick, plus metric cards, data tables, text blocks, and image widgets. The built-in semantic layer lets you define metrics and dimensions once in reusable model files, then reference them from any widget while DAC generates the SQL automatically. Connect to Postgres, MySQL, Snowflake, BigQuery, Redshift, Databricks, and DuckDB through standard Bruin connection configs. Interactive filters with date pickers, dropdowns, multiselects, and search inputs inject values via Jinja templating and re-execute only affected widgets. Live reload via Server-Sent Events refreshes connected browsers instantly when you save a file. Export dashboards as self-contained static HTML with baked-in query results for S3, GitHub Pages, or offline sharing, and render slide decks via the Google Slides export command. On RepoCloud, deploy DAC on a dedicated VPS with root SSH access and persistent storage for your dashboard definitions and database connections under the AGPL-3.0 license.

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.

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.

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.

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.

Retrospectives, sprint poker, check-ins, and standups all get a home in Parabol, a meeting workspace that walks agile teams through each ritual one stage at a time and keeps every outcome in one place. A retrospective flows from icebreaker to reflect, group, vote, and discuss, with anonymous reflection cards that stay hidden until everyone is done writing, then cluster into themes before dot voting decides what deserves airtime; Start/Stop/Continue, 4Ls, and Mad/Sad/Glad templates are ready to use or customize. Sprint Poker estimates stories on Fibonacci, T-shirt, or custom scales, pulling backlog items from Jira Cloud, GitHub, GitLab, or Linear and pushing the agreed estimate back. Check-ins work through agenda items and task updates, while standups gather written answers on a recurring schedule so distributed teams stay current across time zones. A facilitator timer sets deadlines for asynchronous contributions, with optional Slack or Mattermost notifications to the team. Action items land on a shared task board with active, stuck, done, and future columns, and Pages hold real-time collaborative notes next to the meetings that produced them. Every meeting closes with a summary of attendees, discussion, and next steps that can be emailed to the team. 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 37,000 GitHub stars and used by teams at Y Combinator, Airbus, and Phantom, PostHog replaces an entire stack of paid analytics tools — Mixpanel, Amplitude, Heap, LaunchDarkly, Hotjar, and Google Analytics — with a single open-source platform where every tool shares a common event layer and user context. Product analytics captures events automatically or via manual instrumentation with HogQL (SQL) access for custom queries, while web analytics provides GA-like dashboards for traffic, conversions, and Core Web Vitals. Session replay records user interactions with DOM snapshots and network waterfall analysis, linking directly to errors and feature flag exposures. Feature flags safely roll out changes to specific cohorts with multivariate support and instant rollback, while experiments run A/B tests with automatic Bayesian significance calculations and revenue attribution. Error tracking captures stack traces linked to session replays and user properties for immediate reproduction context. AI observability monitors LLM generations, traces, token usage, latency, and costs across model versions. The managed data warehouse syncs 120+ external sources including Stripe, Postgres, Salesforce, and HubSpot alongside product events, queryable through a unified SQL editor. An MCP server enables AI agents in Cursor, Claude Code, or VS Code to query analytics and execute SQL directly. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.