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

Small server, small monitoring. Statlite watches your applications and their host with a single binary that idles at about 15 MiB, so the observer never becomes the heaviest thing on the box. Point it at a Spring Boot Actuator endpoint and it polls health plus a fixed, curated set of Micrometer metrics: request traffic, latency, CPU, and runtime memory, all drawn as clean charts. Quarkus, Micronaut, and anything else that can answer a fixed JSON metrics profile can join the same dashboard. History lives in one local SQLite file, raw samples only, kept for 90 days by default, so yesterday's latency spike is still there when you go looking. The dashboard also watches the host itself: processor load, memory pressure, and filesystem capacity, including a warning before the metrics database fills its own disk. Restart detection tells you when an application bounced, and per-target Basic Auth support lets you poll protected Actuator endpoints safely. Configuration is one YAML file listing your targets and polling interval, friendly to systemd and Docker alike. Everything is deliberately scoped: no alert manager, no external service, no observability platform to operate. Just open the page and see whether your apps are healthy. 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 31,600 GitHub stars and thousands of production deployments, InfluxDB 3 Core is the open-source time series database rebuilt in Rust on the FDAP stack — Apache Flight for high-throughput data transfer, DataFusion for vectorized SQL query execution, Arrow for columnar in-memory representation, and Parquet for compressed columnar storage. The engine delivers sub-10ms query response times on recent data and handles millions of writes per second through line protocol ingestion over HTTP, with unlimited tag cardinality eliminating the high-cardinality limitations that plagued earlier InfluxDB versions. The diskless architecture persists data as compressed Parquet files to S3-compatible object storage, Azure Blob, Google Cloud Storage, or local disk with configurable partitioning strategies, while the write-ahead log and in-memory buffer serve real-time queries against recent data before compaction. Native SQL support through DataFusion includes window functions, CTEs, subqueries, and joins, while InfluxQL maintains backward compatibility with existing InfluxDB 1.x and 2.x applications through the same query API. The embedded Python VM enables processing engine plugins and triggers that execute custom logic on write events, perform cross-database queries, and transform data in real time without external tooling. Flight SQL clients provide high-performance query access from Python, Go, Java, and Rust, and the HTTP API supports writes in line protocol format compatible with Telegraf's 300+ input plugins. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT/Apache 2.0 dual-licensed.

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

Kula watches a Linux server every second and turns CPU, memory, disks, network, containers, and applications into a live dashboard you can open before trouble becomes downtime. Follow total and per-core CPU, load averages, memory and swap pressure, disk I/O, filesystem space, temperatures, GPU load, and network throughput as charts update live in the browser. Drag to zoom into history, switch to focus or TV mode for a wall display, and check the System Info page when you need the hardware story behind a spike. Watch Docker, Podman, and cgroup containers beside host metrics, then add PostgreSQL, MySQL or MariaDB, nginx, and Apache monitoring or define custom metrics for the odd service only your team understands. Scrape the Prometheus endpoint into an existing observability stack, open the terminal UI over SSH for a quick live read, or ask the optional local Ollama assistant to discuss charts without sending data to a cloud service. Protect the dashboard with optional Argon2id logins and multiple users, schedule storage snapshots, and keep the whole tool working on closed networks with no external calls. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

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

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.

Founders do not need another chart of pageviews; they need to know which visit became money, and Talivia is a self-hosted, revenue-first analytics platform that fuses website traffic with payment data so every metric answers in dollars. Connect Stripe, LemonSqueezy, Polar, Dodo, or Yolfi, and the dashboard pairs visitor counts with revenue, conversion rate, revenue per visitor, refunds, and disputes, so a traffic spike that never buys anything stops masquerading as success. Referrers, campaigns, keywords, and countries are ranked by the money they produce, which reduces budget decisions to one question: which channel brought customers who actually paid? When a page underperforms, session replay shows what visitors actually did there and the moment they gave up, so you debug journeys instead of guessing from aggregates. Events and multi-step funnels trace the path from landing page to checkout, while bot traffic detection stops crawlers from inflating your conversion statistics. The live visitor feed shows who is browsing right now, with country, device, browser, acquisition source, events, and spend for each session as it unfolds. Public links publish any dashboard as a read-only page for investors or clients, and teammates join the workspace with their own accounts when they need to dig deeper. Visitors, replays, and payment records all stay in your own PostgreSQL database, so customer journeys never pass through a third-party analytics cloud. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Monitoring and safeguarding Docker infrastructure without heavy enterprise software overhead is the primary mission of Statainer, an open-source management control center for container hosts. System operators inspect live CPU loads, memory consumption, network throughput, and disk I/O metrics across individual containers or comparative top-ranking charts. The interface groups containers by Compose projects, enabling administrators to start, stop, or restart entire application stacks and inspect real-time log streams without opening SSH sessions. The built-in Update Manager detects available image tags, executes single-click container updates, and maintains persistent deployment history with automated rollback capabilities for failed recreations. Custom alert configurations trigger instant notifications to Telegram, Discord, Slack, Pushover, or generic webhook endpoints whenever resource thresholds are breached or container health states change. Developers can provision scoped API keys with token authentication to automate container state transitions and export performance telemetry into custom monitoring workflows. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.