Open Source Dynatrace Alternatives

Grafana lets you query, visualize, alert on and understand your metrics no matter where they are stored. Dashboards are made to be created, explored and shared with a team, which is how a data-driven habit spreads beyond the person who built the first panel.

Panels come from plugins, so the ways to show metrics and logs keep growing, and template variables turn a dashboard into a reusable one with dropdowns at the top. A data source is chosen per query, so a single graph can mix sources, custom ones included.

The daily surface is compact.

• Visualizations: fast client-side graphs with many options, extended by panel plugins.
• Dynamic dashboards: template variables that make one dashboard serve many teams or environments.
• Explore metrics: ad-hoc queries and drilldown, with split view comparing time ranges, queries and sources side by side.
• Explore logs: switch from metrics to logs with label filters preserved, then search or stream them live.
• Alerting: define rules visually and let Grafana evaluate them continuously, notifying Slack, PagerDuty, VictorOps or OpsGenie.

Installation guides cover the usual platforms, and a public play instance lets you try it before installing anything. Grafana is distributed under AGPL-3.0-only, with Apache-2.0 exceptions documented separately, and the contributor path includes a developer guide, a style guide and beginner-friendly issues.

Netdata came out of a failure nobody could explain, where cloud transactions broke silently and no monitoring tool had the resolution to show why. It collects every metric every second and discovers whatever runs on a node by itself, so setup is close to nothing.

The architecture keeps processing at the edge. The Agent handles collection, storage, machine learning, alerts and exports on each machine, while Netdata Cloud adds user management, roles and centralised alerting without pulling metrics into a central store.

A handful of design choices separate it from scrape-and-store metric stacks.

• Per-second collection: data lands and renders immediately rather than at a scrape interval.
• Anomaly detection: multiple unsupervised models are trained per metric on the node itself.
• Tiered storage: roughly half a byte per sample keeps long retention affordable on disk.
• Interactive dashboards: slice and filter the data without learning a query language first.
• Parent-child scaling: nodes centralise into parents handling millions of samples per second.

Coverage spans system resources, storage, network, sensors, processes, logs, containers, virtual machines and synthetic checks, fullest on Linux with FreeBSD, macOS and Windows supported to varying degrees. A University of Amsterdam study rated it the most energy-efficient tool for monitoring Docker-based systems.

Prometheus collects metrics from configured targets at given intervals, evaluates rule expressions against them, displays the results and triggers alerts when conditions are observed. It is a Cloud Native Computing Foundation project and a common base layer for infrastructure monitoring.

What sets it apart is a multi-dimensional data model, where a time series is identified by a metric name plus a set of key and value dimensions. PromQL exists to exploit that dimensionality, letting you slice series along labels instead of querying flat names.

Several architectural decisions shape how it gets deployed.

• Pull model: the server scrapes targets over HTTP rather than waiting for them to report.
• Autonomous nodes: a single server needs no distributed storage to stay useful.
• Service discovery: targets arrive from discovery mechanisms or from static configuration.
• Push gateway: batch jobs too short-lived to scrape push through an intermediary instead.
• Federation: hierarchical and horizontal federation links servers into larger topologies.

Installation runs from precompiled binaries, which the project recommends, or Docker images on Quay and Docker Hub. Building from source needs Go, NodeJS and npm, and the Makefile carries targets for building, testing, formatting and compiling the React interface, producing both the server and the promtool binary.

Most teams end up with a metrics system, a log store and a tracing tool that know nothing about each other, so debugging means carrying a timestamp between three browser tabs. SigNoz puts logs, metrics, traces, alerts, dashboards and exceptions in one product, so context travels with you instead of being reconstructed by hand.

It is built on OpenTelemetry, which means you instrument once with an open standard and keep ownership of the telemetry. Storage is a single columnar database chosen for high-cardinality, high-volume workloads, and queries can be written through a visual builder, PromQL or ClickHouse SQL.

The monitoring surfaces it ships with cover most of what a production team watches.

• APM: service latency, error rate, throughput, Apdex, top endpoints, database calls and external calls.
• Log management: ingest, search, aggregate and correlate logs against traces and metrics.
• Infrastructure monitoring: Kubernetes clusters, pods, nodes and workloads alongside host CPU, memory, disk and network.
• Distributed tracing: flamegraphs, waterfalls, span events and trace analytics, plus funnels that expose drop-offs in a request flow.
• LLM observability: traces for LLM apps and RAG pipelines covering prompts, tool calls, tokens, latency and cost.

The community edition is free and runs on your own infrastructure through Docker, Kubernetes or Linux, with managed cloud and enterprise tiers for teams that need compliance, RBAC, custom retention and support. An MCP server carries telemetry into coding agents for teams working that way.

HyperDX helps engineers work out why production broke by making logs and traces searchable on top of any ClickHouse cluster. Think of it as Kibana pointed at ClickHouse, and it reads whatever schema is already there instead of demanding a new one.

It is a core component of ClickStack, which bundles ClickHouse, HyperDX, an OpenTelemetry collector, and MongoDB. One Docker command brings the whole set up and serves the interface on port 8080, while teams with an existing ClickHouse instance or a ClickHouse Cloud account can point HyperDX at that instead.

Searching is built for people who do not want to write SQL under pressure.

• Property search: full-text search alongside a short property syntax such as level:err, with SQL optional.
• Correlated signals: logs, metrics, session replays, and traces are searched and viewed in one place.
• Live tail: logs and traces stream in, so the freshest events are always on screen.
• Event deltas: anomalies are compared against normal traffic to show what actually changed.
• Alerts and dashboards: alerts take a few clicks, and high cardinality events chart without a query language.
• Terminal client: an hdx CLI offers a TUI with vim-style keys, ANSI charts, NDJSON output, and Drain pattern mining.

Instrumentation goes through OpenTelemetry, covering Kubernetes, JavaScript, Python, Java, Go, Ruby, PHP, .NET, Elixir, and Rust, with the collector listening on port 4318. Testing wants at least 4 GB of RAM and two cores. Code contributions now use a vouching model, where a maintainer sponsors a newcomer before a first pull request is reviewed.

Collecting metrics, logs and traces does not by itself make a system observable. Coroot gathers that data and turns it into findings, so an engineer reads a verdict rather than assembling one from a wall of dashboards.

Collection runs on eBPF, so metrics, logs, traces and profiles arrive with no code changes, including from legacy or third-party services nobody can instrument. The result is a service map covering the whole system, with predefined inspections auditing each application without configuration. Log search is backed by ClickHouse, and tracing stays vendor neutral through OpenTelemetry.

Coroot organizes what it finds into a few working views.

• Health summary: shows the status of hundreds of services at once and tracks service level objectives.
• Distributed tracing: opens any outlier request in a single click for investigation.
• Log patterns: cluster events out of the box and correlate logs with the traces behind them.
• Continuous profiling: attributes a CPU or memory spike to the precise line of code, compared against baseline behavior.
• Deployment tracking: discovers every rollout in a Kubernetes cluster and compares each release with the previous one, with no CI/CD integration.
• Cost monitoring: attributes AWS, GCP and Azure spend down to a specific application without access to your cloud account.

The project states that its built-in inspections identify over 80 percent of issues automatically, and an application missing its objectives triggers one alert carrying every relevant inspection. Coroot runs as a Docker container or in any Kubernetes cluster under the Apache 2.0 license, with an enterprise edition alongside.

Uptrace is an open source APM covering distributed tracing, metrics and logs behind a single interface. Instead of running a separate system for each signal, you send everything through OpenTelemetry and query it from one place, with a query builder, dashboards and alerting on top.

Storage is ClickHouse, with PostgreSQL holding metadata such as metric names and alert definitions. That pairing is where the cost argument comes from: a one kilobyte span compresses to roughly forty bytes on disk, a single core processes more than ten thousand spans per second, and one server can carry billions of spans and metrics.

Querying and alerting are built around shapes engineers already know.

• SQL-like span queries: spans and logs are aggregated with a language close to SQL.
• PromQL-like metric queries: metrics use a syntax Prometheus users will recognize.
• Prebuilt dashboards: more than fifty dashboards appear automatically once matching metrics start arriving.
• Alerting: rules over spans, logs and metrics notify by email, Slack, webhook or AlertManager.
• Grafana compatibility: Uptrace can be configured as a Tempo or Prometheus data source.

Ingestion accepts OpenTelemetry, Prometheus, Vector, FluentBit and CloudWatch among others, and a service graph plus chart annotations tie deploys to what the charts show. Users and projects are managed from a YAML config, and single sign-on works over OpenID Connect with Keycloak, Google Cloud and Cloudflare. A cloud demo needs no login, and a Docker example gets a local instance running quickly.