Open Source Grafana Cloud Alternatives

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.

VictoriaMetrics stores and queries time series data, most often as long-term storage for Prometheus or as a drop-in replacement for Prometheus and Graphite behind Grafana. It is optimized for the awkward case where old series are constantly retired and replaced by new ones at a high rate.

Two deployment shapes are open source under Apache 2.0: a single node and a cluster version. The single node has no dependencies, ships as one small binary, is configured through command-line flags, and the project argues it can stand in for medium-sized clusters built on Thanos, M3DB, Cortex, InfluxDB or TimescaleDB.

Ingestion and querying accept a wide spread of formats.

• Query languages: PromQL, and MetricsQL, the project's own faster dialect of it.
• Ingestion protocols: Prometheus remote write and exposition format, InfluxDB line protocol, Graphite, OpenTSDB, OpenTelemetry, DataDog, NewRelic, JSON, CSV and a native binary format.
• Stream aggregation: aggregate as data arrives, which lets it take the place of StatsD.
• Global query view: many Prometheus instances write into one store and are read with a single query.
• Operations: instant snapshots for backup and restore, metrics relabeling, a cardinality limiter and NFS-based storage such as Amazon EFS.

Published benchmarks claim up to seven times less RAM than Prometheus, Thanos or Cortex, and heavier compression than TimescaleDB, with Grammarly, Roblox, Wix and Spotify among the case studies. An enterprise edition adds anomaly detection, downsampling, multiple retentions and backup automation.

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.

Maple collects traces, logs, and metrics through OpenTelemetry and stores them in ClickHouse. The point is a single place to look when a request goes wrong, without the instrumentation being tied to a vendor that owns the query language it is read with.

The repository is a Bun monorepo. A TanStack Router single-page application serves the dashboard, an Effect-based HTTP API sits behind it carrying an MCP server and AI triage, a separate OTLP ingest gateway handles key authentication and organization enrichment before forwarding to a collector, and an alerting worker evaluates rules on a cron schedule. Shared packages hold the HTTP contracts, the query engine, and UI primitives, while application state lives in PostgreSQL.

The choices to make up front are how it runs and how people sign in.

• Local binary: a Homebrew install and one start command bring up OTLP ingest, embedded ClickHouse, and the dashboard together.
• Docker Compose: the multi-service stack of API, web, ingest, and OTEL collector on local ports.
• Cloudflare Workers: deployment through Alchemy, with PlanetScale Postgres reached over Hyperdrive.
• Clerk auth: organizations come from a Clerk application, with optional networkless token verification.
• Self-hosted auth: a single root password issues bearer sessions valid for twelve hours, capped at seven days from sign-in.

Ingest keys are managed per organization and can be rerolled independently, with private keys encrypted at rest and looked up through non-reversible HMAC hashes. Bun 1.3 or later is the only prerequisite for the code, and a native SwiftUI iOS client lives in the same repository.

Running Loki, Prometheus, Tempo and Pyroscope separately means four storage layers and four operational stories. Gigapipe, formerly known as qryn, implements all four sets of APIs itself, so logs, metrics, traces and profiles land in one lightweight stack that existing clients already know how to talk to.

Ingestion is deliberately promiscuous. OpenTelemetry is officially integrated and needs no changes to your instrumentation, while native ingestion also covers Loki, Prometheus, Tempo, Zipkin, Pyroscope, Influx, Datadog and Elastic. Storage sits on ClickHouse, DuckDB or GigAPI with S3 object storage, so the data stays under your control.

Querying works through the languages people already use.

• LogQL: the Loki API is implemented, so any Loki client or the Grafana Loki datasource works with no plugin.
• PromQL: Prometheus API compatibility is provided using WASM.
• TraceQL: Tempo query support covers traces alongside the other signals.
• Profiling API: profiling endpoints export DOT format for Graphviz, and OTLP profiles are ingested through a development endpoint.
• Index stats and tail: stream metadata queries return stream, entry, byte and chunk counts, and live tail takes limit and start parameters and reports dropped entries.

A built-in explorer and a CLI cover people without Grafana, and an OTLP over gRPC receiver takes traces, logs, metrics and profiles. Configuration, a Docker quickstart and cross-cluster setup are documented in the repository. Gigapipe presents itself as the community-powered alternative to vendor-controlled observability stacks, with a hosted free trial for anyone who wants to test before installing. Contributions require a CLA.