Grafana stack vs SigNoz

The Grafana stack spreads querying across several languages. Metrics in Prometheus are queried with PromQL, logs in Loki are queried with LogQL, and traces are queried with TraceQL if you run Grafana Tempo or with the query language of whichever tracing backend you pick. Your team therefore learns three query models, each with its own syntax, its own mental model of time series and streams, and its own troubleshooting habits. SigNoz takes a different route. Its dashboards can be built with the Query Builder, with PromQL, or with ClickHouse SQL, so a team can start with a visual builder and move to PromQL or SQL where a query needs more precision. The practical question is which of these your engineers already write fluently. A team that knows PromQL from Prometheus has a head start on the Grafana side, while a team that already knows SQL may find SigNoz's ClickHouse SQL familiar.

The data model drives query cost more than the syntax does. Loki indexes only the labels attached to each log stream, not the contents of the log lines, and it stores compressed chunks in object storage. A query that filters on well-chosen labels reads a small set of streams, while a query that scans text across many streams reads far more data. Label design is therefore a decision you make early, and a poor choice of labels, such as high-cardinality values, shows up later as slow queries and heavier storage. SigNoz stores telemetry as columns in ClickHouse, which makes ad hoc SQL over logs, traces and metrics possible in one engine, but the cost of a query still depends on how the tables and filters are used. Before you commit, run a proof of concept with your own log lines, trace shapes and metric names, and confirm that the query languages cover the questions your on-call engineers ask during an incident.

The main running cost difference is how many moving parts you own. The Grafana route means operating Grafana (with MySQL or PostgreSQL for configuration, since SQLite does not scale for production), Prometheus for metrics, Loki for logs with object storage behind it, and a tracing backend, each with its own capacity, backups and upgrades. SigNoz runs as one application with an OpenTelemetry Collector writing to ClickHouse, so the infrastructure line is mainly ClickHouse nodes and storage plus the application tier. Neither route is free to run. Ask each candidate to size storage and compute for your ingest rate and retention target, and do not accept a single figure without those inputs.

Staff time is usually the larger recurring cost. Five or six components mean more version pins, more certificates, more dashboards to keep in step and more on-call paths, while one application shrinks that surface but concentrates it in ClickHouse operations. Upgrade cadence matters here. Grafana's upgrade guidance says minor releases arrive every other month, each minor release is supported for 9 months, and the last minor release of a major version for 15 months, so a Grafana estate must be moved forward roughly on that rhythm across every component. Check SigNoz's release cadence and support terms in writing before you commit, because the sources supplied here do not state a support window for SigNoz releases.

Licenses and support are where quotes diverge. The SigNoz Community Edition is free to self-host, but its support is community Slack only, and SAML, SOC 2 and HIPAA compliance are not included. Grafana OSS is free, while SAML, team sync and SCIM require Grafana Enterprise or Grafana Cloud. Costs are one-time for design, migration and dashboard rebuilds, which often cost more than the first year of infrastructure when you move off Datadog or New Relic. They recur for infrastructure, upgrades, on-call and any subscription. Request a written quote listing components in scope, the hosting environment, retention targets for logs, metrics and traces, the support level and response expectations, and who performs upgrades and backups.

The Grafana stack is a set of separate systems, and each one needs its own design. For Loki, start with monolithic mode for smaller log volumes, which the Loki deployment modes documentation describes as a single binary handling up to about 20 GB of logs per day. Plan for simple scalable mode only if you can accept that it is scheduled for removal in Loki 4.0, which means moving to microservices later. Grafana itself needs a production database: SQLite is the default but the installation docs say it does not scale for production, so provision MySQL 8.0 or later or PostgreSQL 12 or later before go-live. For high availability, run two or more active-active Grafana servers behind a load balancer, and configure alerting deliberately, since the HA documentation says alerting needs extra configuration to avoid duplicate notifications. Loki also has no built-in authentication, so you must place an authenticating reverse proxy such as nginx, HAProxy, OAuth2 Proxy or Pomerium in front of it and set the X-Scope-OrgID tenant handling correctly.

SigNoz is a smaller set of moving parts on paper, but its work concentrates in ClickHouse and identity. Installation is a single application stack, deployed with Docker Standalone, Docker Swarm or Kubernetes with Helm. The main sizing decisions are the ClickHouse storage tier, retention per signal and the ingest volume that the OpenTelemetry Collector will write. Confirm retention and disk requirements with SigNoz and in a test environment rather than assuming them, because storage growth follows your telemetry volume. For SSO, the Community Edition includes SSO, but SAML is limited to the Teams and Enterprise plans, so confirm which identity protocol your directory needs before you choose an edition.

Across both stacks, projects run long in the same places. Label design and cardinality decide whether Loki or Prometheus stays affordable, and they are expensive to change after dashboards depend on them. Retention policy needs an owner before go-live, because Loki keeps logs indefinitely until retention is enabled. Alert migration is often underestimated, since every rule must be rebuilt, tested and deduplicated. Identity integration and load testing finish last and cause the most rework. DeployOpen plans, implements and tests these pieces inside your own infrastructure under a fixed, written scope, so ask for the scope to name each component, each identity provider and each load target before any work starts.