Grafana vs Redash: Open-Source Dashboards Compared

Someone on your team has decided you need an open-source dashboard tool. Two names come up: Grafana and Redash. Both are free to self-host. Both connect to databases. Both produce charts. And that is roughly where the similarities end.

I've deployed both in production environments, and the experience of using them day-to-day is so different that comparing them feels a bit like comparing Vim and Excel. Yes, both display text. No, they are not interchangeable.

The core split: monitoring vs. analytics

Grafana was built to answer "is my infrastructure healthy right now?" It started as a visualization layer for Graphite and InfluxDB — time-series databases that store system metrics. Its default data sources are Prometheus, CloudWatch, Loki, Tempo. The query language it speaks most fluently is PromQL:

rate(http_requests_total{service="api", status=~"5.."}[5m])

That expression tells you how many 5xx errors your API threw in the last five minutes. Grafana renders it beautifully — auto-refreshing line charts, threshold-based color changes, annotations marking deploys. This is what Grafana was born to do.

Redash was built to answer "what does the business data say?" It connects to PostgreSQL, MySQL, BigQuery, Snowflake, Redshift, ClickHouse — the databases where your actual business lives. You open Redash, you write SQL, you pick a chart type. That's it:

SELECT
  date_trunc('week', created_at) AS week,
  plan_name,
  COUNT(*)                       AS new_subscriptions
FROM subscriptions
WHERE created_at >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY 1, 2
ORDER BY 1;

No PromQL. No time-series assumptions. Just SQL against your production or warehouse database, turned into a chart.

These are fundamentally different workflows for fundamentally different people.

Who uses what

The Grafana user is an SRE. A platform engineer. Someone who has Prometheus exporters running on every service, who monitors pod memory in Kubernetes, who gets paged when p99 latency crosses a threshold. They think in metrics, counters, and histograms. They are comfortable with this:

histogram_quantile(0.99,
  sum(rate(http_request_duration_seconds_bucket{job="api"}[5m]))
  by (le)
)

The Redash user is a data analyst. A RevOps manager. Someone who needs to know which campaigns drove conversions last quarter, or how churn differs between annual and monthly plans. They think in SQL. They want to write a query, see rows, pick a visualization, save it to a dashboard, and share a link.

This audience split is not a minor detail — it dictates everything about how the tools work.

Grafana's strength: the plugin ecosystem and alerting

Grafana's ecosystem is enormous. Over 200 official and community data source plugins, covering everything from Elasticsearch to Azure Data Explorer to Infinity (a generic REST API connector). The visualization plugin library is similarly deep — custom panels for flowcharts, network topology maps, status grids, Plotly charts.

And then there's alerting. Grafana's alerting system has matured significantly — you define alert rules using the same query language as your panels, set thresholds, configure notification channels (Slack, PagerDuty, email, webhooks), and manage silences and escalation. For an infrastructure team, this means your dashboard and your on-call alerting live in the same tool. That's genuinely valuable.

Redash has... none of this. No plugin system. No alerting engine. Redash does have "alerts" in the loosest sense — you can set a query to run on a schedule and trigger a notification if a value crosses a threshold — but it is rudimentary compared to Grafana's multi-condition, multi-channel alerting with grouping, inhibition, and silencing.

If you need dashboards and alerting in one tool, and your data is metrics, Grafana wins by default.

Redash's strength: pure SQL simplicity

What Redash lacks in ecosystem it makes up for in directness. There is almost no abstraction between you and your data. You write SQL. You see results. You make a chart.

Three features stand out:

Parameterized queries. You embed {{parameter}} tokens directly in SQL and Redash renders interactive widgets — dropdowns, date pickers, multi-selects — on the dashboard. Users pick values, the query re-runs. No plugin needed, no config file, no YAML. A finance team can build a self-service revenue dashboard by writing exactly one parameterized query:

SELECT
  date_trunc('month', invoice_date)  AS month,
  SUM(amount_cents) / 100.0          AS revenue
FROM invoices
WHERE region = '{{region}}'
  AND invoice_date BETWEEN '{{start_date}}' AND '{{end_date}}'
GROUP BY 1
ORDER BY 1;

Query results as data sources. Redash lets you reference one saved query's results from another query. You can layer transformations without building a pipeline — no dbt, no temp tables, no materialized views. For small teams without a data engineering function, this is a lifeline.

Fork and iterate. See a colleague's query, fork it, tweak it, save your version. It is the simplest possible collaboration model, and it maps perfectly to how data teams actually work.

Grafana can query SQL databases — PostgreSQL, MySQL, ClickHouse are all supported data sources. But the experience is panel-level: a small query box embedded inside a dashboard panel editor. There is no standalone SQL workspace, no saved-query library, no forking. If you are an analyst who works in SQL, Grafana's query interface will feel like an afterthought. Because for Grafana, it is one.

The self-hosting tax

Both tools are free to download. Neither is free to run.

Grafana ships as a single Go binary, which sounds simple until you realize that a production deployment also needs a database backend (SQLite for dev, PostgreSQL or MySQL for production), a reverse proxy, TLS certificates, and — if you want useful dashboards — the actual data sources (Prometheus, Loki, etc.) that Grafana visualizes. A typical Grafana stack on Kubernetes looks something like:

# Just the monitoring stack, not counting your actual services
- prometheus-server
- prometheus-alertmanager
- grafana
- loki
- promtail (on every node)
- tempo (if you want traces)
- mimir (if you need long-term storage)

That is a lot of moving parts to keep running, patched, and backed up. Grafana Labs sells a managed cloud version, which eliminates the ops burden but introduces per-metric and per-log-line pricing that can get expensive at scale.

Redash is a Python app backed by PostgreSQL and Redis, deployed via Docker Compose or Kubernetes. The docker-compose.yml in the Redash repo spins up five containers (server, worker, scheduler, PostgreSQL, Redis). It is simpler than the full Grafana observability stack, but it still requires you to handle upgrades, backups, SSL, and auth.

And here is the bigger concern with Redash: the project's maintenance trajectory. Databricks acquired Redash in 2020. The hosted Redash.io service shut down. The GitHub repo still exists, but the last major release was v10 in late 2021. Community forks are keeping it alive, but there is no official roadmap, no funded team shipping monthly releases. If you adopt Redash today, you are betting on the community fork ecosystem — which might be fine, but you should know what you are signing up for.

For a deeper look at Redash's maintenance status and how it compares to Metabase, I covered that in a separate post.

When Grafana is the right call

Pick Grafana when your primary job is infrastructure monitoring. You run Kubernetes. Your data is in Prometheus and Loki. Your team is engineers who speak PromQL. You need alerting integrated with your dashboards. You want a massive plugin ecosystem.

Grafana is also the right call when you need real-time operational dashboards — the kind that auto-refresh every 5 seconds and live on a TV mounted on the wall. Its streaming and auto-refresh architecture is purpose-built for this. Redash's scheduled-query model is not.

For more on how Grafana compares to other tools in its monitoring lane, see the Grafana vs Apache Superset and Grafana vs Metabase breakdowns.

When Redash is the right call

Pick Redash when your team lives in SQL and just needs a place to write queries, save them, and share charts. When you do not need a visual query builder (that is Metabase's territory). When parameterized queries and query forking are more valuable to you than a plugin ecosystem. When your data lives in relational databases or warehouses — not in Prometheus.

Redash's simplicity is its greatest asset. There is very little to learn. The mental model is: write SQL, get chart. For a small data team that wants to move fast without learning a new abstraction layer, that simplicity is hard to beat.

But factor in the maintenance risk. If you are starting fresh, the Redash project's uncertain future is a real consideration — not a dealbreaker, but something you should plan around.

The third option: skip the self-hosting entirely

Here is the pattern I keep seeing: a team spends a weekend setting up Grafana or Redash. It works. Then six months later someone has to upgrade PostgreSQL, or a Docker image breaks, or an SSL certificate expires on a Friday afternoon, or the Redash worker queue backs up and nobody notices until dashboards go stale. The tool itself is free, but the operational cost is not.

If what you actually want is SQL-native dashboards — write a query, get a chart, share a link — without managing Docker containers, database backups, or reverse proxies, that is exactly what Fastero does. You connect your database, write SQL, and build live dashboards that auto-refresh from your queries. No infrastructure to manage. No plugins to install. No YAML.

And unlike Grafana, SQL is a first-class citizen — not a secondary data source bolted onto a monitoring tool. Unlike Redash, there is a funded team shipping updates every week, not a community fork hoping someone merges the security patch.

If you're weighing whether the right move is Grafana or a code-first approach with Python, that is a different axis entirely — but worth reading if you are still deciding where your dashboards should live.

Try Fastero free — SQL-native dashboards with managed hosting, no infrastructure to babysit. No credit card required.