Datadog vs Grafana

Datadog is the all-in-one monitoring platform where everything works together out of the box. Grafana is the open-source visualization layer that pairs with Prometheus, Loki, and other data sources. One costs real money. The other costs real effort.

Updated 2026-09 · 2026

Datadog

All-in-one monitoring platform with metrics, logs, traces, and more

$15/host/moper host/month (Infrastructure, annual commitment; $18 on-demand)

Strengths

+Everything in one place - metrics, logs, traces, APM, RUM
+Setup is fast - install the agent and data starts flowing
+Correlated views across infrastructure, apps, and logs

Weaknesses

-Bills can spiral fast - $15/host is just the start
-Log ingestion costs are notoriously high at scale
-Custom metrics pricing catches people off guard

Best for

Teams that want unified monitoring without stitching tools together, and have the budget to pay for convenience at scale.

Grafana

Open-source visualization and monitoring you can self-host

Freefree (open source, cloud plans available)

Strengths

+Free and open source - no per-host pricing
+Works with any data source - Prometheus, InfluxDB, Elasticsearch, and more
+Self-hostable with full control over your data

Weaknesses

-Self-hosting means managing Prometheus, Loki, Tempo, etc. yourself
-Stitching together the full stack (metrics + logs + traces) takes work
-Alerting is less polished than Datadog's unified experience

Best for

Engineering teams comfortable with open-source tooling who want to avoid vendor lock-in and per-host pricing at scale.

Feature Comparison

Feature	Datadog	Grafana
Price	$15/host/mo (Infrastructure, annual)	Free (open source)
Metrics	Built-in, per-host pricing	Via Prometheus, Mimir, or other sources
Logs	Built-in, per-GB pricing	Via Loki (free, self-hosted)
Traces / APM	Built-in APM product	Via Tempo (free, self-hosted)
Dashboards	Yes, drag-and-drop	Yes, powerful and flexible
Alerting	Unified across all data	Yes, per data source
Integrations	700+ out-of-box integrations	80+ data source plugins
Self-hosting	No	Yes
Setup time	Minutes - install agent, done	Hours to days for full stack
Vendor lock-in	High	Low - swap data sources freely
Scaling cost	Linear - more hosts = more cost	Infrastructure cost only
Session replay / RUM	Yes, built-in	Limited (via plugins)

The Verdict

At 10 hosts, Datadog is convenient and the bill is manageable. At 100 hosts with logs and APM, the invoice starts to sting. At 1,000 hosts, you'll wish you'd invested in Grafana earlier. The Grafana + Prometheus + Loki stack gives you the same observability capabilities for the cost of running the infrastructure. The tradeoff is setup time and operational complexity. If you have a small team and no dedicated platform engineers, Datadog's ease of use is worth paying for. If you have the engineering capacity and plan to scale, building on Grafana's open-source stack will save you serious money long-term.

How to switch from Datadog to Grafana

Full Datadog export guide →

1Export your existing Datadog dashboards and monitors as JSON using the Dashboards API (GET /api/v1/dashboard) or the UI's export option, so you have a reference to rebuild them in Grafana - Datadog does not support bulk historical metric/log export.
2Stand up the core Grafana stack: Prometheus for metrics, Loki for logs, and Tempo for traces (or use Grafana Cloud's free tier to skip self-hosting initially).
3Install the Prometheus node exporter and relevant service exporters (Postgres, Nginx, Kubernetes, etc.) on your hosts to replace Datadog's Agent-collected metrics.
4Recreate your Datadog dashboards and monitors in Grafana manually, using the exported JSON as a checklist - Grafana has its own JSON dashboard format and a large library of community dashboards to start from instead of building everything from scratch.
5Run both systems in parallel for 2-4 weeks (dual-ship data) to validate that alerts fire correctly and dashboards match before decommissioning Datadog.
6Cut over the team by updating on-call runbooks, Slack/PagerDuty alert integrations, and access permissions to point at Grafana, then cancel or downgrade the Datadog contract.

Datadog vs Grafana: common questions

How do I export my data out of Datadog before switching?+

Datadog doesn't offer a bulk 'export everything' button - you export dashboards and monitors individually as JSON via the API or UI (Dashboard List > export). Metrics and logs generally aren't exportable in bulk; instead, you set up dual-shipping (sending new data to both Datadog and your new Prometheus/Loki stack) during a transition window rather than migrating historical data.

What do I lose by switching from Datadog to Grafana?+

You lose the single-vendor correlation between metrics, logs, and traces that Datadog stitches together automatically - in Grafana you're wiring that up yourself across Prometheus, Loki, and Tempo. You also lose built-in RUM/session replay, and Datadog's APM has more automatic instrumentation than Grafana's Tempo-based tracing out of the box.

Is Grafana Cloud's free tier enough for a small team?+

For a small team running a handful of services, yes - the free tier (10K metrics series, 50GB logs, 50GB traces per month) covers a lot before you hit limits. Once you're running dozens of services with high-cardinality metrics or verbose logging, you'll likely need a paid Grafana Cloud plan or a self-hosted setup.

Will I lose my Datadog integrations when I move to Grafana?+

Most Datadog integrations map to a Prometheus exporter or a Grafana data source plugin, but you'll need to set each one up manually - there's no 1:1 automatic transfer. Common ones (AWS, Kubernetes, Postgres, Nginx) have well-documented exporters, but niche integrations may require custom work or may not have an equivalent.

Does switching to Grafana actually save money over time?+

Yes, usually - once you're past a small number of hosts, Datadog's per-host and per-GB pricing scales faster than the infrastructure cost of self-hosting Prometheus, Loki, and Tempo. The catch is engineering time: you're trading a monthly bill for ongoing operational effort to run and maintain the stack yourself.