Grafana and Datadog can both help teams investigate metrics, logs and traces, but the operating model differs sharply. Grafana can run self-managed or through Grafana Cloud; Datadog is a hosted platform with its own collection, monitoring and application products. Choose by the signals you need and the operational work you can support, not by a blanket open source versus SaaS label.
Key takeaways
- Grafana Cloud bills by consumption: per 1,000 metric series, per GB of logs or traces, and per active user.
- Datadog bills mainly per host for infrastructure and APM, plus a separate per-GB and per-event rate for logs.
- Run both against one real workload for a week before comparing bills; list prices rarely match actual usage.
Who operates the data pipeline in each option?
A self-managed Grafana stack, typically paired with Prometheus, Loki or Tempo, gives a team full control over retention, scaling and upgrade timing, but the team must operate every one of those components itself. Grafana Cloud removes that operational burden and bills on a consumption basis: per 1,000 billable metric series, per gigabyte of logs, traces or profiles processed, and per monthly active user for visualization and incident response features. Datadog also provides fully hosted collection, dashboards and alerting, so there is no separate infrastructure to patch or scale, but its agent still needs to be deployed and kept current across every monitored host. Neither model is automatically cheaper: self-managed Grafana trades a subscription fee for engineering time, while both Grafana Cloud and Datadog trade that engineering time for a bill that scales with usage rather than headcount.
How do Grafana and Datadog actually charge for usage?
Grafana Cloud's pricing follows a consumption model: you pay per 1,000 metric series, per gigabyte of logs and traces, and per monthly active user, with a base usage allowance included and volume discounts applied automatically on Pro plans. A free tier exists alongside paid tiers. Datadog's Infrastructure Monitoring is priced per host per month, with a Pro tier starting near fifteen dollars per host on annual billing; Application Performance Monitoring is also billed per host, starting around thirty-one dollars per host when bundled with Infrastructure. Datadog's Log Management adds a separate charge of roughly ten cents per gigabyte ingested, plus about a dollar seventy per million log events retained at standard indexing. Model your own host count, series count and log volume against both rate cards before comparing headline numbers, since neither vendor's entry price reflects a realistic production bill.
What should you test in a side-by-side trial?
Run a structured trial rather than comparing marketing pages, since both platforms perform well on paper for common signals. Use the same representative application in both tools and compare four things: which data sources each option actually ingests, how much operational work is left to your team, what a projected monthly bill looks like at your real scale, and how quickly each tool speeds up finding a root cause during a staged incident. The table below outlines what to check in each column; fill in your own numbers before deciding, since generic examples will not reflect your traffic or retention needs.
| Question | Grafana | Datadog |
|---|---|---|
| Data sources | Test the metrics, logs and trace backends you plan to connect. | Test the integrations and agent coverage you need. |
| Operations | Count maintenance for self-managed components, or review Cloud support. | Review agent rollout, access controls and service setup. |
| Cost | Per 1,000 metric series, per GB of logs and traces, per active user; a free tier is available. | Around $15/host/month (Pro infra) and $31/host/month (APM bundle), plus about $0.10/GB for log ingest. |
| Incident workflow | Time the path from alert to root-cause evidence. | Run the same incident and compare alert quality. |
How do you decide between them?
Run both platforms against one representative application for a full week rather than a quick demo, since usage-based costs only become visible once real traffic accumulates. Set identical alerts in each tool, wait for or inject a genuine staged fault, and time how long it takes to reach root-cause evidence in both interfaces. Compare the billable units each platform actually reports for that week, not the advertised starting price, since add-on products and retention settings change the total quickly. Teams already running Prometheus, Loki or Tempo in production may find self-managed Grafana a natural extension of tooling they operate already. Teams that want one hosted vendor covering infrastructure, logs and APM without running any collection pipeline may prefer Datadog's bundled model. Treat both as hypotheses to test, not universal verdicts.
Read Grafana Cloud billing documentation and Datadog integration documentation for current rate cards, then compare the Grafana and Datadog profiles. Prices and bundles change; sources checked 27 September 2026.
Frequently asked questions
Is Grafana Cloud cheaper than Datadog?
It depends on your metric series count, log volume and host count: Grafana Cloud bills per 1,000 series and per gigabyte, while Datadog bills mainly per host plus separate log charges. Model both rate cards against your actual environment rather than comparing entry-level list prices, since neither is consistently cheaper across every workload.
Can I use Grafana dashboards on top of Datadog data?
Grafana can query many external data sources, but connecting it to Datadog's own backend typically requires Datadog's API and is not the primary supported workflow for either vendor. Most teams pick one platform as the primary store and use its native dashboards rather than bridging the two.
Do I need to self-host Grafana to use it?
No. Grafana Cloud offers a hosted option with a free tier and consumption-based paid plans, so teams can use Grafana's dashboards without operating Prometheus, Loki or Tempo themselves. Self-managed Grafana remains an option for teams that want full control over retention and the underlying data stores.