Datadog vs. Grafana usually gets flattened into paid SaaS versus open source. That framing misses the trade-off that matters: operational overhead versus cost predictability.
Datadog offers a fully managed observability platform for metrics, logs, and traces, but pricing can become harder to predict as telemetry grows. Grafana offers more control, whether through a self-hosted stack with Prometheus, Loki, and Tempo or Grafana Cloud, but that control comes with operational overhead or cloud costs.
Neither automatically solves the harder problem: connecting a signal on a dashboard to the system behavior that caused it.
Here’s how Datadog vs. Grafana compares on deployment, pricing, and observability scope.
Datadog vs. Grafana at a glance
The biggest differences between Datadog and Grafana come down to how they're deployed, how usage is priced, and how much of the observability stack your team wants to manage.
Deployment model: Managed SaaS vs. self-hosted or Grafana Cloud
Datadog is a fully managed SaaS platform, while Grafana gives teams a choice between running a self-hosted observability stack and using the managed Grafana Cloud.
The main difference is operational responsibility. Datadog and Grafana Cloud manage the underlying infrastructure, while a self-hosted Grafana stack leaves deployment, scaling, storage, upgrades, and availability with your team.
Datadog: Fully managed SaaS
Datadog manages the infrastructure behind its observability platform. Install the Datadog Agent, and Datadog handles ingestion, storage, scaling, and uptime for the platform itself.
There's no cluster to size, no storage backend to tune, and no upgrade path to plan around.
Grafana: self-hosted stack or Grafana Cloud
Grafana can be self-hosted or consumed as the fully managed Grafana Cloud. At one end, the self-hosted Loki, Grafana, Tempo, and Mimir (LGTM) stack (Loki for logs, Grafana for visualization, Tempo for traces, and Mimir for metrics) runs entirely on infrastructure a team owns and operates. At the other, Grafana Cloud manages that infrastructure for you as a SaaS platform.
Self-hosting removes commercial software licensing costs, but not operational costs. Each component in the stack is built to scale, which is exactly why running it takes real engineering work:
- Mimir: Scaling metrics means managing capacity as the number of active series grows.
- Loki: Log ingestion and queries require capacity planning as data volume increases.
- Tempo: Distributed tracing still requires teams to operate and scale the tracing backend and its object storage.
- Upgrades: Separate components have their own releases and dependencies to manage.
- Availability: Your team is responsible for keeping the monitoring stack available when the systems it monitors have problems.
That makes self-hosted Grafana an ongoing platform-engineering responsibility, not simply a one-time deployment. Grafana Cloud removes much of that operational overhead by handing the infrastructure back to Grafana Labs. The trade-off then shifts from engineering effort toward SaaS pricing and data usage.
Pricing predictability and cardinality
Metric cardinality is the number of unique time series created by different combinations of metric labels. For example, adding customer_id to a metric can turn one time series into thousands because every customer creates a unique label value.
That matters for pricing because higher cardinality means more telemetry to ingest, process, and store.
Datadog accounts for that growth through custom metrics, while Grafana Cloud tracks active series. With self-hosted Grafana, the cost instead shows up in the infrastructure and engineering resources needed to handle that additional data.
Datadog: Custom metrics can increase usage costs
Under its custom metrics pricing, Datadog Infrastructure Pro includes 100 indexed custom metrics per host and Enterprise includes 200, with additional usage billed separately. The allocation is pooled across the account rather than applied to individual hosts.
Adding a high-cardinality label such as customer_id can quickly consume that allowance. A small change to metric tagging can therefore increase costs without changing the subscription tier.
Grafana Cloud: Active series can increase metrics usage
Grafana Cloud tracks active series as part of metrics usage. Each unique combination of a metric and its label values can create another active series, so adding customer_id across thousands of customers can significantly increase metrics usage.
The exact cost depends on the Grafana Cloud plan and usage, but higher cardinality still means more telemetry for the platform to process.
Self-hosted Grafana: Cardinality shifts costs to infrastructure
Self-hosting the LGTM stack removes the vendor's per-series or per-metric rate card, but not the cost of processing that telemetry. More series require more compute, memory, storage, capacity planning, and operational work.
Open source therefore shifts the cost from vendor usage charges to infrastructure spend and engineering overhead rather than eliminating it.
Observability scope and AI-assisted analysis
Both Datadog and Grafana Cloud provide anomaly detection and AI-assisted investigation, but they package those capabilities differently. Datadog integrates them into its observability platform through Watchdog, while Grafana Cloud combines machine learning and investigation capabilities with its broader metrics, logs, and traces ecosystem.
Datadog: Built-in anomaly detection with Watchdog
Datadog uses Watchdog as its built-in AI engine for anomaly detection, investigation, and root cause analysis. It continuously analyzes observability data for unusual behavior without requiring teams to configure anomaly detection manually.
Watchdog can draw on APM traces, infrastructure metrics, deployment data, and log anomalies to investigate application performance issues and identify potential causes.
Grafana: Anomaly detection and AI-assisted investigations
Grafana Cloud also provides machine learning and AI-assisted analysis rather than relying solely on teams to correlate signals manually. Grafana Machine Learning supports forecasting, anomaly detection, and outlier detection across data sources including Prometheus, Loki, Elasticsearch, and Datadog. Teams can also create alerts based on detected anomalies.
For incident investigation, Grafana Assistant Investigations can query metrics, logs, traces, and profiles, build hypotheses, and produce a structured investigation report. This sits alongside the underlying Grafana observability stack, where Loki handles logs, Tempo handles distributed tracing, and Mimir provides scalable metrics storage.
The distinction is therefore less about whether AI-assisted analysis exists and more about how it fits into each platform. Datadog provides Watchdog as an integrated intelligence layer across its observability products, while Grafana Cloud combines AI and machine learning capabilities with its composable observability ecosystem.
The false binary: SaaS convenience vs. open-source control
The Datadog vs. Grafana decision can look like a straight trade: pay for the convenience of a managed SaaS platform or spend engineering time running an open-source observability stack for more control. But many teams are trying to reduce both cost uncertainty and operational overhead.
That tension shows up in buying priorities. The 2026 Grafana observability survey found that 65% of respondents cite cost as an important tool selection criterion, while 38% name complexity and overhead as their biggest observability concern.
That creates a third question beyond Datadog vs. Grafana: can teams get a managed observability platform without taking on either unpredictable usage costs or the operational burden of running the monitoring stack themselves? Datadog alternatives offer another way to approach that trade-off.
Where New Relic fits: Full-stack observability without the trade-off
New Relic offers another approach: a managed observability platform that supports open-source telemetry standards without requiring teams to operate the backend themselves. Teams can keep tools such as Grafana and Prometheus while reducing the infrastructure required to collect, store, and analyze telemetry.
Grafana, Prometheus, and OpenTelemetry compatibility
As more teams standardize around vendor-neutral telemetry, moving observability backends shouldn't mean reinstrumenting applications. The Cloud Native Computing Foundation (CNCF) graduated OpenTelemetry in 2026, with more than 2,800 companies contributing to the project. Its vendor-neutral approach gives teams a common way to collect metrics, logs, and traces without tying that instrumentation to a single observability platform.
New Relic fits into that model by supporting OpenTelemetry and Prometheus natively. Teams can send Prometheus metrics to New Relic and use New Relic as a Prometheus data source for Grafana, keeping existing Grafana dashboards while moving the telemetry backend to a managed platform. It's an approach New Relic has supported through its Grafana partnership since 2020.
That gives teams another option between replacing Grafana entirely and continuing to operate the full Loki, Grafana, Tempo, and Mimir stack themselves.
Managed infrastructure without self-hosting overhead
New Relic manages telemetry ingestion, storage, and scaling, reducing the infrastructure and on-call work associated with a self-hosted observability stack. Tool consolidation also brings metrics, logs, traces, and other telemetry into one platform rather than requiring teams to operate separate backends.
New Relic prices telemetry by data ingested per GB, plus user seats, rather than per custom metric or active series, so adding a label like customer_id doesn't trigger a separate metrics charge. Every account also includes 100 GB of free data ingest a month.
AI-assisted correlation across metrics, logs, and traces
Bringing metrics, logs, traces, and other telemetry into a shared data platform allows AI-assisted analysis to work across signals. Teams can connect changes in application performance with the infrastructure and telemetry surrounding them rather than investigating each data source separately.
That addresses the correlation problem at the center of the Datadog vs. Grafana comparison: connecting a signal on a dashboard to the system behavior behind it without building and operating the correlation layer yourself.
See how New Relic's platform brings telemetry and analysis together in one managed observability platform, or sign up for free to try it with your existing monitoring stack.
The views expressed on this blog are those of the author and do not necessarily reflect the views of New Relic. Any solutions offered by the author are environment-specific and not part of the commercial solutions or support offered by New Relic. Please join us exclusively at the Explorers Hub (support.newrelic.com) for questions and support related to this blog post. This blog may contain links to content on third-party sites. By providing such links, New Relic does not adopt, guarantee, approve or endorse the information, views or products available on such sites.
