Most teams comparing Datadog and Prometheus are choosing between two different things. Datadog is a hosted observability platform you rent by the host, with metrics, logs, traces, and security in one bill. Prometheus is an open-source metrics engine that you run yourself, and it only handles metrics.
The two often end up side by side. The Datadog Agent can scrape Prometheus endpoints through its OpenMetrics integration, and plenty of teams run Prometheus in their clusters while paying Datadog for everything else. So the real question for you is how much of your monitoring you want to operate yourself, and how much you'd rather pay someone else to run.
Prometheus originated at SoundCloud and has become a standard part of the cloud-native monitoring stack, particularly alongside Kubernetes. Prometheus 3.0, released in November 2024, was its first major version in seven years. It brought a rewritten UI, native OpenTelemetry metrics ingestion, and UTF-8 metric names.
Below, you'll compare Datadog and Prometheus across ten criteria:
| Feature | Datadog | Prometheus |
|---|---|---|
| Deployment options | ✓✓ | ✓ |
| Data sources | ✓✓ | ✓✓ |
| Data visualization | ✓✓ | ✓ |
| Real-time monitoring | ✓✓ | ✓✓ |
| Search capabilities | ✓✓ | ✓ |
| Machine learning | ✓✓ | ✕ |
| Scalability | ✓✓ | ✓ |
| Pricing, free plan | ✓ | ✓✓ |
| UI and UX | ✓✓ | ✓ |
| Documentation and support | ✓✓ | ✓✓ |
✕ - does not support
✓ - partial support
✓✓ - full support
Datadog is a SaaS platform, so there's no monitoring backend for you to install or maintain. You sign up, install the Datadog Agent on your hosts or as a DaemonSet in Kubernetes, and the Agent starts sending metrics, logs, and traces to Datadog.
Prometheus is software you download and run on your own infrastructure. A typical setup includes the Prometheus server, which scrapes and stores metrics; exporters, which expose metrics from systems like databases and message brokers; client libraries for instrumenting your own code; and Alertmanager for routing alerts. In Kubernetes, most teams deploy it with the Prometheus Operator or the kube-prometheus-stack Helm chart. If you'd rather not run the server yourself, managed options like Amazon Managed Service for Prometheus, Google Cloud Managed Service for Prometheus, and Grafana Cloud accept Prometheus data too.
Datadog gets you running faster with far less operational work. Prometheus asks for more setup and upkeep, but in return you get full control over where your data lives and how it's stored.
Datadog collects data through more than 1,000 pre-built integrations covering cloud platforms like AWS, GCP, and Azure, as well as databases, web servers, queues, and SaaS tools. The Datadog Agent handles host and container metrics, and you can write custom checks or send data through the API, DogStatsD, or OpenTelemetry for anything Datadog doesn't cover natively.
Prometheus collects data by scraping HTTP endpoints that expose metrics in its text format. Exporters bridge the gap for software that doesn't expose Prometheus metrics itself, and the ecosystem includes exporters for most databases, message brokers, hardware, and cloud services. Client libraries let you instrument your own applications directly.
Since version 3.0, Prometheus can also act as a native receiver for OTLP metrics. That means applications instrumented with OpenTelemetry can push metrics straight to it without a Collector in between.
Datadog includes dashboards, timeseries graphs, host and container maps, service maps, and out-of-the-box dashboards for most of its integrations. You can build custom views with a drag-and-drop editor, template variables, and a wide range of widgets, and share dashboards publicly or with specific teams.
Prometheus doesn't aim to be a dashboarding tool. Its built-in web UI lets you run PromQL queries and graph the results, and the rewritten UI in Prometheus 3.0 adds a PromLens-style tree view that breaks a query into its parts so you can see what each step returns. For real dashboards, most teams use Grafana to query and visualize Prometheus data.
Datadog provides real-time monitoring across metrics, logs, and traces. You can watch live dashboards, set up monitors that alert on thresholds or changes, and use distributed tracing to follow individual requests through your services and find bottlenecks.
Prometheus provides real-time monitoring by scraping its targets at a regular interval, commonly every 15 to 60 seconds. You write alerting rules in PromQL, and Alertmanager handles grouping, silencing, and routing those alerts to email, Slack, PagerDuty, or webhooks. Recording rules let you precompute expensive queries, so dashboards and alerts stay fast.
Alertmanager can tell you something is wrong, but it doesn't wake anyone up, track who's on call, or keep an incident timeline. Better Stack connects to Alertmanager and Datadog and turns their alerts into incidents, with on-call scheduling, escalation policies, and unlimited phone call and SMS alerts. It also covers uptime monitoring and status pages, so you can drop a separate paging tool from your stack.
Route your Prometheus and Datadog alerts to the right person with Better Stack
Datadog lets you search across metrics, logs, and traces from one interface, with facets, filters, and boolean operators. You can pivot from a spike on a metric graph to the logs and traces from the same time window, which makes it much faster to go from "something is wrong" to "here's the request that failed."
Prometheus uses PromQL to query its time-series database. PromQL is powerful for metrics work: you can select series by label, aggregate across dimensions, calculate rates, and join series together. However, Prometheus only stores metrics, so there are no logs or traces to search, and you'll need other tools such as Loki or Tempo to cover those.
Datadog wins this round because it searches across every telemetry type in one place, while Prometheus stops at metrics.
Datadog includes several machine learning features. Anomaly detection and outlier detection monitors flag unusual behavior in your metrics, and forecast monitors predict when a metric will cross a threshold, such as a disk filling up. Watchdog automatically surfaces anomalies across your stack without you configuring anything, and Bits AI, Datadog's AI assistant, can help investigate incidents. Some of these features, including Watchdog and anomaly detection, are reserved for the Enterprise tier.
Prometheus has no native machine learning features. PromQL includes statistical functions like predict_linear() for simple linear forecasting, and in Prometheus 3.0 the old holt_winters() function was renamed to double_exponential_smoothing() and moved behind an experimental feature flag. For anomaly detection, you'll need to export data to an external tool or write your own rules.
Datadog is a managed platform, so scaling is Datadog's problem rather than yours. It handles large data volumes without you provisioning storage or tuning anything. The tradeoff shows up on your bill, since costs grow with every host, container, custom metric, and gigabyte of logs.
A single Prometheus server scales vertically and handles a lot of data, but it wasn't designed for horizontal scaling or long-term storage on its own. Local storage is typically kept for weeks, not years, and one server can only hold so many active series. For larger environments, teams use federation to aggregate data from several Prometheus servers, or use remote write to send data to a scalable backend like Thanos, Cortex, or Grafana Mimir. These approaches work well, but they add components you have to deploy and operate.
If you want to keep Prometheus but skip running Thanos or Mimir for long-term storage, Better Stack accepts Prometheus metrics through remote write or scraping. You get managed long-term storage, PromQL queries, and dashboards next to your logs and traces. The free plan includes 30 GB of metrics, and beyond that, metrics retention costs $0.75 per GB per month.
Datadog offers a free plan for up to 5 hosts with 1 day of metric retention, plus a free trial of its paid features. Infrastructure monitoring then comes in two paid tiers:
Infrastructure monitoring is only one line on the bill. APM, log management, synthetics, real user monitoring, and security are each priced separately, on their own units such as hosts, gigabytes, or sessions. That's why your final Datadog invoice is often much higher than the per-host number suggests.
Prometheus is open source and free to use. Your costs come from the compute and storage to run it, plus any long-term storage backend like Thanos or Mimir, and the engineering time to operate all of it. If you use a managed service such as Amazon Managed Service for Prometheus or Grafana Cloud, you pay for ingested samples, storage, and queries instead of servers.
Datadog's UI is polished and consistent across its products, with customizable dashboards, maps, and a unified search that lets you move between metrics, logs, and traces without switching tools. The sheer number of features can feel overwhelming at first, but common workflows are easy to find.
Prometheus 3.0 replaced its dated web UI with a modern one built on a current React stack, with less clutter, better autocomplete, and the tree view for understanding queries. It's a big improvement for exploring data, but it's still a query tool rather than a full monitoring interface. That's why Prometheus is so often paired with Grafana, which provides the dashboards, alert views, and multi-source visualization most teams expect.
Datadog provides extensive documentation, integration guides, and the free Datadog Learning Center with hands-on courses. Paid customers can open support tickets and use in-app chat, and premium support plans are available for faster response times.
Prometheus relies on its open-source community. The official documentation covers configuration, PromQL, storage, and best practices, and the community is large and active, with mailing lists, forums, GitHub discussions, and the annual PromCon conference. If you need commercial support, several vendors offer it for Prometheus deployments.
Whichever tool you pick, alerting is where both get harder than they should be. Datadog monitors take time to tune, and Alertmanager routing lives in YAML. Better Stack plugs into both, so your existing alerts create incidents with an on-call schedule, escalation policies, and a second-by-second timeline. The free plan includes 10 monitors and heartbeats and a status page, and on-call starts at $34 per responder per month, or $29 billed annually.
The choice here isn't really about features. Datadog sells you time: you skip running servers, and you pay for that every month, per host and per product. Prometheus gives that time back as work, since you own the scraping, storage, scaling, and alert routing, and the software itself costs nothing.
If your team has the Kubernetes experience to run Prometheus and a long-term storage backend, it will do the job for a fraction of the price. If nobody wants to be the person who gets paged when Prometheus runs out of disk, Datadog's invoice is the cost of never becoming that person.
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