Comparison
Prometheus vs Datadog
Self-hosted open source versus managed SaaS observability, how to choose the right fit for your team.
Comparison table
| Factor | Prometheus + Grafana | Datadog |
|---|---|---|
| Licensing | Open source (Prometheus is Apache 2.0; Grafana is AGPLv3) | Commercial SaaS |
| Cost model | Infrastructure cost only; no per-host or per-metric fee | Per host/container; per custom metric; per log GB ingested |
| Operational overhead | High: you manage Prometheus, Alertmanager, Grafana, retention, HA | Low: fully managed SaaS |
| Kubernetes integration | Strong: kube-prometheus-stack, ServiceMonitor CRDs, native PromQL | Strong: Datadog Agent DaemonSet, auto-discovery, Kubernetes dashboard |
| APM / tracing | Via Jaeger, Tempo, or OpenTelemetry (separate setup) | Built-in APM with distributed tracing, flame graphs |
| Log management | Via Loki or ELK (separate setup) | Built-in Log Management (charged per GB) |
| Data retention | Configurable locally (Prometheus defaults to 15 days); longer with Thanos/Mimir + object storage | Depends on plan and data type; see Datadog documentation |
| Query language | PromQL (powerful, requires learning) | Proprietary query language + SQL-like notebook queries |
When to choose each
Choose Prometheus + Grafana when:
- Cost is a primary constraint, as self-hosting can cost less than per-host SaaS pricing at scale, depending on workload and the engineering time needed to run it
- You have SREs or platform engineers who can own the monitoring stack
- You need long-term metric retention under your own control
- Data sovereignty or compliance requires metrics to stay on-premises or in your cloud account
- You are already using OpenTelemetry and want vendor-neutral observability
Choose Datadog when:
- You want a unified platform (metrics + logs + traces + RUM) with minimal setup
- Your team lacks platform engineering capacity to run a self-hosted observability stack
- You need APM with distributed tracing and code-level profiling out of the box
- You are an early-stage startup where engineering time is more expensive than Datadog's fee
- You need to satisfy an investor or customer who requires enterprise-grade observability tooling quickly
Which team size and stage suits Prometheus, and which suits Datadog?
Small teams without a dedicated platform or SRE function usually get to value faster with a managed product such as Datadog, while teams with at least one engineer who can own the monitoring stack tend to be well served by Prometheus and Grafana. The deciding variable is rarely the feature list. It is who will be paged when the monitoring itself breaks, and whether that person has time to maintain it.
Prometheus is a single binary that stores data on local disk. Its own documentation states that local storage is not clustered or replicated, and should be treated like any single-node database. That is fine for a handful of clusters, but it means high availability, long-term storage and upgrades are tasks your team owns. Datadog removes that ownership, in exchange for a usage-based bill and a dependency on a third party.
Existing stack matters as well. If you already run Kubernetes with Helm and GitOps, the kube-prometheus-stack chart fits naturally into how you deploy everything else. If your estate is mixed, with virtual machines, managed databases, serverless and SaaS integrations, a hosted platform with many ready-made integrations can save a lot of wiring.
- Early stage, no platform engineer: managed SaaS is usually the pragmatic start.
- Two or more engineers who treat observability as a product: self-hosted is realistic.
- Large Kubernetes estate with strong cost pressure: self-hosted or a managed Prometheus service is worth modelling.
- Mixed legacy and cloud estate with little time: a unified SaaS agent reduces integration effort.
How should a CTO decide between them?
Decide by scoring five questions in order: who owns operations, what the telemetry must be allowed to contain, how fast data volume will grow, how many signal types you need in one place, and how much switching flexibility you want to keep. Whichever option wins on three or more of these is normally the right default, and the remaining two become risks to manage rather than reasons to change course.
The operating model question comes first because it is the hardest to change later. A tool nobody owns degrades quietly: alert rules rot, dashboards drift, and retention fills the disk. The data question is second. If regulation or customer contracts restrict where telemetry may be processed, you need to confirm what each option sends outside your accounts before you commit.
Volume growth is the third question. Usage-priced products reward discipline and punish surprise, because cost follows the number of hosts, custom metrics and ingested log volume. Self-hosted systems shift the risk elsewhere: cardinality explosions can exhaust memory on a Prometheus server long before they show up on any bill.
| Question | Leans Prometheus and Grafana | Leans Datadog |
|---|---|---|
| Who owns the stack? | A named platform or SRE team | No one has spare capacity |
| Where may telemetry live? | Must stay in your own accounts | A processor agreement is acceptable |
| How will volume grow? | Fast, with metric-heavy workloads | Predictable and modest |
| Which signals do you need together? | Metrics first, others added later | Metrics, logs, traces and user monitoring from day one |
| How portable must you stay? | OpenTelemetry and open formats preferred | Convenience outweighs portability |
How do the cost and licensing models differ?
Prometheus and Grafana cost you engineering time and infrastructure, while Datadog charges for usage across several separate dimensions. Neither is automatically cheaper. The cheaper option is the one whose cost drivers match how your estate grows, and that needs to be modelled against your own numbers rather than a headline ratio.
With Prometheus, the software is open source under the Apache 2.0 licence, so there is no licence fee. You pay for compute, block storage or object storage, network transfer, and above all for the people who run it. Grafana can be self-hosted or taken as a managed service, and managed Prometheus offerings from cloud providers and vendors sit between the two models, trading some control for less operational work.
Datadog is commercial SaaS. Its charges are driven by items such as monitored hosts or containers, custom metrics, ingested and indexed logs, and tracing volume, and each product line is priced separately. That makes the bill a function of engineering decisions: adding a high-cardinality tag or logging at debug level in production changes cost directly. Ask for current pricing from the vendor and review contract terms such as commitment and overage handling before relying on any estimate.
What does it take to migrate or switch?
Switching in either direction is a phased project rather than a cutover, and the safest route is to run both systems side by side until alerts and dashboards agree. Because OpenTelemetry is a vendor-neutral instrumentation standard, instrumenting applications with it first keeps the later choice of backend cheaper.
Moving from Datadog to Prometheus means rebuilding dashboards and monitors as PromQL queries and Alertmanager rules, replacing agent integrations with exporters, and choosing separate components for logs and traces, for example Loki and Tempo. The query languages differ, so expect manual translation rather than automatic conversion. Plan time for on-call routing, runbook links and silence policies, which are often undocumented.
Moving from Prometheus to Datadog is usually simpler on the collection side, since the Datadog Agent can scrape Prometheus-format endpoints. The harder part is governance: deciding which metrics deserve to be shipped, so that the bill does not grow with every exporter you previously ran for free. Many teams keep a small self-hosted Prometheus for cluster-level metrics while using SaaS for traces and user monitoring, which is a legitimate end state.
What are the common pitfalls with each option?
The recurring failure with Prometheus is under-investing in operations, and the recurring failure with Datadog is under-investing in cost governance. Both are avoidable with early decisions rather than late clean-ups.
On the self-hosted side, teams commonly run a single Prometheus replica with default retention and then discover the gap when a node is lost. Others let label cardinality grow unchecked, so memory use climbs until the server restarts. Alert rules written without ownership produce noise, and noisy alerts train people to ignore pages.
- Prometheus: no high availability or remote storage plan, unbounded label cardinality, alerts with no runbook or owner.
- Prometheus: treating Grafana dashboards as click-built artefacts instead of version-controlled code.
- Datadog: custom metrics and tags added without review, logs indexed that nobody searches, and no budget alerting on usage.
- Datadog: assuming one product covers every signal well, then paying for overlapping tools anyway.
- Both: no service level objectives, so alerts follow symptoms of infrastructure rather than user experience.
How would a neutral consultant approach this choice?
We would start from your constraints and not from a preferred tool. In practice that means a short assessment of your current alerting, on-call load, data handling obligations and estimated telemetry volume, followed by a written recommendation with the trade-offs stated plainly. Where a hybrid is the honest answer, we say so.
Delivery follows how we work on other engagements: fixed scope agreed in writing, a named senior engineer, and everything built in your own accounts and repositories so you are not locked to us either. If you choose Prometheus, that includes alert rules, dashboards and runbooks stored as code. If you choose Datadog, it includes tagging standards and usage controls so the bill stays understandable.
Frequently asked questions
Can we use Prometheus and Datadog together?
Is Prometheus suitable for long-term metric storage on its own?
Does choosing OpenTelemetry remove the need to pick a vendor?
How do we stop a SaaS monitoring bill growing unexpectedly?
How long does it take to move from one to the other?
Need help setting up monitoring?
Whether you are setting up Prometheus from scratch or trying to reduce a Datadog bill, we can help design and implement the right observability stack for your environment.