Grafana vs Prometheus: Key Differences and When to Run Both

Grafana and Prometheus solve different parts of the observability workflow, so they’re more complementary than alternatives. Prometheus collects, stores, and queries metrics. Grafana visualizes data from Prometheus and dozens of other sources. The real decision is not Grafana or Prometheus in isolation, but when to use each, when to run both, and when your stack has outgrown that setup.

This guide covers what each tool does, the key differences between them, and how to tell whether your environment calls for one, both, or a different backend entirely.

What Is Grafana?

Grafana is a visualization and alerting layer that queries external backends, shipping as open source for self-hosting or as the managed Grafana Cloud service. It connects to storage backends like Prometheus, Loki, structured query language (SQL) databases, and cloud monitoring services, querying each through its own plugin and rendering the results across panel types, so data from multiple backends lands on a single dashboard without those backends knowing about each other.

In most organizations it ends up as the shared front end for monitoring and incident response, with engineering, platform, and site reliability engineering (SRE) teams building dashboards over whatever backends already exist.

What Is Prometheus?

Prometheus is an open-source monitoring and alerting toolkit for metrics. The project started at SoundCloud and later graduated within the Cloud Native Computing Foundation (CNCF). Prometheus pulls metrics from configured targets over Hypertext Transfer Protocol (HTTP) on a defined scrape interval, stores those samples in a local time-series database (TSDB), and evaluates alerting rules with PromQL (Prometheus Query Language). Every Prometheus server operates as an autonomous single-node system with no reliance on distributed storage.

In practice, Prometheus is the default metrics backend of cloud-native stacks. Applications expose metrics endpoints for it to scrape, exporters translate metrics from systems that expose nothing natively, such as Linux hosts, databases, and message queues, and the resulting time series feed both alerting rules and whatever dashboard layer sits on top.

Grafana vs. Prometheus: Key Differences

The technical boundary between the two tools sits at the query application programming interface (API). Prometheus collects and stores queryable metrics and exposes an HTTP API; Grafana calls that API and owns rendering, dashboarding, and alert management.

In practice the boundary assigns operational ownership: storage and collection fall to whoever operates Prometheus, and presentation falls to whoever operates Grafana.

Dimension	Prometheus	Grafana
Primary role	Metrics collection, local TSDB storage, PromQL evaluation, alerting rule execution	Visualization, multi-source dashboarding, alert management UI
Data collection	Pull-based HTTP scrape; Pushgateway for batch jobs	None natively; collection requires a separate component
Storage model	Single-node TSDB; time-based blocks; 15-day default retention	None in open source; cloud deployments depend on external backends
Query language	PromQL (native, full-featured); HTTP API at /api/v1	Passes queries to data sources in native syntax; Grafana expressions for cross-source transforms
Alerting	PromQL rules evaluated against local TSDB, routed to Alertmanager	Multi-source alert rules with internal or external Alertmanager
Visualization	Expression browser for ad-hoc PromQL only	Full panel-based dashboard interface; broad data-source support; role-based access control (RBAC) and sharing
Scaling pattern	Vertical (single node); federation; remote write to external backends	Stateless UI; scale depends on connected storage backends
Setup	Single binary or container image; configuration in YAML	Binary, container, or managed Grafana Cloud; configured through the web interface
Pricing model	Apache 2.0; free; infrastructure cost only	Open source available; hosted pricing varies by deployment model

Prometheus owns collection, storage, and rule evaluation, while Grafana owns presentation and cross-source alerting. Neither tool absorbs the other’s job, and the differences below unpack where that division helps and where it strains.

Data Collection

Prometheus uses a pull model where the server initiates HTTP requests to configured targets on a defined scrape interval. The server discovers targets automatically through kubernetes_sd_configs, Consul, domain name system (DNS), Elastic Compute Cloud (EC2), or static configuration.

When a pod crashes, Prometheus records a staleness marker at the next scrape interval instead of silently dropping metrics, so the monitoring system controls scrape timing and target inventory.

Grafana collects nothing natively. Every panel depends on a configured data source, and each data-source plugin presents query options suited to the backend it targets, such as PromQL for Prometheus or SQL for relational databases. A production architecture can also place an OpenTelemetry (OTel) Collector at the collection layer, remote-writing metrics to a backend that scales horizontally on object storage.

Storage and Retention

Prometheus writes ingested samples to a local TSDB that groups data into two-hour blocks, compacts those blocks in the background, and stores them on disk at an average of one to two bytes per sample. Default retention is 15 days, and the practical ceiling is local SSD capacity. As local retention grows, query performance can degrade because scattered file access competes with active ingestion and compaction.

Grafana stores no telemetry at all. Its retention story is whatever the connected backends provide, which is why long-retention requirements are a backend decision, not a dashboard decision.

Query Language and APIs

PromQL lets SREs express multi-dimensional, rate-based, and aggregated conditions using the same language for dashboards, ad-hoc debugging, and alerting. Grafana passes queries through to each data source in its native syntax and layers its own expressions on top for cross-source transforms. PromQL also outlives the Prometheus server itself, because backends that implement the Prometheus query API accept it unchanged.

Coralogix is one of those backends: a managed observability service for logs, metrics, and traces that accepts your existing PromQL through the Prometheus query API. The dashboards and alerting rules your team already maintains carry over without a rewrite, which removes the largest migration cost when a Prometheus deployment hits its limits.

Alerting

In a Prometheus deployment, PromQL alerting rules are evaluated against the local TSDB, and the same expression that flags a symptom during incident investigation can be promoted directly to an alerting rule without translation. Alertmanager handles the notification pipeline downstream with deduplication, grouping, routing, inhibition, and silencing.

Grafana Alerting supports alert rules that query more than one data source. A single Grafana-managed rule can combine queries from different backends, apply reduce and math expressions to set the firing condition, and route notifications through shared contact points. During real incidents, an alert often needs to correlate a metric threshold with a pattern in logs, and Grafana lets teams manage that in one interface instead of splitting the workflow across tools. Coralogix takes that consolidation one step further by evaluating alerts against logs, metrics, and traces in the same backend, so correlation happens in the data layer instead of the dashboard.

Visualization

Prometheus ships only an expression browser scoped to ad-hoc PromQL debugging, with no saved dashboards or team sharing. For real dashboards, the Prometheus project itself points users to third-party tools such as Grafana.

Grafana covers what that interface leaves out: persistent panels, multi-source correlation, role-based access control, and shared exploration workflows. Engineers can query and aggregate data in Explore without building a dashboard first, and dashboards can be defined as code and provisioned from version control, which gives distributed teams reviewable changes instead of dashboards that drift through untracked edits. Coralogix sits on both sides of that divide, shipping its own dashboard interface while also exposing its data to Grafana as a standard data source.

Scaling and Cardinality

Prometheus scales vertically, and its single-node TSDB hits a label cardinality wall that is predictable and well-documented. Maintainers suggest planning around a few million active time series per server, and high-cardinality environments such as large multi-tenant Kubernetes fleets reach that ceiling in practice. Teams respond by adding a remote storage backend or moving metrics to a store that scales horizontally before the single node becomes the bottleneck.

Coralogix is one such backend: it processes metrics in-stream and bills on data ingested, with no per-host or per-series charges, so rising cardinality stops acting as a cost multiplier even as the active series count climbs. Grafana itself is largely outside this problem, since its stateless UI scales with whatever backends sit behind it.

Pricing and Operating Cost

Prometheus carries an Apache 2.0 license with no network copyleft obligations, so the bill is infrastructure plus the engineering hours spent operating remote storage, managing object storage, and monitoring the monitoring stack.

Hosted observability pricing shifts that cost to usage-based vendor billing, and per-host or per-user pricing adds a third axis where the bill climbs with fleet size even when data volume stays flat. Ingestion-only models remove that axis; Coralogix, for example, bills per gigabyte ingested with unlimited users and hosts on every plan. The right call depends on which cost you want to carry: predictable infrastructure plus headcount, or vendor billing that tracks usage.

When to Choose Grafana

If you already run separate backends for metrics, logs, and traces, Grafana pulls all of them into one dashboard without requiring a shared storage layer, which cuts the context switching that slows an investigation down.

It is also the safer default when the team mix is broad. Dashboards are shared, access-controlled assets, alert rules can span backends, and adopting Grafana commits you to nothing on the storage side, since every backend stays swappable behind its plugin. That swap-friendly design covers managed backends too: Coralogix exposes its logs, metrics, and traces to Grafana through a dedicated data source plugin, so changing the storage layer later doesn’t cost you the dashboards. Teams that expect their backend lineup to change pick the front end first for exactly that reason.

When to Choose Prometheus

Prometheus is the pick when the problem is Kubernetes-native metrics collection and alerting. Its service discovery finds pods, services, nodes, and endpoints through the Kubernetes API with no manual target lists, and in the 2025 CNCF Annual Cloud Native Survey it runs in 77 percent of organizations, which keeps the exporter and tooling ecosystem ahead of any rival metrics backend.

Single-node autonomy is the other deciding factor. Because each server stands alone, monitoring stays available even when the infrastructure around it is failing, which is precisely when you need it most. The tradeoff sits at the margins of precision: Prometheus is not built for 100 percent accuracy use cases like per-request billing, where collected data may not be detailed or complete enough. Choosing Prometheus today also doesn’t lock the stack in, because backends like Coralogix accept its remote write output when collection needs to outlive the single node.

When to Run Both

Running Prometheus for collection and Grafana for visualization is the standard pairing, and teams with metrics-first use cases and short retention windows can run it for years without hitting a wall.

The integration itself is one configuration step: Grafana adds Prometheus as a data source pointing at its HTTP API, and every dashboard panel then queries PromQL through that connection. If your active series count stays under a few million per instance and retention needs fit within 15 to 30 days, the open-source stack delivers strong value at the cost of infrastructure and engineering time.

Projects such as Thanos, Cortex, Mimir, and VictoriaMetrics extend Prometheus through remote storage and object-backed retention, giving the pairing incremental scaling paths when the first capacity boundary approaches. Each option adds components to deploy, upgrade, and monitor, so the extension path trades one ceiling for more moving parts. A managed backend such as Coralogix delivers the same headroom without another distributed system for your team to operate.

When to Consolidate on Coralogix

The pairing stops fitting when the questions stop being metrics-only. Displaying logs and traces alongside metrics in Grafana means operating a separate backend and data source for each signal, often with a different query language, and that operational overhead becomes the main architectural constraint. The same trigger fires on cost: when per-host pricing or retention spend breaks the budget, the backend decision reopens.

Coralogix consolidates logs, metrics, and traces into one observability backend while keeping the parts of your stack that work. Existing PromQL dashboards and alert rules carry over through the Prometheus query API, Prometheus servers keep collecting and remote-write their samples to Coralogix, and Grafana keeps querying everything through the data source plugin introduced above. The migration work sits in the storage path, not in rewriting queries, dashboards, or instrumentation.

The pricing model changes with the architecture. Coralogix bills per gigabyte ingested instead of per host, per user, or per series, and its TCO Optimizer routes logs, metrics, and traces into the Frequent Search, Monitoring, Compliance, and Blocked pipelines, so you index only high-value data and keep the rest queryable without paying to index it. The data lands in customer-owned object storage such as Amazon Simple Storage Service (S3) in open Parquet format, and that archive stays queryable from Coralogix without rehydration fees.

Coralogix gives you full-stack observability, rich correlation, and unlimited retention without a dashboard rewrite. If your Prometheus stack is closing in on its cardinality or retention ceiling, try Coralogix for free and test it against your own production data on a 14-day trial.

Frequently Asked Questions About Grafana vs. Prometheus

Can Grafana work without Prometheus?

Grafana can work with dozens of supported data sources without Prometheus in the stack. It can sit in front of log stores, SQL databases, cloud monitoring services, and other telemetry backends, querying each through its own data-source plugin and combining them on one dashboard. Coralogix is among those backends, exposed to Grafana through its own data source plugin.

Does Prometheus include its own dashboard?

Prometheus ships with a built-in expression browser at the /graph endpoint that supports ad-hoc PromQL queries with table and graph output. You can use it for debugging, and Grafana or console templates are recommended for production dashboarding because the expression browser has no saved dashboards, sharing, or access controls.

Can you keep using PromQL after moving metrics off Prometheus?

Usually, yes. Backends that implement the Prometheus query API accept your existing PromQL, so dashboards and alerting rules carry over without a rewrite, and Coralogix supports this path through its Prometheus query API compatibility. The migration work sits in the collection and storage path, not the query layer, and Coralogix pricing shows what that data volume would cost under ingestion-based billing.

How long can Prometheus realistically store data?

Prometheus retention is configurable, with a 15-day default set via storage.tsdb.retention.time, and the practical limit is available SSD storage instead of a software setting. As local retention grows, query performance can degrade because scattered file access across disk competes with active ingestion and compaction, which is why long-retention use cases usually move older data to a remote store. Coralogix handles that tier with remote, index-free querying of data archived in your own bucket, so retention length stops being a disk-capacity question.

Is OpenTelemetry replacing Prometheus or Grafana?

OpenTelemetry is a vendor-neutral instrumentation and collection standard, not a replacement for Prometheus storage or Grafana visualization. Prometheus 3.0 ships native OpenTelemetry Protocol (OTLP) ingestion, so teams can instrument with OTel SDKs and send metrics directly to an existing Prometheus server, and Grafana deployments sit alongside OTel-based collection paths. Coralogix ingests OTLP natively as well, and its OpenTelemetry documentation covers the collector setup.