Grafana vs Prometheus: Key Differences and Best Use Cases

Grafana and Prometheus are two of the most widely used tools in open-source monitoring, but they are often compared in the wrong way. The question is not simply Grafana vs Prometheus as if both tools do the same job. Prometheus is mainly used to collect, store, query, and alert on metrics. Grafana is mainly used to visualize and explore data from Prometheus and other data sources through dashboards, panels, and alerts.

In a typical monitoring setup, Prometheus collects time-series metrics from applications, infrastructure, Kubernetes clusters, and exporters. Grafana then connects to Prometheus as a data source and turns those metrics into dashboards that engineering, DevOps, platform, and SRE teams can use during day-to-day monitoring and incident response. This is why Grafana and Prometheus are usually used together rather than treated as direct replacements.

That said, the combined Grafana Prometheus stack has limits. It is strong for metrics monitoring and dashboarding, but production observability often needs more than metrics. Teams also need logs, traces, longer retention, SQL-based analysis, AI-assisted investigation, and a cleaner way to correlate signals across services.

This guide explains the key differences between Grafana and Prometheus, how they work together, when to use each tool, and where Parseable fits when teams need a unified observability platform for logs, metrics, and traces.

Grafana vs Prometheus: Quick comparison

The simplest way to understand Grafana vs Prometheus is this:

Prometheus is the metrics backend, while Grafana is the visualization and exploration layer. Prometheus collects and stores time-series metrics.

To help you understand it in much more detail, here’s a quick comparison table:

In practice, most teams do not choose only one. They use Prometheus monitoring to collect and query metrics, then use Grafana dashboards to visualize those metrics and share them across engineering, platform, DevOps, and SRE teams.

Prometheus also has its own alerting model. Alerting rules are defined in the Prometheus server, while Alertmanager handles notification routing, grouping, silencing, inhibition, and deduplication.

Grafana, on the other hand, can create and manage alerts across connected data sources, which makes it useful when teams want alerting workflows tied to dashboards and multiple observability backends.

Grafana and Prometheus work well when the main workflow is metrics collection and dashboarding. But once teams need to correlate Prometheus metrics with logs and traces, query telemetry with SQL, or reduce the number of separate observability backends, Parseable becomes a stronger long-term layer.

If your Grafana Prometheus setup is starting to need logs, traces, PromQL, SQL, and longer-term telemetry context in one place, sign up for Parseable and test the workflow with your existing Prometheus-compatible metrics.

What is Grafana?

Grafana is an open-source visualization, dashboarding, alerting, and data exploration platform. In a Grafana vs Prometheus comparison, Grafana is best understood as the visual layer that helps teams turn observability data into dashboards, panels, alerts, and exploratory views.

Grafana can query data from different backends and show it in one interface. That includes Prometheus metrics, Loki logs, Tempo traces, CloudWatch data, Elasticsearch, SQL databases, and other connected sources.

What Grafana is used for

Grafana is commonly used to:

• Build monitoring dashboards for applications, infrastructure, Kubernetes clusters, databases, APIs, and cloud services.
• Visualize Prometheus metrics in a Grafana Prometheus setup, where Prometheus collects the metrics and Grafana turns them into readable dashboards.
• Track key system signals such as CPU usage, memory consumption, request latency, error rates, throughput, queue depth, and service availability.
• Explore metrics, logs, and traces during incidents without building a dashboard first.
• Connect multiple observability sources such as Prometheus, Loki, Tempo, CloudWatch, Elasticsearch, and SQL databases.
• Create shared operational views for SRE, DevOps, platform, and engineering teams.
• Set up alerts based on connected data sources, so teams can detect issues before they become larger incidents.

For teams that are already running into dashboard sprawl, the conversation often shifts from using more panels to choosing a cleaner observability backend. That is why teams looking beyond Grafana usually evaluate alternatives based on telemetry coverage, retention, and backend complexity.

What is Prometheus?

Prometheus is an open-source monitoring system and time series database built for collecting, storing, querying, and alerting on metrics. In a Grafana vs Prometheus comparison, Prometheus is best understood as the metrics backend that collects time-series data from applications, services, infrastructure, and exporters.

Prometheus uses a pull-based model, where it scrapes metrics from configured targets at regular intervals. Teams then use PromQL, the Prometheus Query Language, to analyze metrics, create alerting rules, and understand system behavior over time.

What Prometheus is used for

Prometheus is commonly used to:

• Collect time-series metrics from applications, Kubernetes clusters, infrastructure, databases, APIs, and services.
• Monitor cloud-native systems where services expose metrics endpoints that Prometheus can scrape.
• Query metrics with PromQL to analyze CPU usage, memory usage, request latency, error rates, throughput, saturation, and availability.
• Create metric-based alerts for conditions such as high error rates, latency spikes, failed jobs, or resource exhaustion.
• Use exporters to collect metrics from systems that do not expose Prometheus metrics natively, such as Linux hosts, databases, load balancers, and message queues.
• Power Grafana dashboards by acting as the data source behind visual panels and operational views.
• Support Kubernetes monitoring by scraping metrics from pods, nodes, services, and cluster components.

When Prometheus becomes part of a larger platform decision, teams usually compare it against broader open-source observability platforms that can handle more than metrics alone.

Grafana vs Prometheus: key differences

Grafana is a visualization layer, Prometheus is a metrics backend

Grafana helps teams see and understand observability data. It connects to Prometheus and other data sources, then turns that data into dashboards, charts, tables, alerts, and exploratory views.

Prometheus is the system that collects and stores metrics. It scrapes metrics from configured targets, stores them as time series data, and lets teams query them with PromQL.

In a common Grafana Prometheus setup, Prometheus handles the metrics pipeline, while Grafana handles the visual layer.

Grafana supports many data sources, Prometheus focuses on metrics

Grafana can connect to many observability backends. Teams can use it with Prometheus, Loki, Tempo, CloudWatch, Elasticsearch, SQL databases, and other systems.

Prometheus is more focused. It is built for metrics and time series monitoring. That makes it a strong fit for infrastructure metrics, Kubernetes metrics, service-level metrics, and application performance signals such as latency, traffic, errors, and saturation.

This is why Grafana is often used as the front end for multiple systems, while Prometheus is used as the metrics source behind many Grafana dashboards.

Prometheus uses PromQL, while Grafana depends on the data source

Prometheus uses PromQL, the Prometheus Query Language. Teams use PromQL to filter, aggregate, and analyze time series metrics.

Grafana does not have one universal query language for every workflow. The query language depends on the data source connected to Grafana. If Grafana is connected to Prometheus, teams write PromQL. If it is connected to Loki, they use LogQL. If it is connected to a SQL database, they write SQL.

This flexibility is useful, but it can also create query-language sprawl when teams use many backends.

Prometheus handles metric scraping, Grafana does not

Prometheus actively collects metrics. It uses a pull-based model where the Prometheus server scrapes metrics from configured targets at regular intervals.

Grafana does not scrape metrics from applications by itself. It queries data that already exists in connected data sources. This is why Grafana needs a backend such as Prometheus, Mimir, Loki, Tempo, CloudWatch, Elasticsearch, or a database to show meaningful data.

Grafana is better for dashboards, Prometheus is better for metrics collection

Grafana is the stronger choice when the goal is dashboarding and visualization. It gives teams flexible panels, shared dashboards, alert views, and exploration workflows.

Prometheus is the stronger choice when the goal is collecting and querying metrics. It is built for metric scraping, time series storage, PromQL queries, and alerting rules.

For most teams, the answer is not Grafana or Prometheus. It is Grafana with Prometheus. Prometheus collects and stores metrics, while Grafana makes those metrics easier to understand and act on.

Where the Grafana and Prometheus stack becomes harder to operate

A Grafana Prometheus setup is a strong starting point for metrics monitoring. But as teams scale their observability stack, the combination can become harder to operate.

Here are some of the situations where managing this stack becomes difficult:

Metrics alone do not explain every incident

Prometheus monitoring is strong for metrics such as CPU usage, memory usage, request latency, error rates, throughput, and saturation. These signals help teams detect that something is wrong.

But metrics do not always explain why something is wrong.

A latency spike may appear in Prometheus. Grafana dashboards may show that the issue started after a deployment. But the root cause may still require application logs, distributed traces, deployment events, or error payloads. At that point, the team has to move outside the basic Grafana and Prometheus workflow to find the missing context.

This is where the Grafana vs Prometheus comparison becomes too narrow. For production debugging, teams usually need metrics, logs, and traces together.

Query language fragmentation increases

In a simple Grafana Prometheus setup, teams mostly work with PromQL. That is manageable when metrics are the main signal.

The workflow changes when the observability stack expands. Prometheus uses PromQL. Loki uses LogQL, which creates a separate workflow for teams comparing log backends such as Loki and Parseable. SQL databases use SQL. Trace backends may use their own query model. Grafana can visualize all of these sources, but each backend still has its own query language and data model.

This creates query-language sprawl. During incidents, engineers may need to move between PromQL for metrics, LogQL for logs, SQL for stored events, and another workflow for traces. Grafana gives teams a common interface, but it does not always remove the complexity underneath.

Long-term retention becomes a separate architecture problem

Prometheus is useful for recent operational metrics, but long-term metric retention often needs extra planning. Prometheus stores time series data locally and provides retention settings for controlling how long data remains on disk. For longer history or larger deployments, teams often use remote storage, remote write, or additional systems around Prometheus. Prometheus documentation also notes that remote write can increase memory usage depending on the shape of the data.

That means long-term retention is not only a dashboarding question. It becomes an architecture question. Teams need to decide where old metrics live, how much data to retain, how queries perform over longer windows, and how the backend scales as cardinality and metric volume grow.

The storage format also matters, especially when teams want telemetry data to stay queryable without locking it into a proprietary backend, which is why Parquet-based observability storage becomes relevant at scale.

Grafana adds visibility, but not always simplification

Grafana is valuable because it can bring many sources into one visual layer. Its data source model lets teams connect to backends that store metrics, logs, traces, and profiles, then visualize them in dashboards and Explore.

But visual unification is not the same as architectural simplification. A dashboard may show metrics from Prometheus, logs from Loki, traces from Tempo, and cloud data from another source. That gives teams visibility, but the data still lives in separate backends with separate retention models, query languages, scaling requirements, and access controls.

For smaller teams, that may be acceptable. For teams running production systems at scale, this can create operational overhead across the entire observability stack.

Tool sprawl becomes the real problem

The real limitation is not that Grafana or Prometheus are weak tools. Both are strong at what they are designed to do. The problem is that production observability often grows into a stack of separate systems.

A team may start with Prometheus monitoring and Grafana dashboards. Then they add Loki for logs, Tempo or Jaeger for traces, Alertmanager for notifications, remote storage for long-term metrics, and another system for deeper log or event analysis.

Each tool solves a specific problem, but the stack becomes harder to operate as a whole. Teams need to manage upgrades, integrations, retention, permissions, query languages, cost, and incident workflows across multiple systems.

That is the point where a Grafana vs Prometheus discussion needs a third option: a more unified observability layer that can keep metrics, logs, and traces connected from the start.

A better long-term observability layer: Parseable

A Grafana Prometheus setup works well when the main requirement is metrics collection and dashboarding. Prometheus gives teams PromQL for metrics, while Grafana turns those metrics into dashboards, alerts, and operational views.

Parseable does not replace that workflow by forcing teams into a different query model. Instead, it extends it.

Teams can keep PromQL for Prometheus-style metrics analysis, use SQL for deeper telemetry investigation, and bring logs, metrics, and traces into one observability layer. That makes Parseable a better fit when the stack starts moving beyond metrics dashboards and into full production observability.

Ingest Prometheus metrics into Parseable

Parseable supports ingesting metrics from Prometheus and Prometheus-compatible sources. Teams can send Prometheus metrics into Parseable using native Prometheus remote write, Fluent Bit with OpenTelemetry output, or the OpenTelemetry Collector with the Prometheus receiver.

For teams already using Prometheus monitoring, remote write is the most direct path:

Applications / Exporters → Prometheus → Parseable

Read our docs for more information.

Teams can also use the OpenTelemetry Collector to scrape Prometheus metrics and export them to Parseable:

Applications / Exporters → OpenTelemetry Collector → Parseable

This gives teams a clean path from a Grafana Prometheus setup into Parseable. They can keep the metric collection patterns they already use while moving the storage and analysis layer into a broader observability platform.

Read our docs for more information.

Query metrics with PromQL and SQL

This is the key difference in Parseable’s positioning. Parseable is not only a SQL layer for telemetry, and it is not only a PromQL layer for metrics. It supports both.

Teams can use PromQL when they want familiar Prometheus-style metric analysis:

rate(http_requests_total[5m])

They can also use PromQL for latency and histogram analysis:

histogram_quantile(0.99, sum(rate(http_request_duration_seconds_bucket[5m])) by (le, job))

At the same time, teams can use SQL when they need broader telemetry analysis across datasets, fields, logs, and operational events. This is useful when a metric shows the symptom, but logs or traces explain the cause.

A practical example:

SELECT service_name, COUNT(*) AS error_count
FROM application_logs
WHERE level = 'error'
GROUP BY service_name
ORDER BY error_count DESC;

PromQL answers metric questions well. SQL helps teams ask broader investigation questions across telemetry data. Parseable gives teams both in the same platform.

Use Grafana with Parseable

Parseable can also work with Grafana, which makes the migration path less disruptive for teams already using Grafana dashboards.

Teams can query Parseable metrics from Grafana in two ways:

• Grafana’s built-in Prometheus data source
• The Parseable Grafana plugin

With Grafana’s built-in Prometheus data source, teams can point Grafana to a Parseable Prism node and query metrics through Parseable’s Prometheus-compatible API. This is useful when teams want to preserve existing Grafana workflows while using Parseable as the backend for Prometheus-compatible metrics.

For teams with multiple datasets or mixed telemetry signals, the Parseable Grafana plugin is the better fit. It lets users switch between datasets and write PromQL queries directly in the editor. It also supports building dashboards from logs, traces, and metrics together.

That means teams do not have to choose between Grafana and Parseable. Grafana can remain the visualization layer, while Parseable becomes the unified telemetry backend.

Keep metrics connected to logs and traces

The bigger advantage of Parseable is not only that it supports PromQL and SQL. The bigger advantage is that metrics do not have to live separately from logs and traces.

In a traditional Grafana Prometheus stack, Prometheus handles metrics. Logs often move to Loki or another log backend. Traces may move to Tempo, Jaeger, or another tracing system. Grafana can visualize these sources together, but the data still lives across separate systems.

Parseable changes that model by giving teams one platform for logs, metrics, and traces. Prometheus-compatible metrics can sit beside application logs, traces, alerts, dashboards, and AI-assisted investigation workflows.

This matters during incidents. PromQL can show that latency increased. SQL can help inspect related logs. Traces can show which service path slowed down. Grafana can still visualize the data, but Parseable keeps the underlying telemetry connected.

Keep the parts of Grafana and Prometheus that already work

Parseable should not be positioned as a hard replacement for Grafana and Prometheus in every environment. A better framing is that Parseable helps teams keep the workflows that work while reducing the complexity around them.

Teams can keep PromQL. They can keep Grafana dashboards. They can keep Prometheus-compatible ingestion. But instead of expanding into a fragmented stack of separate backends for metrics, logs, and traces, they can use Parseable as the unified observability layer underneath.

That is the practical difference: Grafana and Prometheus are strong for metrics monitoring and visualization. Parseable is stronger when teams want PromQL, SQL, logs, metrics, traces, dashboards, and AI-assisted analysis in one telemetry platform.

If you want to keep PromQL and Grafana while reducing the backend complexity around them, sign up for Parseable and connect your Prometheus-compatible metrics to a unified telemetry platform.

Reduce tool sprawl across metrics, logs, and traces

Grafana plus Prometheus is a strong combination for metrics dashboards. But production observability usually needs more than metrics. Teams also need logs, traces, dashboards, alerts, anomaly detection, retention, AI-assisted investigation, and flexible querying across different telemetry datasets.

This is where a traditional Grafana Prometheus stack can start to expand into multiple tools. Prometheus handles metrics. Loki may handle logs. Tempo or Jaeger may handle traces. Grafana sits on top for visualization. Another backend may be added for long-term retention, and another workflow may be needed for deeper analysis.

Each tool solves a real problem, but the stack becomes harder to operate as a whole.

Parseable gives teams a more complete foundation by bringing logs, metrics, and traces into one observability platform. Teams can ingest Prometheus-compatible metrics, query metrics with PromQL inside Parseable, use SQL for deeper telemetry analysis, build dashboards and alerts, and keep incident context connected across signals.

This does not mean teams have to stop using Grafana. Grafana can still be the visualization layer where it already works. The difference is that Parseable can become the unified telemetry backend underneath, reducing the need to maintain a separate backend for every signal.

Grafana vs Prometheus vs Parseable

Grafana, Prometheus, and Parseable solve different levels of the observability problem. Grafana helps teams visualize data. Prometheus helps teams collect and query metrics. Parseable helps teams bring logs, metrics, and traces together with PromQL, SQL, dashboards, alerts, and AI-assisted analysis.

The practical takeaway is simple. Choose Parseable when the team needs a broader observability layer that connects metrics with logs, traces, dashboards, alerts, retention, SQL, PromQL, and AI-assisted investigation.

Choose Grafana when the main need is visualization. Choose Prometheus when the main need is metrics collection and PromQL-based monitoring.

Conclusion

Grafana and Prometheus are both excellent tools, but they solve different parts of the monitoring problem. Prometheus is built for collecting, storing, querying, and alerting on metrics. Grafana is built for turning that data into dashboards, panels, alerts, and shared operational views.

For many teams, the right answer is not Grafana vs Prometheus. It is Grafana with Prometheus.

As production systems grow, metrics dashboards are only one part of the observability workflow. Teams also need logs, traces, long-term retention, SQL, PromQL, anomaly detection, alerts, and AI-assisted investigation in one place.

Parseable lets teams ingest Prometheus-compatible metrics, query them with PromQL, analyze telemetry with SQL, and keep logs, metrics, and traces connected in one observability platform. Teams can also continue using Grafana where it already works, while using Parseable as the backend for unified telemetry and deeper investigation.

If your team already uses Grafana and Prometheus but is starting to feel the limits around logs, traces, retention, or query sprawl, Parseable is the next logical step. Sign up for Parseable and bring metrics, logs, traces, PromQL, SQL, dashboards, alerts, and AI-assisted investigation into one platform.