Grafana vs Prometheus: Which is Better in 2026?
Grafana and Prometheus are not competitors: they are complementary layers of the same observability stack, and the "vs" framing is a common beginner mistake. Prometheus is a pull-based metrics collection engine and time-series database (TSDB) with its own query language (PromQL). Grafana is a visualization and dashboarding layer that queries backends -- Prometheus being the most common -- and renders the results. The real question is not which one to choose, but when you need each, when Prometheus alone is enough, and when Grafana Cloud (the managed SaaS product that bundles Grafana with Loki, Mimir, and Tempo) changes the calculus. This comparison is for teams setting up observability for the first time, evaluating whether Grafana Cloud is worth paying for, or deciding how much of the LGTM stack (Loki, Grafana, Tempo, Mimir) to operate themselves.
Bottom line: Grafana is our overall pick for monitoring workflows. Pick Prometheus if you need a fully free option.
Short on time? Here's the quick answer
We've tested both tools. Here's who should pick what:
Grafana
Observability and visualization platform
Best for you if:
- • Open-source visualization for metrics
- • Beautiful dashboards from any data source
Prometheus
Monitoring and alerting toolkit
Best for you if:
- • You need something completely free
- • Prometheus is an open-source monitoring system with a time-series database
- • It collects metrics, provides alerting, and powers Grafana dashboards
| At a Glance |  Grafana |  Prometheus |
|---|---|---|
Starts at | FreeFree tier available | FreeFree tier available |
Best For | Monitoring | Monitoring |
Rating | 4.5/5 | 4.3/5 |
Choose Grafana or Prometheus?
Choose Grafana if
Observability and visualization platform
- Powerful dashboards
- Many data sources
- Open source
Choose Prometheus if
Monitoring and alerting toolkit
- Industry standard
- Great for metrics
- Open source
- You want a fully free tool (Grafana requires payment)
| Feature | Grafana | Prometheus |
|---|---|---|
| Pricing Model | Freemium | Free |
| User Rating | ★4.5/5 107 reviews | ★4.3/5 135 reviews |
| Categories | MonitoringData Visualization | MonitoringDevOps |
In-Depth Analysis
Grafana
Strengths
- +Data-source agnostic: a single Grafana instance queries Prometheus, CloudWatch, Elasticsearch, Postgres, InfluxDB, and 100+ other backends simultaneously, centralizing dashboards without migrating data
- +Grafana Cloud Free tier includes 10,000 active metrics series, 50 GB each of logs, traces, and profiles, and three users at no cost -- making it a realistic starting point for small teams without a credit card
- +The LGTM stack (Loki for logs, Grafana for visualization, Tempo for traces, Mimir for long-retention Prometheus-compatible metrics) provides a complete, open-source observability platform with a unified query experience
- +Scenes-powered dashboards (GA in Grafana 11.3) deliver more dynamic, flexible dashboard architecture with proper view/edit modes, sticky variable controls, and timezone-aware sharing
- +Grafana 12 (released 2025) adds built-in diagnostics, enhanced alerting with Grafana-managed recording rules, and role-based access controls for contact points and notification templates
- +Grafana Cloud Pro usage-based pricing (approximately $8 per 1,000 active series for metrics, $0.50/GB for logs and traces) runs roughly 50% less than Datadog at comparable usage profiles
Weaknesses
- -Grafana itself stores no data: it requires a backend data source for every signal type, which means operating (or paying for) Prometheus, Loki, Tempo, and Mimir separately if you want the full observability picture
- -Running the full LGTM stack self-hosted is an engineering project: Prometheus federation for multi-cluster, Thanos or Mimir for long-term storage, and Loki compaction all require dedicated SRE time to operate reliably
- -Grafana Cloud billing has multiple independent meters (per active series, per GB of logs, per GB of traces, per user) that can combine into bills two to five times the initial estimate at high data volumes
- -Native alerting lacks ML-based anomaly detection; Watchdog-equivalent functionality requires building PromQL expressions manually or integrating third-party tools
- -14-day data retention on the free tier means dashboards show only two weeks of history, which is insufficient for capacity planning or quarterly trend analysis without a paid upgrade
Best For
Teams that need to visualize data from multiple backends in one place, organizations standardizing on the LGTM stack for full-stack observability, and any team that wants a managed observability platform without Datadog's per-host pricing model.
Grafana is the de facto visualization layer for the open-source observability ecosystem. In 2026, with Grafana 12 shipping diagnostic tooling and Scenes-powered dashboards reaching maturity, the product has grown from a dashboard tool into a credible full-stack observability platform when paired with the LGTM backends. Grafana Cloud removes the heaviest operational burden at a fraction of Datadog's price. The catch is that Grafana alone does nothing: it needs a data source, and running those data sources reliably is where most of the operational complexity lives.
Prometheus
Strengths
- +Prometheus 3.0 (November 2024, now at v3.12 as of mid-2026) is the biggest release since 2.0 in 2017: native OTLP receiver accepts OpenTelemetry metrics directly at /api/v1/otlp/v1/metrics without routing through a collector, and Remote Write 2.0 delivers 60% fewer bytes over the wire, 70% less CPU, and 90% less memory allocation compared to Remote Write 1.0
- +PromQL is a purpose-built metrics query language with rate(), histogram_quantile(), and recording rules that generate alerts and precomputed aggregates directly inside the TSDB -- no external processing layer required
- +Pull-based scraping model makes Prometheus self-documenting: every scrape target is an explicit Prometheus config entry, making it easy to audit what is being monitored and at what interval
- +Native Kubernetes service discovery means Prometheus automatically discovers pods, services, and endpoints via the Kubernetes API without any additional configuration for standard exporters like kube-state-metrics and node-exporter
- +The exporter ecosystem is massive: 800+ community exporters cover hardware, databases, message queues, cloud APIs, and virtually every popular open-source project, all emitting metrics in the Prometheus exposition format
- +Apache 2.0 license with zero commercial restrictions -- Prometheus can be embedded in products, resold as part of managed services, or extended freely without legal uncertainty
Weaknesses
- -Prometheus is metrics-only: it does not handle logs, distributed traces, or continuous profiling. A complete observability stack requires Loki (logs), Tempo (traces), and Pyroscope (profiles) alongside it
- -Local TSDB storage is not designed for long-term retention: the recommended retention window is 15 days. Long-term storage requires Remote Write to Thanos, Cortex, Mimir, or Grafana Cloud -- all of which add operational complexity
- -Prometheus has no built-in visualization: PromQL queries are readable but raw. The built-in expression browser is adequate for debugging but not for production dashboards or alerting UIs
- -High-cardinality label sets (many unique label value combinations) cause TSDB pressure and memory spikes; teams often discover this limit only in production when a new deployment introduces thousands of pod-name label values
- -Horizontal scaling for high-ingestion environments requires either federation (limited, query-time fan-out) or an external system like Mimir or Thanos, which doubles the operational surface
Best For
Any team running Kubernetes or Linux infrastructure that wants open-source, self-hosted metrics collection; the foundation layer for every LGTM-based observability stack; and teams that need to embed metrics collection inside their own product without licensing concerns.
Prometheus is the standard metrics collection and short-term storage layer for cloud-native observability in 2026. The v3.x releases have closed the last major gaps: native OTLP ingestion, UTF-8 metric names, and Remote Write 2.0 make Prometheus a first-class OpenTelemetry destination. It is not a complete observability solution on its own -- it was never designed to be -- but as the metrics layer of a larger stack, it has no serious open-source competitor.
Head-to-Head Comparison
What Each Tool Actually Does
TiePrometheus collects, stores, and queries metrics via PromQL. Grafana queries data sources (including Prometheus) and renders dashboards, alerts, and explore views. They are not substitutes: removing Prometheus from a Grafana-based stack removes all metrics storage. Removing Grafana leaves Prometheus's own expression browser, which is functional for debugging but not production dashboards. Most teams run both.
When You Only Need Prometheus
Prometheus winsFor teams that primarily need alerting via Alertmanager and are comfortable writing PromQL directly, Prometheus alone is fully functional. Recording rules generate precomputed aggregates. Alerting rules fire to Alertmanager which routes to PagerDuty, Slack, or email. If the primary use case is alert-on-threshold rather than exploratory dashboard analysis, the overhead of running Grafana can be deferred until the team grows.
Visualization and Dashboards
Grafana winsGrafana is unambiguously superior for visualization. Prometheus's built-in expression browser shows raw line charts and tables; it has no dashboard persistence, no variables, no templating, and no multi-panel views. Grafana's Scenes-powered dashboards (GA in Grafana 11.3) support complex layouts, dynamic variables, and multi-source panels. For any production observability setup that humans will look at regularly, Grafana is not optional.
Alerting
TiePrometheus + Alertmanager is the mature, battle-tested alerting stack for metrics: rules are version-controlled YAML, Alertmanager handles deduplication, grouping, silencing, and routing. Grafana Alerting (unified alerting since Grafana 9) can also query Prometheus and fire alerts, and it supports non-Prometheus data sources. For multi-source alerting (alert on log volume AND metric threshold together), Grafana Alerting is necessary. For pure metrics alerting with GitOps discipline, Prometheus + Alertmanager is simpler to operate.
OpenTelemetry Integration
Prometheus winsPrometheus 3.0 ships a native OTLP receiver that accepts OTLP metrics at /api/v1/otlp/v1/metrics without requiring an OpenTelemetry Collector in the path. UTF-8 metric name support means OTel dot-separated names (e.g., http.server.request.duration) are stored natively without underscoring. Grafana Cloud also accepts OTLP across all signals (metrics, logs, traces), but Prometheus's metrics-layer integration is more direct and lower-latency for pure metrics pipelines.
Long-Term Storage and Scalability
Grafana winsPrometheus local TSDB is designed for 15-day retention. Beyond that, teams must Remote Write to Mimir, Thanos, or Cortex -- all of which are also part of the Grafana ecosystem. Grafana Cloud's Mimir backend provides effectively unlimited metrics retention at $8 per 1,000 active series/month with no storage management required. Self-hosted Mimir can retain years of metrics at near-zero marginal cost but requires dedicated SRE investment to operate reliably.
Logs and Traces
Grafana winsPrometheus is metrics-only by design: it has no log ingestion and no trace storage. Grafana's LGTM stack adds Loki (logs, queried with LogQL) and Tempo (distributed traces, queried with TraceQL), both open-source and Grafana Cloud-hosted. For teams that want a unified query experience across metrics, logs, and traces in a single UI, the Grafana ecosystem is the only open-source path.
Migration Considerations
There is no migration between Prometheus and Grafana because they are not alternatives -- they are complements. Teams typically start with Prometheus for metrics collection and add Grafana when dashboards become necessary. If you are evaluating Grafana Cloud as an alternative to self-hosting the full LGTM stack, the migration path is straightforward: configure Remote Write on your existing Prometheus instance to send metrics to Grafana Cloud's Mimir-compatible endpoint, and point Grafana to Grafana Cloud as its data source. Logs require deploying a Promtail or OpenTelemetry Collector agent to ship to Loki. Most teams complete this in under a week and retain their existing Prometheus exporters and recording rules unchanged.
Pricing: Grafana vs Prometheus
| Plan | Grafana | Prometheus |
|---|---|---|
| Tier 1 | Free Free | Free Free |
| Tier 2 | $19 month base Pro | N/A |
| Tier 3 | $25000 year minimum Enterprise | N/A |
Pricing verified from each vendor's public pricing page. Compare in detail on Grafana pricing and Prometheus pricing.
Who Should Use What?
On a budget?
Prometheus is free. Grafana is freemium.
Go with: Prometheus
Want the highest-rated option?
Grafana: 4.5/5 (107 reviews). Prometheus: 4.3/5 (135 reviews).
Go with: Grafana
Value user reviews?
Grafana: 107 reviews (4.5/5). Prometheus: 135 reviews (4.3/5).
Go with: Prometheus
3 Questions to Help You Decide
What's your budget?
Grafana is freemium. Prometheus is free. Go with Prometheus if free matters most.
What's your use case?
Both are monitoring tools. Compare their specific features to decide.
How important are ratings?
Grafana is rated higher: 4.5/5 vs 4.3/5.
Key Takeaways
Grafana
- Higher user rating: 4.5/5 vs 4.3/5
- Free tier available
- Our pick for this comparison
Prometheus
- Completely free
- Larger review base (135 reviews)
The Bottom Line
Use Prometheus as your metrics collection and short-term storage layer regardless of what you choose for visualization -- it is the most widely deployed metrics system in cloud-native infrastructure and the default for every Kubernetes operator and service mesh. Add Grafana when you need production dashboards, multi-source visualization, or unified alerting across signals: that is almost always within the first month of running Prometheus in production. If you want to minimize operational overhead, Grafana Cloud's free tier (10,000 series, 50 GB each of logs and traces, 3 users) is the rational default starting point for teams with under 10 engineers. At scale, the choice is between self-hosting the full LGTM stack (lowest cost, highest operational burden) and Grafana Cloud (roughly 50% cheaper than Datadog, zero infrastructure to manage). The tools are designed to be used together: the question is only how much of the infrastructure you want to own.
Frequently Asked Questions
Do I need both Grafana and Prometheus, or can I use one without the other?
You can use Prometheus without Grafana if you are comfortable writing PromQL directly and using Alertmanager for notifications. Prometheus's built-in expression browser handles debugging and ad-hoc queries. However, Grafana without a data source does nothing -- it requires a backend like Prometheus to query. In practice, almost every team that uses Prometheus for more than a few weeks adds Grafana for dashboards. Grafana Cloud includes a managed Prometheus-compatible backend (Mimir), so you can use Grafana Cloud without running Prometheus yourself.
What is the LGTM stack and how does Prometheus fit into it?
LGTM stands for Loki (logs), Grafana (visualization), Tempo (distributed traces), and Mimir (long-term Prometheus-compatible metrics storage). Prometheus typically acts as the local collection and short-term storage layer that Remote Writes into Mimir for long-term retention. In this model, Prometheus handles the scraping and first 15 days of data, Mimir holds months or years of history, and Grafana queries both. You can run each LGTM component yourself or use Grafana Cloud, which manages all four as a hosted service.
What changed in Prometheus 3.0?
Prometheus 3.0, released in November 2024, is the biggest update since Prometheus 2.0 in 2017. The major changes are: a native OTLP receiver that accepts OpenTelemetry metrics directly without a collector; Remote Write 2.0, which reduces bytes over the wire by 60% and CPU usage by 70% compared to Remote Write 1.0; and UTF-8 metric name support, which allows OTel dot-separated names to be stored natively. As of mid-2026, Prometheus is at v3.12.
How much does Grafana Cloud cost for a small team?
Grafana Cloud Free includes 10,000 active metrics series, 50 GB each of logs, traces, and profiles, and three users at no charge with no credit card required. Data retention on the free tier is 14 days. Above the free tier, Grafana Cloud Pro charges approximately $8 per 1,000 active series per month for metrics, $0.50 per GB for logs and traces, and $8 per user per month for Pro editor or admin seats. A small team monitoring 50,000 metrics series, 20 GB of logs per month, and five users would pay roughly $440/month.
Can Prometheus store logs or traces?
No. Prometheus is strictly a metrics system. It scrapes numeric time-series data exposed in the Prometheus exposition format or OTLP, stores it in a local TSDB, and answers PromQL queries. For logs, the Grafana ecosystem equivalent is Loki, which uses the same label-based data model as Prometheus but stores log streams queried with LogQL. For distributed traces, the equivalent is Tempo, queried with TraceQL. Prometheus cannot be extended to handle these signal types.
Is Prometheus good for long-term metrics storage?
Prometheus local storage is not designed for long-term retention. The recommended maximum retention is 15 days (configurable with --storage.tsdb.retention.time), and performance degrades with very large TSDB sizes. For long-term storage, teams use Prometheus Remote Write to ship metrics to Thanos (open-source, S3-backed), Cortex (open-source, horizontally scalable), Mimir (Grafana Labs, open-source, horizontally scalable), or Grafana Cloud. Mimir and Grafana Cloud are the most commonly adopted paths in 2026.