Buying snapshot
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| Decision point | What Grafana Labs states |
|---|---|
License | Grafana is distributed under AGPL-3.0-only, with separate Apache-2.0 exceptions. |
Deployment | Run the open-source project yourself (the repository links to installation guides) or use managed Grafana Cloud. On Enterprise, Grafana Visualization is offered in the cloud or self-hosted. |
Cloud billing model | Each Grafana Cloud service has its own free allowance and usage unit. Pricing is volume-based and tiered, with volume discounts unlocked automatically by consumption. |
Compliance (Grafana Cloud) | Grafana Labs lists FedRAMP compliance, PCI DSS compliance, AICPA SOC Type II verification and GDPR compliance. |
Our take
Grafana fits engineering teams that want one dashboard and alerting layer over telemetry they already store in several systems. If your team can run it and accepts the AGPL terms, the self-hosted project covers that job without a Grafana Cloud bill. Choose Grafana Cloud when you would rather not operate it, and estimate usage per service before you commit.
Evaluate Grafana
Popular comparisons
See all 6 Grafana comparisons
Where it stands out
Mixed data sources in one graph. You can set a data source per query, including custom data sources, so one panel can combine data from different backends without moving the data first.
Ad-hoc investigation. Explore supports ad-hoc queries and drilldown, with split view to compare time ranges, queries and data sources side by side. Moving from metrics to logs keeps the label filters.
Reusable dashboards. Template variables appear as dropdowns at the top of a dashboard, so one dashboard can serve many services or hosts.
Built-in alerting. You define alert rules visually; Grafana evaluates them continuously and sends notifications to systems such as Slack, PagerDuty, VictorOps and OpsGenie.
Open standards in Grafana Cloud. Grafana Cloud is built on OpenTelemetry and Prometheus and offers hundreds of plugins and integrations, so existing tools and data sources can connect without a rip-and-replace.
Tools to cut unused telemetry. Grafana Labs says its Adaptive Telemetry suite cuts telemetry costs by 30 to 50% on average. Adaptive Metrics aggregates unused time series, and Adaptive Logs recommends log patterns to drop.
Product screenshot
Trade-offs to weigh
License terms to review. Grafana is AGPL-3.0-only, with Apache-2.0 exceptions. If you plan to modify Grafana or build it into a product you offer to others, check these terms with your legal team first.
Many meters to forecast. On Pro, each service bills in its own unit (active series, GB processed, written and retained, host hours, active users, tokens) once its free allowance is used, so a Cloud budget needs a usage estimate for every service you turn on.
Retention decides the plan. Free keeps telemetry for 14 days. Pro keeps metrics for 13 months but logs, traces, profiles and k6 tests for 30 days. For longer log history, Enterprise lists custom retention.
Pricing and plan conditions
The Grafana Cloud plans set support, retention and access. The Pro platform fee is the entry price; after each service's free allowance, usage is billed per service at the rates below. Enterprise is a yearly spend commitment, and some Enterprise unit prices depend on annual commit and volume.
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| Plan | Published price | What it adds |
|---|---|---|
Free | $0 | All Grafana Cloud services with usage limits, including Adaptive Telemetry and AI Assistant; community support; 14 days retention for metrics, logs, traces, profiles and k6 performance tests. |
Pro (self-serve) | From $19 / month + usage | Everything in Free plus: unlimited access to all Grafana Cloud services, paying as you go above the free tier; 8x5 email support; 13 months retention for metrics; 30 days retention for logs, traces, profiles and k6 performance tests. |
Enterprise | Starts at $25,000 / year spend commit | A full-service offering for security, compliance and deployment requirements: premium support, custom retention and deployment flexibility (Public Cloud, Federal Cloud or Bring Your Own Cloud). |
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| Service | Free allowance | Pro rate after the allowance |
|---|---|---|
Metrics | 10k active series per month | Starts at $6.50 / 1k series, with automatic volume discounts. On Enterprise, as low as $3 / 1k series based on annual commit and volume, with a minimum commit of $25,000 per year. |
Logs | 50 GB ingested per month | Starts at $0.050/GB process, $0.400/GB write, $0.100/GB retain. Enterprise: custom unit price based on annual commit. |
Traces | 50 GB ingested per month | Starts at $0.05/GB process, $0.40/GB write, $0.10/GB retain |
Profiles | 50 GB ingested per month | Starts at $0.05/GB process, $0.40/GB write, $0.10/GB retain. Enterprise: custom unit price based on annual commit. |
Grafana Visualization | 3 active users per month | Starts at $8 / active user; Grafana Visualization with Enterprise Plugins starts at $55 / user with Enterprise Plugins built and maintained by Grafana Labs |
Kubernetes Monitoring | 2,232 host hours & 37,944 container hours per month, including telemetry generated | Starts at $0.01 / host hour ($7.20/host); starts at $0.0007 / container hour ($0.50/container) |
Grafana Assistant (AI) | 3 active AI users per month (40M tokens / user / mo) & 25M tokens per month for service account usage | Starts at $20 / active AI user (includes 40M tokens per user); starts at $2 / 1M tokens for additional tokens |
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
3.7k GitHub commits 90d77.8k GitHub stars0 vulnerabilities across 2 packagesOpenSSF score 7.4/10
See all signals from 10 sources
npm · @grafana/data@13.3.0 · PyPI · grafana-client@5.1.4
Alternatives to Grafana
The reviewed substitutes for Grafana among the metrics & dashboards, and what would make each one the better answer.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- Datadog
- Datadog and Grafana both give teams dashboards and alerts on metrics, logs and traces. Grafana is open source and connects to many data sources a team already runs, with a managed cloud option, while Datadog is a commercial SaaS platform that collects and stores the data itself. The pick depends on whether the team assembles monitoring from open-source parts or buys it as one service.Applies to: Whether monitoring is assembled from open-source components or bought as a platform.
- New Relic
- New Relic and Grafana both put application and infrastructure metrics, logs and traces on dashboards with alerting. New Relic is a SaaS platform that collects telemetry with its own agents and correlates it for root-cause work, while Grafana is an open-source visualization layer that charts many data sources, including business databases, in one dashboard.Applies to: For teams that want application and infrastructure telemetry on dashboards with alerts.
- Apache Superset
- Apache Superset and Grafana are open-source dashboard tools, and either can chart SQL data for a wide audience. Superset is a BI platform for exploring business tables with SQL and rich charts, while Grafana is tuned for time-series metrics, logs and alerts from monitoring systems, so the pick follows the kind of data on the screen.Applies to: Whether operational metrics and business reporting share one dashboarding tool.
- Metabase
- Metabase and Grafana are open-source dashboarding tools that can both chart data from SQL databases. Grafana is built around time-series and operational metrics with alerting, while Metabase is a BI tool for business users who ask questions of tables, so the pick depends on whether operational and business data share one tool.Applies to: Whether operational metrics and business reporting share one dashboarding tool.
Related technologies
Normally used together rather than chosen between, so these are not alternatives.
- Splunk
- Splunk indexes and searches machine data such as logs, and raises alerts and reports from that store. Grafana is a visualization layer with pluggable data sources, so it can chart Splunk data next to metrics from time-series databases and cloud monitoring in a single dashboard.Applies to: Whether these two do the same job, or different jobs in one pipeline.
- Grafana Loki
- Loki is the log store in the Grafana stack and Grafana is the interface you read it through; the reviewers' line is that if you are already on Prometheus and Grafana, Loki is the natural fit. A team runs both, so presenting one as an alternative to the other would be wrong in the plain sense of the word.
Head to head
See detailed alternatives analysis
Grafana is the go-to open-source visualization layer for observability teams, with 73,000+ GitHub stars and a pluggable data source model that connects to Prometheus, Loki, Elasticsearch, InfluxDB, and dozens more backends. But Grafana's strength as a dashboarding frontend means it depends on external data stores for metrics, logs, and traces -- and its AGPL-3.0 license, steep configuration requirements, and limited built-in alerting push many teams toward alternatives. Here are the strongest Grafana alternatives for teams that need observability beyond dashboards.
Top Alternatives Overview
Datadog is a fully managed SaaS observability platform that bundles infrastructure monitoring, APM, log management, and real user monitoring under one roof. It offers 800+ out-of-the-box integrations, automatic service discovery, and a proprietary query language that is simpler to learn than PromQL. Datadog's usage-based pricing starts with a free tier and scales at roughly $15 per host per month for infrastructure monitoring. Choose this if you want a single vendor that handles metrics, traces, and logs without managing any backend infrastructure.
Prometheus is the CNCF-graduated monitoring system that Grafana was originally built to visualize. Written in Go with 63,600+ GitHub stars, it uses a pull-based metrics collection model with PromQL for querying and a dimensional data model based on metric names plus key-value labels. Prometheus servers run independently with local storage, making them operationally simple to deploy. Choose this if you need a battle-tested, fully open-source metrics backend (Apache 2.0) with native Kubernetes service discovery and zero licensing costs.
Elastic Observability unifies logs, metrics, traces, and profiling on top of the Elastic Stack (Elasticsearch, Kibana, Logstash). It is fully OpenTelemetry-compliant, includes always-on anomaly detection powered by a decade of machine learning refinement, and supports petabyte-scale log storage with the logsdb index mode that reduces data footprint by up to 65%. Elastic Cloud Hosted starts at $99/month for Standard and scales to $184/month for Enterprise, based on a production setup with 120 GB storage in 2 zones. Choose this if log analytics is your primary use case and you need powerful full-text search alongside observability.
Splunk is the enterprise-grade platform now owned by Cisco, combining security analytics (SIEM) with full-stack observability. It ingests logs, metrics, traces, and events with 2,000+ integrations available via Splunkbase, and uses SPL (Search Processing Language) for data queries. Splunk Enterprise and Splunk Cloud are priced by quote on workload, ingest or entities, and Splunk Free is a no-cost licence capped at 500 MB of indexing a day. Choose this if you need a combined security and observability platform with strong compliance features (PCI, HIPAA, GDPR).
New Relic is an AI-powered observability SaaS that offers a generous free tier (100 GB of data ingest per month) and Standard pricing of $10 for the first full platform user and $99 per additional user a month, plus $0.40 per GB of data beyond the free amount. It provides code-level diagnostics, distributed tracing, and browser monitoring with automatic instrumentation for major languages. New Relic's single-platform approach means APM, infrastructure, logs, and synthetics share one data model. Choose this if you want low-friction onboarding with a substantial free tier and predictable per-host pricing.
Observe is a modern observability platform built on a streaming data lake architecture that promises 10x faster troubleshooting at 60% lower cost than legacy platforms. It features an AI SRE that uses natural language to correlate signals and suggest root causes, with pricing starting at $0.49/GB for log ingestion. Choose this if you handle high-volume telemetry and want a data-lake-first approach with aggressive cost optimization.
Architecture and Approach Comparison
Grafana operates as a visualization and dashboarding layer that queries external data sources -- it does not store data itself. This architecture gives teams flexibility to mix backends (Prometheus for metrics, Loki for logs, Tempo for traces) but creates operational complexity: you deploy, scale, and maintain each backend independently. Grafana Cloud bundles the LGTM stack (Loki, Grafana, Tempo, Mimir) as a managed service, but self-hosted Grafana requires significant infrastructure expertise.
Datadog and New Relic take the opposite approach: fully managed, vertically integrated platforms where ingestion, storage, querying, and visualization live in one service. This eliminates backend management but creates vendor lock-in. Splunk sits in between -- it stores and indexes data in its own platform but requires substantial infrastructure for self-hosted deployments (Splunk Enterprise), or you pay premium rates for Splunk Cloud.
Prometheus uses a pull-based model where the server scrapes HTTP endpoints at configured intervals, storing time-series data locally. This design is operationally simple for metrics but does not handle logs or traces. Elastic Observability takes an ingest-everything approach with Elasticsearch as the unified store, making it strongest for log-heavy workloads where full-text search matters. Observe's streaming data lake architecture processes telemetry as event streams rather than pre-indexed documents, which reduces storage costs for high-cardinality data.
A key architectural distinction: Grafana and Prometheus are open-source tools you assemble into a stack, while Datadog, New Relic, Dynatrace, and Observe are commercial platforms you subscribe to. Teams that value control and cost transparency lean toward the open-source stack; teams that prioritize speed-to-value and minimal ops overhead choose managed platforms.
Pricing Comparison
Pricing across observability tools varies dramatically based on data volume, host count, and feature requirements. Here is how the major Grafana alternatives compare:
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| Tool | Pricing Model | Starting Price | Free Tier | Typical Enterprise Cost |
|---|---|---|---|---|
| Grafana (self-hosted) | Open Source (AGPL-3.0) | $0 | Yes (full OSS) | Infrastructure costs only |
| Grafana Cloud | Freemium / Usage-Based | $0 | 10k metrics series, 50 GB logs | $20/active user/month |
| Datadog | Usage-Based | $0 | Limited free tier | $15+/host/month |
| Prometheus | Open Source (Apache 2.0) | $0 | Yes (full OSS) | Infrastructure costs only |
| Elastic Observability | Paid Tiers | $99/month | No | $99-$184/month per tier |
| Splunk | Workload, ingest or entity-based | $0 (Splunk Free, 500 MB/day) | Splunk Free | Quote only |
| New Relic | Usage-Based | $0 | 100 GB ingest/month | $19+/host/month |
| Observe | Usage-Based | $0.49/GB logs | Trial available | Custom pricing |
Self-hosted Grafana plus Prometheus costs nothing in licensing but requires dedicated engineering time for deployment, scaling, and upgrades. Grafana Cloud's free tier includes 10,000 billable metric series and 50 GB each of logs, traces, and profiles. Splunk prices large deployments by quote, scaled to ingest volume, workload or entities.
When to Consider Switching
Switch from Grafana when your team spends more time managing the backend stack than building dashboards. If deploying Mimir for long-term metrics storage, Loki for logs, and Tempo for traces requires a dedicated platform team, a managed solution like Datadog or New Relic removes that operational burden entirely.
Consider alternatives when log analytics becomes your primary need. Grafana's Loki uses a label-based index that is cost-efficient but less powerful for full-text search than Elasticsearch. Teams doing heavy log investigation with complex queries will find Elastic Observability or Splunk significantly faster for ad-hoc searches across petabytes of unstructured data.
Move to Prometheus if you only need metrics monitoring and want to reduce complexity. Running Grafana solely as a visualization layer for Prometheus adds an extra component to deploy and maintain. Prometheus's built-in expression browser and Alertmanager handle basic visualization and alerting without a separate UI layer.
Switch to a commercial platform when your organization requires built-in compliance reporting, RBAC, and audit trails. Grafana Enterprise offers these features but at additional cost. Splunk provides out-of-the-box compliance support for PCI, HIPAA, and GDPR, while Datadog and Dynatrace include enterprise security features in their standard offerings.
Migration Considerations
Migrating away from Grafana is simplified by the fact that Grafana stores configuration, not data. Your actual telemetry lives in the backends (Prometheus, InfluxDB, Elasticsearch), so switching visualization platforms does not require moving historical metrics. Export your Grafana dashboards as JSON and use them as specifications for rebuilding in the target platform.
For teams moving to Datadog or New Relic, the biggest shift is re-instrumenting data collection. Replace Prometheus exporters and Loki agents with the target platform's agents (Datadog Agent, New Relic APM agents). Both platforms support OpenTelemetry, so if you have already standardized on OTel instrumentation, the migration is primarily a configuration change pointing exporters to new endpoints.
Moving to Elastic Observability preserves your investment in OpenTelemetry. Elastic's EDOT (Elastic Distributions of OpenTelemetry) provides production-ready OTel collectors, and existing PromQL queries can be translated to ES|QL, though the syntax differs substantially. Budget two to four weeks for a mid-sized deployment to rebuild dashboards and alert rules.
The learning curve varies significantly. Teams comfortable with PromQL will find Datadog's query syntax approachable, while Splunk's SPL is a different paradigm that requires dedicated training (users consistently report a steep learning curve). Expect the steepest transition when moving from Grafana's open-source ecosystem to Splunk's enterprise platform, and the smoothest transition when moving to Grafana Cloud, which preserves all existing dashboards and queries.