Grafana OSS vs. Grafana Enterprise vs. Grafana Cloud: A Practical Feature Comparison

Grafana is often introduced as a dashboarding tool. That description is still correct, but it no longer captures the full product landscape.

Today, "using Grafana" can mean at least three different things:

1. running the open source application on your own infrastructure,
2. licensing additional enterprise features while keeping the platform self-hosted, or
3. adopting a fully managed observability platform operated by Grafana Labs.

All three options share the familiar Grafana experience: dashboards, visualizations, data sources, exploration, and alerting. The meaningful differences begin elsewhere — in governance, operational responsibility, integrated observability workflows, support, and total cost of ownership.

Grafana's own feature comparison summarises the distinction well: OSS, Enterprise, and Cloud all provide the core visualization layer; Enterprise and Cloud add advanced governance; and Cloud additionally provides a managed observability stack and integrated solutions.

This article compares the three approaches and explains when each one is the best fit.

First, a terminology clarification

The Grafana product names are easy to confuse because the word "Enterprise" is used in more than one context.

Grafana OSS

Grafana OSS is the free, open source edition. You install it, configure it, upgrade it, secure it, and operate it on your own infrastructure.

It provides the core Grafana experience:

• dashboards and visualizations,
• explore,
• alerting,
• built-in and community data sources,
• user and team management,
• provisioning through configuration and APIs,
• and a large plugin ecosystem.

Grafana Enterprise

Grafana Enterprise is a commercial, self-hosted edition built on the open source foundation.

It adds enterprise-oriented features such as advanced access control, enterprise data source plugins, audit logging, reporting, query caching, identity integrations, and commercial support.

Your organisation still runs the infrastructure.

Grafana Cloud

Grafana Cloud is a fully managed observability platform operated by Grafana Labs.

It includes hosted Grafana, but it is much more than "Grafana as a service." Depending on the plan and enabled products, it can also provide managed storage and workflows for:

• metrics,
• logs,
• traces,
• continuous profiles,
• application observability,
• Kubernetes monitoring,
• frontend observability,
• synthetic monitoring,
• performance testing,
• SLO management,
• incident response,
• and AI-assisted investigations.

Grafana Cloud itself has Free, Pro, and Enterprise commercial plans. A Grafana Cloud Enterprise plan is not the same product as self-hosted Grafana Enterprise.

Grafana Labs explains the overall landscape in its guide to Grafana deployment options.

1. The core Grafana experience is available in all three options

All three editions provide the features most people initially associate with Grafana:

• querying data sources,
• building dashboards,
• creating visualizations,
• exploring telemetry,
• defining alerts,
• sharing dashboards,
• and integrating with external systems.

For a team that primarily wants to visualize data already stored in Prometheus, PostgreSQL, Elasticsearch, Loki, CloudWatch, or another supported backend, Grafana OSS may be entirely sufficient.

This is important: Grafana OSS is not a limited trial version. It is a mature production application with a broad user community and a large ecosystem.

Grafana Enterprise does not replace this experience. It extends it. Existing dashboards, queries, alert rules, and operational knowledge remain relevant.

Grafana Cloud also retains the same central user experience. The difference is that Grafana Labs operates the application and can connect it to a much broader managed platform.

What does not change

Regardless of the chosen edition, teams still need to make decisions about:

• what telemetry to collect,
• how to name and label it,
• what dashboards are useful,
• which alerts are actionable,
• and which service-level objectives represent real user impact.

A managed platform can reduce operational work, but it cannot automatically create a good observability strategy.

2. Governance is the clearest difference between OSS and Enterprise

Grafana OSS provides permissions for organisations, teams, folders, and dashboards. For a small or trusted engineering group, these controls may be enough.

As adoption grows, organisations often need more precise answers to questions such as:

• Which team can query a specific data source?
• Who changed a dashboard or permission?
• Can access be synchronised automatically from the identity provider?
• Can business units be isolated from one another?
• Can reports be generated and distributed on a schedule?
• Can security teams review an audit trail?

Grafana Enterprise adds capabilities designed for these requirements.

Typical Enterprise features include:

• fine-grained role-based access control,
• data source permissions,
• SAML authentication,
• enhanced LDAP integration,
• team synchronisation,
• audit logging,
• usage insights,
• scheduled reporting,
• PDF exports,
• query caching,
• and enterprise support.

Many of these capabilities are also available in relevant Grafana Cloud plans.

The difference is where the platform runs. With Grafana Enterprise, the organisation controls the servers, databases, networks, upgrades, and deployment lifecycle. With Grafana Cloud, Grafana Labs operates the service.

When governance becomes the deciding factor

Grafana Enterprise is especially relevant when:

• self-hosting is required by policy,
• multiple departments use the same instance,
• sensitive data sources must be isolated,
• auditability is mandatory,
• identity management must be centralised,
• or community support is not sufficient for a business-critical service.

For an overview of the commercial self-hosted offering, see the Grafana Enterprise product page.

3. Enterprise plugins can turn Grafana into a shared pane of glass

All editions support built-in and community plugins.

Grafana Enterprise additionally includes access to commercial data source plugins maintained and supported by Grafana Labs. The plugin catalogue includes integrations for systems such as:

• Datadog,
• Splunk,
• New Relic,
• Dynatrace,
• Oracle,
• Snowflake,
• ServiceNow,
• Salesforce,
• SAP HANA,
• and other enterprise platforms.

The current catalogue is available in the Grafana plugin documentation.

This is useful for organisations that already operate several monitoring, analytics, and business systems. Grafana can provide a common visualisation and investigation layer without requiring every data set to be moved into a new backend.

For example, a single dashboard may combine:

• service metrics from Prometheus,
• incidents from ServiceNow,
• infrastructure data from Datadog,
• business data from Snowflake,
• and operational logs from Splunk.

What plugins do not solve

A shared interface does not remove the characteristics of the underlying systems.

The original data source still determines:

• retention,
• query performance,
• availability,
• access restrictions,
• and, in some cases, query or ingestion cost.

Grafana can unify the experience, but it does not magically unify every backend's operating model.

4. Grafana is not automatically the telemetry backend

This is the most important architectural distinction in the comparison.

Grafana OSS and the core Grafana Enterprise product are primarily the interface through which users query, visualize, and alert on data. They do not automatically provide scalable storage for all telemetry.

A self-hosted observability platform may combine:

• Grafana for visualization and alerting,
• Mimir or Prometheus for metrics,
• Loki for logs,
• Tempo for traces,
• Pyroscope for continuous profiling,
• and Grafana Alloy or OpenTelemetry Collector for telemetry collection.

This combination is often called the LGTM stack: Loki, Grafana, Tempo, and Mimir.

The projects are open and composable, but operating them at scale is a significant engineering responsibility.

A production deployment may require teams to manage:

• distributors,
• ingesters,
• query frontends,
• queriers,
• compactors,
• storage gateways,
• caches,
• object storage,
• replication,
• tenancy,
• retention,
• and capacity.

Grafana Enterprise also has commercial self-hosted components for metrics, logs, and traces. They belong to the broader Grafana Enterprise Stack and should not be confused with the licence for the Grafana visualization application alone.

What Grafana Cloud changes

Grafana Cloud provides managed backends and operates the distributed infrastructure for the customer.

The customer still needs to:

• instrument applications,
• deploy and configure collectors,
• define useful labels,
• control telemetry volume,
• and build meaningful dashboards and alerts.

However, Grafana Labs takes responsibility for operating the storage and query platform.

This is why Grafana Cloud should not be described simply as "hosted dashboards." It changes the ownership model for the observability stack.

5. Cloud adds integrated workflows, not only managed storage

A self-hosted open source stack provides flexible building blocks. Grafana Cloud increasingly provides complete workflows built on top of those blocks.

The Grafana Cloud feature overview includes capabilities such as:

• managed metrics, logs, traces, and profiles,
• Application Observability,
• Kubernetes Monitoring,
• Frontend Observability,
• Synthetic Monitoring,
• performance testing with k6,
• SLO and error-budget management,
• incident response and on-call workflows,
• cloud integrations,
• telemetry cost management,
• and cross-signal troubleshooting.

Many of these use open source projects or open standards underneath. The commercial value is in the integration, operation, onboarding, and opinionated workflows.

Example: Kubernetes monitoring

A team can build Kubernetes observability using Prometheus, kube-state-metrics, Loki, dashboards, and alert rules.

That approach provides control, but the team must decide:

• which metrics to collect,
• which dashboards to maintain,
• which alerts are useful,
• how to correlate pod logs with metrics,
• how to navigate across clusters,
• and how to expose the platform to application teams.

A managed Kubernetes monitoring product adds guided onboarding, prebuilt dashboards, fleet navigation, recommended alerts, and integrated views.

Example: application observability

A self-hosted team can combine OpenTelemetry, Prometheus-compatible metrics, Loki, and Tempo to build an application-observability workflow.

Grafana Cloud Application Observability packages this into an integrated experience with service inventory, service maps, prebuilt views, and correlation between metrics, logs, and traces.

Components versus workflows

This is a useful way to frame the difference:

• OSS gives you components.
• Enterprise gives you commercially supported, governable components.
• Cloud gives you managed components plus integrated workflows.

6. Alerting is available everywhere, but advanced reliability workflows differ

Grafana Alerting is available in the open source edition. Teams can create multidimensional alert rules, route notifications, and manage alerts from the Grafana interface.

That is enough for many use cases.

More mature reliability programmes often need additional layers:

• service-level objectives,
• error budgets,
• burn-rate alerts,
• anomaly detection,
• alert enrichment,
• escalation policies,
• on-call schedules,
• incident timelines,
• and post-incident workflows.

These can be assembled around a self-hosted stack, but doing so requires additional tools and integration work.

Grafana Cloud provides more of these capabilities as a connected workflow. The objective is not merely to trigger an alert, but to help a team move from:

1. detection,
2. through triage,
3. to investigation,
4. escalation,
5. remediation,
6. and review.

The value increases when metrics, logs, traces, alerts, and incident context are already available in the same platform.

7. AI is becoming another major differentiator

Grafana Assistant adds natural-language workflows to Grafana.

Depending on the available features and connected data, it can help users:

• write and explain PromQL, LogQL, TraceQL, and SQL queries,
• create and modify dashboards,
• summarise logs and telemetry,
• investigate alerts,
• correlate metrics, logs, and traces,
• navigate an unfamiliar observability environment,
• and launch AI-assisted investigations.

Grafana Assistant can also be used from a self-managed Grafana OSS or Enterprise instance. In that deployment model, the Assistant interface runs in the self-hosted Grafana instance but connects to a Grafana Cloud Assistant backend.

The Grafana Assistant documentation explains that core chat, querying, dashboarding, and navigation workflows can be available from self-managed Grafana, while some advanced Cloud-dependent capabilities require the full Grafana Cloud feature set.

This distinction matters for architecture and compliance reviews.

Seeing Assistant inside a self-hosted Grafana instance does not mean that the AI service is fully self-contained inside the customer's infrastructure.

Organisations should review:

• what telemetry the Assistant can access,
• how permissions are enforced,
• where requests are processed,
• which features depend on Grafana Cloud,
• and what governance controls apply to AI-generated actions.

8. Operational responsibility remains with you in OSS and Enterprise

Grafana Enterprise adds commercial capabilities and support, but it does not eliminate self-hosting work.

For both OSS and Enterprise, the customer remains responsible for areas such as:

• infrastructure provisioning,
• networking,
• TLS and secrets,
• high availability,
• database maintenance,
• backups,
• upgrades,
• plugin management,
• capacity planning,
• monitoring the monitoring platform,
• incident response,
• and disaster recovery.

When the broader telemetry stack is self-hosted, the team must also operate the metrics, logs, traces, profiles, and object-storage layers.

Grafana's guide to team and skill requirements lists Linux, networking, upgrades, databases, capacity planning, and object storage among the competencies required for self-managed deployments.

Grafana Cloud reduces this burden, although it does not eliminate operational work entirely.

Customers still manage the edge of the platform:

• application instrumentation,
• collectors,
• agents,
• network connectivity,
• telemetry pipelines,
• data quality,
• and access policies.

The difference is that Grafana Labs operates the central backends.

The opportunity-cost question

The most expensive part of a self-hosted platform may not be compute or storage.

It may be the time spent by experienced engineers on:

• upgrades,
• scaling,
• tuning,
• incident recovery,
• and keeping the platform available.

That work may be entirely justified when control is strategically important. But it should be included in the cost comparison.

9. Pricing: licence cost is only one part of TCO

Grafana OSS has no software licence fee, but it is not cost-free.

Its total cost of ownership may include:

• compute,
• storage,
• network transfer,
• databases,
• backup infrastructure,
• Kubernetes or virtual-machine operations,
• engineering time,
• on-call coverage,
• upgrade projects,
• and downtime risk.

Grafana Enterprise adds a commercial licence to the self-hosting costs. In return, the organisation gains enterprise features, premium integrations, support, and a commercial relationship with Grafana Labs.

Grafana Cloud uses plan- and usage-based pricing. Billing can depend on the products used and on dimensions such as:

• active metric series,
• ingestion and retention,
• logs, traces, and profiles,
• active users,
• test execution,
• host or container usage,
• and AI usage.

The current model is described on the Grafana pricing page.

The correct comparison

The wrong comparison is: Free software versus paid software.

A better comparison is: Self-managed infrastructure and engineering capacity versus a commercial licence or managed-service bill.

A platform may be inexpensive for one telemetry profile and expensive for another. The result changes with:

• number of hosts,
• metric cardinality,
• ingestion volume,
• retention,
• query patterns,
• and the amount of engineering work included in the calculation.

A useful TCO model

When comparing the three options, include at least four cost categories:

1. Software and service cost — Licence fees, subscriptions, and usage charges.
2. Infrastructure cost — Compute, storage, network, databases, and backup systems.
3. Engineering cost — Deployment, upgrades, scaling, tuning, and support.
4. Risk and opportunity cost — Downtime, delayed projects, and engineers maintaining the platform instead of improving products or reliability practices.

Without all four categories, OSS will often appear artificially cheap.

10. Data residency and compliance may decide the architecture

Self-hosting provides the greatest control over where data is stored and how the environment is connected.

An organisation can deploy Grafana OSS or Enterprise:

• in its own data centre,
• in a private cloud,
• in a selected public-cloud region,
• inside an isolated network,
• or close to regulated workloads.

The organisation chooses:

• storage systems,
• encryption controls,
• backup policy,
• network boundaries,
• access model,
• and data-retention rules.

That flexibility comes with full responsibility for maintaining the security and compliance environment.

Grafana Cloud provides multiple regions, private connectivity options, and vendor-maintained compliance controls, but customers must operate within the regions and deployment models offered by Grafana Labs.

The official data residency and compliance comparison explains the trade-off:

• self-hosting provides maximum control,
• while Cloud provides managed controls and certifications within the supported service boundaries.

For regulated organisations, this may be more important than any individual product feature.

11. Licensing also matters

Grafana, Loki, and Tempo use the AGPLv3 licence for their core open source editions.

For many organisations that run unmodified software internally, this does not create a practical problem. However, companies that modify the software, redistribute it, or expose modified versions as a network service should review the licence carefully.

Grafana Labs provides a commercial Enterprise binary and commercial licensing options for organisations that do not want to operate under AGPL obligations.

The official explanation is available on the Grafana licensing page.

This is not only a legal detail. Licensing requirements can influence:

• whether the software can be embedded in a product,
• whether modifications must be published,
• how a managed service can be offered,
• and whether procurement requires indemnification.

Legal teams should assess the actual deployment model rather than relying on the general statement that "open source is free."

12. When to choose Grafana OSS

Grafana OSS is a strong choice when:

• the main requirement is dashboards, exploration, and alerting,
• telemetry is already stored in existing systems,
• community plugins are sufficient,
• the user base is relatively small or trusted,
• basic permissions meet the governance requirements,
• the team has the skills to operate the deployment,
• source-code access and flexibility are important,
• and avoiding a commercial dependency is a priority.

Typical use cases include:

• internal engineering dashboards,
• platform-team tooling,
• development and test environments,
• smaller production environments,
• and organizations that consider observability infrastructure a core competency.

Main advantages

• no licence fee,
• maximum deployment flexibility,
• broad community ecosystem,
• open architecture,
• and no requirement to adopt a managed service.

Main disadvantages

• infrastructure and upgrades are your responsibility,
• advanced governance is limited,
• support is community-based,
• and integrated observability workflows must be assembled and maintained.

13. When to choose Grafana Enterprise

Grafana Enterprise is appropriate when self-hosting is required but OSS governance is no longer sufficient.

Typical drivers include:

• SAML and centralized identity management,
• fine-grained access control,
• data source permissions,
• audit logging,
• scheduled reporting,
• premium data source plugins,
• query caching,
• regulatory or internal-control requirements,
• vendor support,
• and commercial licensing or indemnification needs.

Typical users include:

• financial institutions,
• healthcare organizations,
• public-sector environments,
• large enterprises with shared platforms,
• and companies that must keep observability data inside controlled infrastructure.

Main advantages

• preserves self-hosted control,
• adds enterprise security and governance,
• provides commercial support,
• and supports premium integrations with existing enterprise tools.

Main disadvantages

• requires a paid licence,
• infrastructure remains your responsibility,
• the broader telemetry platform may still need to be deployed separately,
• and operational complexity is not removed.

The most important point is that Enterprise improves the self-hosted model; it does not replace it.

14. When to choose Grafana Cloud

Grafana Cloud is the strongest fit when the organization wants observability as a managed platform rather than as a collection of self-operated components.

Typical drivers include:

• limited capacity to operate distributed telemetry systems,
• a need to deploy observability quickly,
• managed metrics, logs, traces, and profiles,
• integrated application and Kubernetes observability,
• SLO and incident-management workflows,
• performance and synthetic testing,
• AI-assisted troubleshooting,
• managed scalability and availability,
• and a preference for usage-based spending.

Grafana Cloud is also attractive for organizations that want to standardize on open technologies such as Prometheus and OpenTelemetry without operating all of the corresponding backend infrastructure.

Grafana Labs’ article on why companies migrate from Datadog to Grafana Cloud highlights common motivations such as cost control, consolidation, open standards, and reducing the need to switch between tools.

The article is vendor-authored, so its customer examples should be read as product marketing rather than independent benchmarking. Nevertheless, it clearly describes the problems Grafana Cloud is designed to address.

Main advantages

• reduced platform-operations burden,
• fast onboarding,
• managed scalability,
• integrated observability workflows,
• commercial support by plan,
• and access to Cloud-specific products and AI capabilities.

Main disadvantages

• ongoing service and usage costs,
• less infrastructure-level control,
• dependence on the vendor’s regions and service model,
• and the need to actively manage ingestion, retention, and cardinality to control spend.

15. Hybrid architectures are a valid option

The choice does not always need to be absolute.

Common hybrid patterns include:

• Grafana Cloud for production and Grafana OSS for local development,
• self-hosted data sources queried from Grafana Cloud,
• Cloud-managed metrics with selected logs retained internally,
• Grafana Enterprise for regulated workloads and Grafana Cloud for less restricted teams,
• or a gradual migration from OSS backends to managed services.

Hybrid adoption can reduce migration risk and preserve existing investments.

It can also create complexity if teams must manage:

• two permission models,
• duplicate dashboards,
• separate alerting systems,
• inconsistent telemetry schemas,
• or unclear ownership boundaries.

A hybrid design should therefore be intentional. Each component should have a clear reason to remain self-hosted or move to Cloud.

Conclusion

Grafana OSS, Grafana Enterprise, and Grafana Cloud share the same visualisation foundation, but they solve different organisational problems.

Grafana OSS is the open, flexible building block. It is a strong option for teams that value control and are prepared to operate the platform.

Grafana Enterprise adds governance, premium integrations, support, and commercial assurances while preserving the self-hosted model.

Grafana Cloud changes both the operating model and the scope of the platform. Grafana Labs manages the central telemetry infrastructure, but the value goes beyond hosted metrics, logs, traces, and profiles. Cloud also provides integrated, opinionated workflows such as Application Observability, Kubernetes Monitoring, SLO management, incident response, performance testing, frontend observability, and AI-assisted investigations.

The most useful question is not: Which edition has the longest feature list?

It is: Which responsibilities should remain inside our engineering organisation?

When the answer is "we want to run and control the platform," choose OSS if the community feature set is enough, or Enterprise if advanced governance and support are required.

When the answer is "we want to use observability rather than operate it," Grafana Cloud is the most complete option.

Reviewed by Grzegorz Kocur