Grafana Problems

You Ask, We Answer: What are the Major Problems with Grafana?

Here at Sirius Open Source, we often get asked, "What are the major problems and weaknesses associated with adopting and scaling Grafana?" This is a very good question, and one that deserves a clear, honest answer. We understand that when making a major technology decision, we typically worry more about what might go wrong than what will go right.

We want to be upfront: Grafana has achieved widespread status as the definitive visualization layer for the cloud-native stack. However, the truth is, while Grafana facilitates rapid initial visualization and offers unparalleled flexibility through its agnostic integration capabilities, this very flexibility masks profound fragility in high-scale enterprise environments. Scaling Grafana introduces distinct structural contradictions and liabilities concerning performance, governance, security, and financial predictability.

This article will explain the key architectural limitations, operational burdens, governance gaps, and commercial risks embedded within the Grafana ecosystem. We aim to be fiercely transparent, allowing you to choose the best path and mitigating investment required for your specific needs.

 

Architectural Limitations: The Rendering Cliff and High-Cardinality Bottlenecks

The structural contradictions within Grafana largely stem from its client-side rendering model. This design choice, while flexible, introduces severe performance constraints when handling modern, high-volume telemetry data.

The Browser Heap Exhaustion Problem

A critical instability in the ecosystem is the platform’s handling of high-cardinality data—metrics with dimensions possessing a vast number of unique values.

Rendering Cliff Failure: Backend Time-Series Databases (TSDBs) can ingest millions of active series, but Grafana’s frontend architecture bears the heavy load of final data processing and visualization. This architectural decoupling creates a "rendering cliff" failure mode where the massive dataset returned by the query exhausts the memory allocated to the browser tab.

Operational Impact: When users aggregate across high-cardinality labels (e.g., dynamic URLs or user sessions), the browser attempts to render thousands of distinct data points. This load triggers "too many time series" errors or causes the browser tab to crash entirely due to Out-Of-Memory (OOM) exceptions. Critically, during high-severity incidents, dashboards configured to visualize ephemeral infrastructure (like "All pods") often fail to load, effectively blinding operators and prolonging Mean Time To Recovery (MTTR).

Even when the browser heap is not exhausted, performance degrades non-linearly with dataset size, leading users to report "clumsy navigation" and significant latency on heavy dashboards.

The Operational Burden of Self-Hosting and Fragile Upgrades

For organizations that choose to self-host Grafana, the complexity of High Availability (HA) and the platform's rapid evolution transform it from a utility into a complex system requiring constant, expert-level care.

The "Database is Locked" Boot Loop

A critical failure mode involves database locking issues, particularly in HA setups using SQLite (the default) or improperly tuned database backends.

SQLite Limitations: During normal operation, write-heavy processes (like alert state updates and annotation creation) cause the default SQLite database to hit concurrency limits, resulting in intermittent "database is locked" errors. Grafana documentation explicitly advises against using SQLite for HA or high-load setups, yet it remains the default, creating recovery risks later in the lifecycle.

Startup Failure: During upgrades or startup, Grafana attempts to acquire a lock to perform schema migrations. In HA clusters, this frequently fails, resulting in a "boot loop" where instances continually log "database is locked" or "failed to obtain lock" errors.

Breaking Changes and Plugin Fragility

Grafana’s rapid release cycle introduces breaking changes that necessitate manual intervention, placing organizations on a fragile "upgrade treadmill".

AngularJS Removal: The decision to disable support for the AngularJS framework by default in version 11 fractures the ecosystem, breaking older dashboards and a vast library of community plugins. Teams are forced to audit and rewrite hundreds of dashboards, or risk them rendering as blank screens post-upgrade.

Plugin Supply Chain Risk: The community-maintained plugin catalog is susceptible to "Zombie plugins" that have been abandoned by their creators and are incompatible with the latest Grafana version. While security policies enforce signature verification, administrators often resort to bypassing these checks via allow_loading_unsigned_plugins to use proprietary internal or niche tools, exposing the platform to security vulnerabilities.

The Unified Alerting Schism: Migration and Reliability Gaps

The transition from legacy alerting to "Unified Alerting" has been disruptive and problematic, characterized by migration failures and High Availability (HA) challenges.

  • Catastrophic Data Loss: Automated migration scripts are prone to failure, often failing to transfer contact points or notification policies correctly. Administrators have reported critical failure modes leading to the complete loss of alerting posture, effectively wiping out monitoring configurations.
  • Ghost Alerts: The migration can leave behind "ghost alerts"—remnants in the database that continue to fire but are invisible and unmanageable within the UI, forcing administrators to perform dangerous manual database surgery to stop them.
  • High Availability Deduplication: The HA implementation relies on a gossip protocol to synchronize state, which requires precise network configuration (TCP and UDP traffic on port 9094). Network misconfigurations can break the synchronization mesh, leading to a "split-brain" scenario where users receive duplicate notifications for the same alert event.
  • Thundering Herd Effect: The HA architecture dictates that all Grafana instances evaluate all alert rules, with deduplication occurring only at the notification stage. Scaling Grafana replicas consequently increases the read load on the backend Time-Series Database (TSDB), potentially causing performance degradation on the monitoring infrastructure itself.

Governance Crisis and Observability-as-Code Friction

Governance is one of the largest human-process challenges in the Grafana ecosystem, driven by dashboard sprawl and immature management tooling.

  • Dashboard Sprawl: The platform's ease of use leads to rapid proliferation of "shadow dashboards" that are cloned for temporary needs. These dashboards often lack documentation, becoming "ghostware" that is unintelligible to anyone other than the creator, complicating version control and diluting the value of the observability platform.
  • JSON Unreadability: The native export format for dashboards is a monolithic, unreadable JSON blob. Managing this JSON in Git is problematic because minor UI edits can reorder the entire structure, creating large diffs that make code reviews impossible and merge conflicts frequent.
  • Tooling Fragmentation: Tooling intended to address this, such as Terraform, provides a poor authoring experience. The interactive nature of Grafana causes immediate state "drift" when manual UI edits are made during an incident, which Terraform struggles to reconcile, risking the removal of critical visibility during the next terraform apply.

Legal Risks and Financial Volatility

The commercial strategy surrounding Grafana introduces distinct legal and financial risks for large organizations.

The AGPLv3 Legal Compliance Minefield

Grafana Labs’ shift to the Affero General Public License v3 (AGPLv3) introduces legal ambiguity that concerns enterprise legal teams. The AGPLv3 requires that if a user interacts with a modified version of the software over a network, the source code must be made available to them. This murkiness regarding the definition of a "derivative work" often leads to blanket bans on AGPL software in major technology companies to avoid the risk of having to open-source proprietary internal tools that link to Grafana.

The Mandatory Security Tax

Grafana Labs uses "feature gating," locking critical security and governance features behind the expensive Enterprise license. Features considered baseline requirements for enterprise security and compliance—such as Single Sign-On (SAML, OIDC), detailed Role-Based Access Control (RBAC), and comprehensive Audit Logs—are unavailable in the Open Source Software (OSS) version. This forces organizations in regulated industries to pay significant licensing fees to access essential security infrastructure.

Grafana Cloud "Billing Shock"

For organizations that choose Grafana Cloud to avoid operational burdens, the usage-based financial model introduces cost volatility.

  • Unpredictable Billing: Billing is based primarily on "Active Series" and "Data Points Per Minute" (DPM), which is difficult to predict and control.
  • High-Cardinality Spikes: A simple misconfiguration, such as an OpenTelemetry exporter generating high-cardinality attributes (e.g., unique trace IDs as labels), can cause the number of active series to explode instantly. This results in massive, unexpected bills ("billing shock") at the end of the month, as standard monitoring patterns often generate financially punitive high cardinality by default.
 

Summary: The True Cost of Flexibility

The core problem with Grafana is the paradox that its immense flexibility demands disproportionate investment in engineering resources and governance to mitigate its inherent stability risks.

  • If you choose to self-host: The TCO is heavily burdened by the internal cost of skilled SRE labor (easily exceeding $300,000 per FTE) required to manage the operational complexity, database locking issues, and breaking upgrades.
  • If you choose the Managed Cloud: You trade operational stability for financial risk, exposing yourself to unpredictable usage-based billing and the mandatory "security tax" required to enable basic enterprise authentication features.

Ultimately, Grafana must be treated not as a simple utility, but as a complex platform requiring dedicated investment in governance, cost control, and lifecycle management to prevent the visibility it provides from coming at the cost of stability, security, and budget.