If you're choosing an open-source observability stack in 2026, you've probably landed on two options: assemble one from Grafana ecosystem components, or use a unified platform like OneUptime. Both are legitimate choices. This post breaks down what each approach actually looks like in practice, where each shines, and where each falls short.

No "10 reasons why X is better" listicle. Just an honest look at two different philosophies for solving the same problem.

Two philosophies, one goal

The Grafana ecosystem follows a best-of-breed approach. You pick specialized tools for each observability signal and wire them together. Grafana visualizes. Prometheus scrapes metrics. Loki collects logs. Tempo stores traces. Grafana Cloud IRM handles alerting, on-call routing, and incident management. You might add a separate status page tool on top.

One important 2026 caveat: the standalone open-source Grafana OnCall went into maintenance mode in 2025 and was archived (repo made read-only) in March 2026, with its functionality folded into the cloud-only Grafana Cloud IRM. If you self-host, on-call routing now means Alertmanager plus a third-party paging tool rather than a supported open-source Grafana component.

OneUptime follows a unified approach. One platform handles monitoring, metrics, logs, traces, error tracking, status pages, incident management, and on-call - all in one codebase, one deployment, one interface.

Neither philosophy is inherently superior. The right choice depends on your team size, existing infrastructure, and operational priorities.

The Grafana stack: what you're actually assembling

A typical Grafana-based observability stack looks like this:

That's a minimum of five to seven separate systems, each with its own configuration language, upgrade cycle, and failure modes.

Where Grafana excels

Visualization depth. Grafana dashboards are best-in-class. The query editor, panel options, and plugin ecosystem are unmatched. If your team lives in dashboards and needs highly customized views, Grafana is hard to beat.

PromQL maturity. Prometheus and PromQL have years of battle-testing behind them. The query language is expressive, well-documented, and understood by most SRE teams. Recording rules, alerting rules, and federation patterns are well-established.

Ecosystem breadth. There are Prometheus exporters for nearly everything. The CNCF ecosystem is built around Prometheus metrics. If you're already running Kubernetes, Prometheus is likely already there.

Flexibility. Want to swap Loki for Elasticsearch? Tempo for Jaeger? You can mix and match components. No vendor lock-in within the stack itself.

Community. The Grafana and Prometheus communities are massive. Stack Overflow answers, blog posts, conference talks - you'll rarely hit a problem nobody has seen before.

Where Grafana gets painful

Operational overhead. Each component is a separate deployment with its own scaling characteristics. Prometheus needs persistent storage and careful retention tuning. Loki's ingester and querier need separate scaling. Tempo needs object storage configuration. Alertmanager is yet another service to wire up. Multiply this by staging and production environments, and you have a lot of infrastructure to maintain.

Configuration sprawl. Prometheus uses YAML with its own syntax. Alertmanager has its own configuration format. Loki has a different configuration schema. Grafana dashboards are JSON. On-call schedules live in OnCall. There's no single place to see your entire observability configuration.

Correlation challenges. Jumping from a metric spike in Grafana to the relevant logs in Loki to the specific trace in Tempo is possible but requires careful label alignment. You need consistent labels across all three signals, and the "Explore" workflow still involves manual context-switching between data sources.

Status pages, on-call, and incident management. Grafana's incident management and on-call now live in Grafana Cloud IRM, which is cloud-only - the open-source Grafana OnCall was archived in March 2026. Self-hosted Grafana has no built-in incident management, on-call, or public status pages. You'll route alerts through Alertmanager and add separate tools - Cachet, Statuspage.io, or something custom - for the rest.

Cost at scale. Grafana Cloud pricing is competitive but can grow quickly with high cardinality metrics and log volume. Self-hosted avoids the bill but adds operational cost. Either way, running five-plus services isn't free.

OneUptime: what you get in one platform

OneUptime bundles the following into a single deployment:

Where OneUptime excels

Operational simplicity. One deployment. One database. One upgrade path. For small-to-mid-size teams that don't want to become observability infrastructure operators, this matters enormously. You deploy OneUptime and get monitoring, logs, traces, status pages, and on-call out of the box.

Built-in status pages. Public and private status pages with custom domains, subscriber notifications, and SSO are included. No separate tool required. When an incident triggers, the status page updates automatically through workflows.

Integrated incident lifecycle. Monitor fires an alert → on-call engineer gets paged → incident is created → status page updates → postmortem is generated. This entire flow happens in one system with full context. No jumping between five different UIs.

OpenTelemetry native. OneUptime accepts OpenTelemetry data natively. If you're already instrumented with OTel (and you should be), you point your exporters at OneUptime and get metrics, logs, and traces in one place. No separate backends for each signal.

Fully open source. The entire codebase is open source - not open core. The same code runs on the SaaS platform and self-hosted deployments. Enterprise support and on-prem deployment options are available for teams that need them.

Pricing transparency. SaaS pricing is usage-based at $0.10/GB for telemetry ingestion. No per-host pricing, no per-container surcharges, no hidden costs for custom metrics. Self-hosted is free.

Where OneUptime falls short

Dashboard customization. OneUptime's dashboards are functional but don't match Grafana's depth. If your team needs 30 highly customized panels with complex PromQL transformations and template variables, Grafana's visualization layer is more powerful.

PromQL. OneUptime doesn't use PromQL. It uses its own query interface for metrics. Teams deeply invested in PromQL queries and recording rules will need to adapt.

Ecosystem integrations. Grafana has thousands of community dashboards and data source plugins. OneUptime's integration surface is growing but smaller. If you need to visualize data from 15 different sources in one dashboard, Grafana has more connectors today.

Community size. Grafana and Prometheus have larger communities. When you hit an edge case, there are more people who've been there before.

Cost comparison: real numbers

Here's a rough comparison for a mid-size team (50 engineers, 200 services, moderate telemetry volume):

Grafana Cloud

Grafana self-hosted

OneUptime SaaS

OneUptime self-hosted

These numbers will vary based on your actual volume, but the pattern holds: OneUptime's unified approach is significantly cheaper to operate, especially when you factor in the engineering time to maintain a multi-component Grafana stack.

When to choose the Grafana stack

• You already have Prometheus and Grafana running and they're working well. Don't rip and replace what works.
• You need deep dashboard customization with complex PromQL queries, template variables, and community dashboard imports.
• You have a dedicated platform/SRE team that can operate and maintain multiple observability services.
• You need to aggregate data from many heterogeneous sources - databases, message queues, custom exporters - into unified dashboards.
• Your organization has standardized on specific components and swapping them out isn't realistic.

When to choose OneUptime

• You want monitoring, status pages, incident management, and on-call in one platform without operating five separate systems.
• You're a small-to-mid-size team that can't afford to dedicate engineering time to maintaining observability infrastructure.
• Status pages and incident management matter as much as metrics and logs to your organization.
• You want to self-host everything with a single deployment rather than orchestrating multiple services.
• You're already using OpenTelemetry for instrumentation and want a backend that natively accepts all three signals.
• You're migrating away from expensive commercial tools (Datadog, PagerDuty, StatusPage.io) and want a single replacement rather than assembling six open-source tools.

Can you use both?

Yes. A common pattern is running Prometheus and Grafana for infrastructure metrics (especially in Kubernetes environments where they're already deployed) while using OneUptime for uptime monitoring, status pages, incident management, and on-call. OneUptime accepts OpenTelemetry data, so it can complement rather than replace existing metric pipelines.

This isn't an all-or-nothing decision.

The bottom line

The Grafana ecosystem gives you maximum flexibility and depth at the cost of operational complexity. OneUptime gives you an integrated experience with less overhead at the cost of some customization depth.

For teams that want to focus on building product rather than operating observability infrastructure, the unified approach saves real time and money. For teams with dedicated platform engineering resources and complex visualization needs, the Grafana stack's flexibility is worth the operational investment.

Both are honest, open-source approaches to observability. Pick the one that matches your team's capacity and priorities - not the one with the best marketing.