Grafana Cloud Alternative

Gigapipe does not build dashboards. Grafana and Perses already do that well, and Gigapipe works with both. It works a layer below, on the two things in observability that are actually broken.
Dashboards are so 2016
Observability has two structural problems. Building a better chart solves neither of them.

Pricing decides your architecture

When the bill scales with how much you observe, retention windows and sampling rates stop being engineering decisions. They get made above the engineers who need the data, on a spreadsheet, by people who will never read the trace that was dropped.

Split storage decides what you can ask

Telemetry arrives as one story and gets filed into four systems. Every question that crosses them costs a join you perform yourself, in three query languages, and the correlations nobody has time to write are the ones that would have found the problem.

Gigapipe works on both.

A free Grafana Cloud account, without the limits
Look closely at what the free tier actually caps. Almost all of it is storage, and storage is the part Gigapipe takes over.
10,000 metric series
Stops binding

Grafana is not storing your metrics, so there is no active series count to exceed.

50 GB logs, traces, profiles
Stops binding

Ingestion goes to Gigapipe. Nothing is written into Grafana Cloud storage to be measured.

14-day retention
Stops binding

History is bounded by the storage you run, not by the tier you are on.

3 users
Still applies

This one is a seat limit rather than a storage limit, so it holds. Self-hosted Grafana has no seat cap.

Grafana Cloud AI features run against the datasource rather than against Grafana-stored data, so they work the same way when the datasource is Gigapipe.

Why the storage shape decides what AI can do
The interface is not the hard part any more. The layout of the data underneath it is.

Four stores, four answers

Loki holds the logs, Prometheus the metrics, Tempo the traces, Pyroscope the profiles. Any question that spans them is four queries in three languages, joined after the fact.

Correlation becomes the model's problem

When the data is split, an assistant has to reassemble it on every question. Reasoning quality then depends on how well it stitches, not on what your telemetry actually says.

One store lets Gigapipe correlate natively

Keeping all four signals in one place is what lets Gigapipe correlate them natively, instead of leaving an assistant to reassemble the picture on every question.

Gigapipe compared with Grafana Cloud
Storage shape
One store holding logs, metrics, traces and profiles together.
Loki, Prometheus, Tempo and Pyroscope: four services, four datasets.
Cross-signal queries
A join inside one database.
Four queries and a correlation performed after the fact.
What you pay for
OSS is free, always. Cloud is the machine in your contract.
Per active series, per GB of logs, traces and profiles, per user.
Free tier
The whole product, on your own hardware, with no caps.
10,000 series, 50 GB per signal, 3 users, 14-day retention.
Retention
However long your object storage holds.
A billing decision.
Where data lives
Your own storage.
Grafana-operated cloud.
AI over your telemetry
Cloud anomaly detection runs inside your own deployment.
Your data is sent to Grafana's AI infrastructure to be processed.
Visualisation
Grafana or Perses, through their built-in datasources.
Grafana.
What you operate
One binary and your storage.
Nothing, until the bill makes you self-host the LGTM stack.
OpenTelemetry
Native OTLP over gRPC and HTTP, for all four signals.
OTLP accepted, then split across the four backends.
Point Grafana at Gigapipe
Gigapipe answers the Loki, Prometheus, Tempo and Pyroscope APIs, so Grafana connects through its built-in datasources. There is no plugin to install and no dashboard to rewrite.
datasources.yaml
apiVersion: 1
datasources:
  - name: Gigapipe Logs
    type: loki
    url: https://<your-gigapipe-host>
  - name: Gigapipe Metrics
    type: prometheus
    url: https://<your-gigapipe-host>
  - name: Gigapipe Traces
    type: tempo
    url: https://<your-gigapipe-host>
  - name: Gigapipe Profiles
    type: grafana-pyroscope-datasource
    url: https://<your-gigapipe-host>
Grafana Cloud alternative FAQ
Do I have to stop using Grafana?
Does a free Grafana Cloud account really work with it?
What about Grafana Cloud AI features?
How is this different from self-hosting the LGTM stack?
How does the pricing compare?
What does moving actually involve?
Keep Grafana. Own the data underneath.