Migrating Observability from Grafana to Kloudfuse

Migrating Observability from Grafana to Kloudfuse

Table of Contents

Moving from a Grafana-based stack to Kloudfuse starts with understanding how they differ.

Grafana’s tools, like Prometheus, Loki, and Tempo, each work well on their own. But bringing them together takes extra effort. You have to manually connect the pieces, align labels, and maintain the integrations yourself.

Kloudfuse takes a different approach. It uses open standards like OpenTelemetry for instrumentation and PromQL, LogQL, TraceQL, SQL, and GraphQL for querying. Everything runs through a single, unified data system. There’s less manual work, and everything just fits.

The pricing model is simple and flat, with no hidden overages or surprises. You also avoid vendor lock-in and reduce the chances of missing critical signals.

Kloudfuse also includes key features that Grafana doesn’t. One of those is Digital Experience Monitoring (DEM), which shows how real users are experiencing your product, something backend metrics alone can't capture.

This guide will walk through a practical migration plan for each observability pillar (metrics, logs, traces). It is a complete walkthrough from start to finish, so that your observability is migrated successfully from Grafana to Kloudfuse.

Why Migrate to Kloudfuse?

Grafana stacks work, but at scale, they often feel like duct-taped parts. Kloudfuse streamlines observability with a platform built for simplicity, scale, and modern telemetry.

Inventory Current Observability

Start by treating your existing stack like an audit. For every service or application, build a checklist of what telemetry is being produced and where it’s going.

Loop in SREs, developers, and ops teams. People often build sidecar containers or cron jobs that quietly push data somewhere. These can easily be overlooked. Make sure everyone contributes to mapping out every data flow.

Finally, measure the volume and behavior of your data. Look at metrics sample rates, logs per second, label or field cardinality, and how long you’re retaining data. This gives you a clear idea of what your system produces and helps size your Kloudfuse plan correctly. It also flags anything that could drive up costs later.

Migration Strategy

Once your observability audit is complete, the next step is to choose how you'll migrate. There are several common approaches, each with trade-offs.

Parallel (Dual-Run) Migration

Configure each service to send telemetry to both the Grafana stack and Kloudfuse at the same time. This lets you compare data outputs and test Kloudfuse using live traffic, while keeping Grafana available as a fallback. The downside is the cost and complexity, as you’ll temporarily double your ingest volume. Still, this is the safest option for large systems, especially where rollback needs to be quick.

Phased Rollout

Migrate one part of the stack at a time. For example, start with metrics in a non-production environment, then move to production, and leave logs and traces on Grafana for now. Or migrate lower-risk applications before core services. This approach spreads out risk and gives more control, but it extends the period where you're maintaining two systems.

Big-Bang Cutover

Switch everything at once during a planned maintenance window. Reconfigure agents to send all data to Kloudfuse and shut down Grafana ingestion. This is the simplest plan operationally, with no dual-running, but it carries the most risk. If something fails, rollback may not be straightforward.

Hybrid Approach

Many teams take a blended path. Start with a parallel run for a small subset, like staging environments or a few non-critical services. After validation, cut those over completely, then repeat with the next group. This crawl-walk-run strategy balances speed with safety.

Whatever approach you take, write it down and share it. Define which services or streams will migrate when, and how failures or rollbacks will be handled. Clear coordination avoids confusion and keeps the team aligned.

Migration by Stream

In this section, we will discuss migration per stream to Kloudfuse. We will start with the metrics.

Metrics Migration

For metrics, Kloudfuse supports open standards (Prometheus metrics, OpenTelemetry metrics) natively. In practice, you can reuse most of your existing pipelines:
Remote_write:
- url: http:///write

Once Prometheus restarts, it will stream all samples into Kloudfuse. This preserves all your existing exporters and scrape configs.
prometheus:
remoteWrite:
    - url: http:///write
configs:
    # copy your scrape configs here

This tells Grafana Agent to scrape your endpoints and push metrics to Kloudfuse. It’s lighter-weight than running a separate Prometheus.

With metrics now streaming into Kloudfuse, you’ll see familiar dashboards pop up. Each chart or PromQL query in Grafana should work or require minimal adjustments. Use Kloudfuse’s Metrics Explorer (which understands PromQL) to spot-check graphs against Grafana’s. This is the time to catch any missing metrics or cardinality explosions (e.g., if a label changed).

Logs Migration

Migrating logs is usually straightforward since Kloudfuse supports Grafana’s LogQL and common ingestion endpoints. The main task is repointing your log shippers:
[OUTPUT]
    Name        http
    Match       *
    Host       
    Port        443
    TLS         On
    URI         /ingester/v1/fluent_bit
    Format      json_lines

This configures Fluent Bit to send logs to Kloudfuse’s HTTP ingestion endpoint. The setup for Fluentd follows a similar pattern.
<match **>
@type http
endpoint http://:80/ingester/v1/fluentd
@type json

    chunk_limit_size 1MB
    flush_interval 10s

After updating and restarting the agent, logs will flow into Kloudfuse instead of Loki. Test by sending a few log lines and checking the Kloudfuse Log Explorer to confirm they arrive.
{
"user": "alice", 
"Status":200,
"path":"/"
}

A log like the one above will let you filter by user or status right away. You can still tweak parsing rules if needed, but many teams find their existing logs “just work” on the new platform.
level="error"
| timeslice 2m
| count by (_timeslice, service)
| outlier(_count) by 2m, model=dbscan, eps=2

This query slices error logs every 2 minutes per service and applies DBSCAN outlier detection. This helps you spot services generating anomalous error rates compared to peers.

Throughout this phase, compare the log streams side by side. Ensure that log lines from each source appear in Kloudfuse (no gaps in time) and that indexed fields look correct. If anything is missing (say, an agent failed to start), fix the config and re-run that subset. Once all logs are visible, you have full log observability on Kloudfuse.

Tracing/APM Migration

Moving distributed tracing works much like migrating metrics. Replace proprietary or legacy agents with OpenTelemetry and direct the data to Kloudfuse.
service = "checkout" and status_code = 500

to identify error spans in a specific service. Because Kloudfuse supports TraceQL, familiar queries remain possible.

At the end of this phase, you should be seeing complete trace maps and span lists in Kloudfuse. Validate by generating a known request (for example, an API call through your system) and checking the trace in Grafana’s Tempo versus Kloudfuse. Span timing, parent-child relationships, and overall trace structure should align closely across both systems. With traces and logs now converging in Kloudfuse, you gain full observability in a single platform.

Dashboards & Alerts Migration

With raw telemetry flowing in, it’s time to rebuild or import your dashboards and alerts in Kloudfuse:

Migration Tools

Kloudfuse provides helper scripts to migrate Grafana artifacts. For example, a Python script dashboard.py can download a Grafana dashboard JSON and upload it to the Grafana instance inside Kloudfuse.

You can batch-process entire directories of dashboards if needed. Alerts (Grafana Alertmanager or in-dashboard alerts) typically need to be recreated manually, but the logic stays the same.

Query Adjustments

After importing a dashboard, review each panel’s query. Change the data source name to Kloudfuse, and adjust any metric names or label names if necessary (e.g., if the metric prefix changed).

Since Kloudfuse supports PromQL, TraceQL and LogQL, any queries from Grafana Cloud or Grafana OSS should translate directly or with minimal syntax adjustments. For instance, a Loki query in a panel can point to Kloudfuse’s Logs dataset. The “no-vendor” approach of Kloudfuse means there’s usually not a new proprietary query language to learn, just the same familiar ones.

Advanced Alerting

Kloudfuse supports all basic alert types (threshold, budget, anomaly) and also some advanced ones like outlier detection and forecasting. As you recreate your alerts, consider using these new tools.

For example, if you had a hard-coded CPU threshold alert, you might instead try Kloudfuse’s anomaly alert, which learns normal baselines. This is a chance to refine noisy alerts or consolidate multiple alerts. Note that alerts in Kloudfuse are written against metrics/logs with PromQL-style queries.

Alert Testing Before Cutover

While both systems are active, test each critical alert. A simple way to do this is by temporarily lowering alert thresholds to force a trigger. This helps confirm that Kloudfuse is processing alert logic correctly and sending out notifications as expected.

Run the same test in both Grafana and Kloudfuse. Introduce a known event, like increased load or an intentional error, and check that alerts fire at the same time. This side-by-side validation helps ensure consistency and gives you confidence that your alerting setup remains reliable after the switch.

By the end of the migration, your Kloudfuse dashboards should reflect everything you had in Grafana. Each alert rule should have a clear, working equivalent.

The goal is for engineers to open Kloudfuse, recognize the dashboards they rely on, and see alerts working as expected. Everything should feel familiar, just running on a more streamlined platform.

System Validation & Cutover

With the above steps complete, it's time to validate your observability setup and execute the final cutover. Follow these steps to ensure a smooth transition:

1. Run both systems in parallel: Start by keeping both Grafana and Kloudfuse active. Compare metrics, logs, and traces across the two systems using representative queries like total request rate, error rate, and tail latency. The goal is to confirm that the data matches closely and that Kloudfuse is capturing everything accurately.

2. Monitor Kloudfuse’s ingestion health: Use the Kloudfuse UI to watch for any signs of ingestion lag, dropped data, or error spikes. Apply realistic load, such as traffic replays or canary deployments, to confirm that logs and traces appear correctly and are complete.

3. Investigate any ingestion or parsing issues: Check Kloudfuse’s internal error logs. If some logs aren’t being parsed or spans are missing, the logs will usually indicate why. Resolve any configuration issues before proceeding.

4. Keep Grafana in standby mode: Point your services and agents to Kloudfuse, but leave Grafana running without receiving new data. This gives you a fallback while testing, without shutting off the existing system completely.

5. Perform the cutover: Once you're confident in the system, shift all telemetry to Kloudfuse. This means reconfiguring services to stop sending data to Grafana, disabling any remaining agents or remote write configurations, and verifying that nothing is still targeting Loki or Tempo.

6. Monitor the go-live period closely: During the initial cutover window, monitor Kloudfuse for ingestion errors, alerting behavior, or any data gaps. Keep a close watch to catch any issues early and respond before they escalate.

7. Decommission the Grafana stack: After a few days of stable operation, shut down Prometheus servers, Grafana Agent pods, and cancel any related cloud services or licenses. It’s a good idea to keep read-only access to historical Grafana data, just in case.

8. Take advantage of advanced features: Now that you're fully on Kloudfuse, explore integrated tools like Prophet for forecasting, K-Lens for anomaly detection, and metric rollups to reduce storage use while preserving key trends.

9. Review and refine: Post-migration, continue auditing your telemetry setup. Remove unused signals, fine-tune alert thresholds, and adjust retention settings to stay efficient and focused on high-value data.

Best Practices

Moving observability is similar to redirecting the utilities of a city. Below are some best practices that can be used to guide the migration:

Conclusion

Migration from the Grafana ecosystem to Kloudfuse requires planning and team alignment. Begin with an audit, execute both systems side by side, check results, and then cut over with confidence.

The result is a single observability platform that's easier to manage, simpler to scale, and more cost-projectable. And since Kloudfuse natively supports open query languages and formats, your team maintains its current skills and flexibility.

You’ll come out of the migration with better visibility and a modern observability foundation that grows with you.