If you run high-volume load tests, your metrics filesystem is going to grow rapidly. A single k6 execution containing 1000 virtual users running over 30 minutes can easily dump several gigabytes of raw NDJSON point data. Storing these metrics on local disks is not practical: it makes historical comparisons impossible and creates an operational nightmare. To analyze trend distributions over time, you need a scalable time-series database (TSDB).
Let us look at the three primary observability architectures for ingesting and analyzing k6 performance metrics, mapping out their technological tradeoffs.
Comparing InfluxDB, Prometheus, and Grafana Cloud
Choosing the right metric backend depends on your infrastructure scale, security boundaries, and team size. Let us break down the architectural dimensions of the three leading time-series stacks:
| Architectural Dimension | Local: InfluxDB + Grafana OSS | Remote: Prometheus Remote Write | Enterprise: Grafana Cloud Ingestion |
|---|---|---|---|
| Data Ingest Pattern | Push-based (native k6 direct output) | Push-based remote write adapter | Native direct cloud streaming |
| Storage Technology | Time-Structured Merge Tree (TSM) | Prometheus TSDB (Block storage) | Distributed Cortex / Mimir |
| Query Language | InfluxQL / Flux | PromQL | PromQL / LogQL |
| Cardinality Handling | Moderate (Requires tagging optimization) | Limited (High cardinality degrades TSDB) | Advanced (Horizontally scales tags) |
| Retention & Downsampling | Custom continuous database queries | Basic retention policies | Automated long-term retention |
1. The Local Option: InfluxDB and Grafana OSS
For ad-hoc local tests or secure, isolated air-gapped environments, the traditional local push setup is highly effective. You configure k6 to push metrics directly to InfluxDB using the built-in collector, and build a local Grafana instance to display them. This approach is completely free, self-hosted, and requires zero external network access.
However, InfluxDB's Time-Structured Merge (TSM) engine has limits when it comes to index scaling. If your k6 scripts generate highly unique tags (such as recording individual user IDs or dynamic trace identifiers on every request), you will encounter index bloat. This high tag cardinality slows down query execution, causing your local dashboards to stall.
2. The Orchestration Option: Prometheus Remote Write
If you run k6 within Kubernetes clusters (especially when orchestrating tests via the k6-operator), using Prometheus Remote Write is the industry standard. Instead of scraping, k6 pushes metrics to a remote write adapter, which forwards them directly to your existing Prometheus instance.
This allows you to leverage your existing monitoring stack and write standard PromQL queries to build dashboards. The limitation here is memory overhead. Because Prometheus TSDB relies on memory mapped files, heavy load tests can lead to massive RAM consumption during metrics spikes. You must configure strict tag filtering in k6 to keep cardinality manageable.
3. The Scale Option: Grafana Cloud Ingestion
For large enterprise teams running continuous regression validation across multiple CI/CD pipelines, Grafana Cloud is the preferred architecture. It uses Mimir (a highly distributed, horizontally scalable time-series database) to ingest metrics.
Grafana Cloud handles massive tag cardinality without slowing down, keeping historic data archived and accessible for long-term regression analysis. The engine aggregates data points automatically, alerting you to p95 and p99 latency regressions over days or weeks. This is the only realistic way to manage metrics scale across a large engineering organization.
To learn how to configure the raw outputs that drive these databases, read k6 Reporting: Real-time Web Dashboards and Headless NDJSON Streaming. If you want to automate this database push within CI/CD pipelines, check out k6 in CI/CD: Automated Pipeline Orchestration in Azure DevOps and GitHub Actions. If you are developing these scripts on a Windows terminal, see our operational guide on Building custom k6.exe Binaries on Windows using xk6.