Best of Grafana — April 2026

  1. 1
    Article
    Avatar of grafanaGrafana Labs·6w

    Kubernetes Monitoring Helm chart v4: Biggest update ever!

    Grafana's Kubernetes Monitoring Helm chart v4 is a major overhaul addressing real pain points from v3. Key changes include: converting destinations and collectors from lists to maps (enabling proper multi-file merging and named overrides), replacing hard-coded collector names with user-defined collectors using composable presets, making telemetry service deployments explicit to avoid surprise duplicates, splitting the overloaded clusterMetrics feature into three focused features, separating pod log collection methods into distinct features with native OTLP support, replacing the bulk labelsToKeep approach with explicit opt-in label declarations (reducing memory usage), and allowing granular control over individual profiler types. A migration tool is available to convert v3 values files to v4 format automatically.

  2. 2
    Article
    Avatar of grafanaGrafana Labs·5w

    Introducing Pyroscope 2.0: faster, more cost-effective continuous profiling at scale

    Pyroscope 2.0 is a ground-up rearchitecture of the open source continuous profiling database. Key changes include eliminating write-path replication (each profile written once to object storage instead of 3x), data co-location that reduces symbol storage by up to 95%, and a fully stateless read path enabling elastic query scaling. Rollouts that previously took 8-12 hours now complete in minutes. The new architecture also enables metrics derived from profiles, individual profile inspection, and heatmap queries. Pyroscope 2.0 has been running in Grafana Cloud since April 2025, processing 19.5PB of data. Native OTLP profiling support is included. Object storage is now required for distributed deployments.

  3. 3
    Article
    Avatar of grafanaGrafana Labs·6w

    Grafana Alerting: Respond faster and get situational awareness with alert enrichment in Grafana Cloud

    Grafana Cloud has introduced alert enrichment, a new public preview feature for Grafana Alerting that attaches contextual information to alerts before they reach on-call responders. Instead of bare signals like 'CPU usage is high,' enriched alerts can include relevant log lines, AI-powered explanations, links to dashboards, automated ML investigations via Sift or Assistant, dynamic templated annotations, and data fetched from external APIs or data sources like Loki and Mimir. Seven enricher types are available: Assign, External, Datasource, Sift, Knowledge Graph, Explain, and Assistant. Enrichments can be scoped per alert rule or applied globally across all alerts by label/annotation filters. The goal is to automate the first triage steps so engineers can focus on resolution rather than context-gathering.

  4. 4
    Article
    Avatar of grafanaGrafana Labs·5w

    Introducing o11y-bench: an open benchmark for AI agents running observability workflows

    Grafana Labs has open sourced o11y-bench, a benchmark for evaluating AI agents on observability workflows. Built on the Harbor framework, it runs agents against a real Grafana stack with synthetic metrics, logs, and traces, then grades them on 63 tasks spanning PromQL queries, LogQL, TraceQL, multi-step incident investigations, and dashboard editing. The benchmark uses two headline metrics — Pass^3 (consistency across three runs) and Pass@3 (best-of-three success) — prioritizing reliability over one-off success. Initial results across 29 model variants showed Claude Opus 4.7 (reasoning off) leading on consistency, with Qwen 3.6 Plus as the top open-source model. Dashboarding tasks proved hardest due to combined state, query correctness, and variable wiring requirements. The project is open source and accepts community contributions to its HuggingFace leaderboard.

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