Executive Summary

About 200,000 engineering teams running Prometheus-compatible stacks struggle with dashboards and alerts that silently lose short-lived but critical spikes because coarse downsampling and poor query-step selection turn incidents into averaged noise, causing missed alerts and expensive firefighting. Platform and SRE teams bear the brunt of this pain, especially as retention and compute costs force more aggressive aggregation. You could build a lightweight tool that analyzes metric series and automatically picks per-query step and downsampling strategies to preserve spike fidelity, surfaced as a Grafana plugin and an API that rewrites queries or emits recommended retention/aggregation rules. It would integrate with Prometheus, Cortex, Thanos, and existing pipelines so platform teams can adopt it without replacing collection layers. The market looks attractive now: with cloud-native adoption rising and a TAM of roughly $12.0B (200K teams × $60K ACV), trend data shows teams are shifting from raw ingestion to smarter, cost-saving signal-preserving techniques, and analyst scores (market score 88/100, revenue potential 85/100) support demand. You could differentiate by focusing explicitly on measurable spike-preservation guarantees and low-touch integration (heuristics + targeted ML), offering SLO-style metrics for signal fidelity rather than a wholesale observability replacement; the challenges are high competition and the need for strong integrations and convincing evidence that preserving spikes reduces noise without increasing false positives.

Market Opportunity

Preserve critical monitoring spikes by adaptive query-step selection in dashboards targets a $12.0B = 200K engineering teams × $60K ACV total addressable market with high saturation and a year-over-year growth rate of 17% YoY (MarketsandMarkets/industry estimates for observability & monitoring, 2024).

Key trends driving demand: Cloud-native adoption — more teams run Prometheus-compatible stacks, increasing demand for tools that improve signal fidelity without replacing collection layers.; Cost-conscious observability — teams shift from raw ingestion to signal-preserving techniques to reduce costs while keeping actionable insights, creating demand for smarter downsampling.; Shift-left SRE/DevOps — platform teams seek low-toil solutions that automatically enforce best practices like spike preservation across dashboards and alerts.; ML-assisted observability — lightweight ML models for anomaly detection and pattern prioritization are now practical at scale and enable adaptive resolution strategies..

Key competitors include Grafana Labs, Datadog, New Relic.

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Preserve critical monitoring spikes by adaptive query-step selection in dashboards

Executive Summary

Market Validation

Market Opportunity

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