Confusing logs billing hurts adoption — add per‑GB ingest & query SKUs

Many mid-to-large engineering organizations stall or avoid adopting log analytics because billing is opaque and unpredictable; roughly 200,000 such teams represent a $30B observability market (about $150K ARR each), and unexpected per-ingest and per-query charges are a recurring adoption blocker. Cloud-native, ephemeral workloads and high-cardinality telemetry magnify the problem—teams routinely see 2x–5x variance month-to-month and lack the tooling to tie costs back to services or deployments. You could build a productized per‑GB ingest and per‑GB query SKU model that exposes unit pricing, live meters, programmable budget controls, and retention- and tag-based cost profiles so teams can predict and optimize spend at the service or team level. Pair this with AI-driven cost forecasting and anomaly detection, audited metering and anti-gaming controls, SDKs/integrations for accurate client-side measurement, and migration tools plus transparent invoices that break costs down by source, retention, and query type. Executing this will require strong engineering around metering accuracy and careful packaging to avoid cannibalizing higher-margin offerings. The timing is favorable: usage‑based billing preferences, accelerating cloud-native adoption, and emerging AI ops make cost transparency a top buying criterion, and even a 1% share of the $30B market implies roughly $300M ARR potential. To stand out against medium competition you’ll need enterprise-grade metering, turnkey migration and ecosystem integrations, and a candid go-to-market that targets cost-sensitive, high-volume teams first while acknowledging some customers will still prefer managed or bundled pricing.