42 articles on observability — what drives the cost, how it is priced, and where the savings actually are.
Distributed tracing is billed by span or trace volume, so capturing every request gets expensive at scale. Intelligent sampling keeps the diagnostic value while cutting the volume you pay for. Here is how sampling affects cost.
Datadog bills by hosts, log ingestion and retention, custom metrics, and per-product SKUs, and it can quietly become one of your largest cloud-adjacent bills. Here is how to find and cut the drivers of Datadog cost.
Logs are cheap to write and expensive to keep forever. A retention strategy that tiers, expires, and archives logs by value cuts storage and ingestion cost without losing what you actually need. Here is how to build one.
Observability data (metrics, logs, and traces) is billed by volume, and unmanaged it can rival your compute bill. A cost-aware approach controls what you collect, how long you keep it, and how you sample. Here is the playbook.
Self-hosted Prometheus trades a per-metric SaaS bill for the cost of running and operating your own monitoring stack. Which is cheaper depends on scale, cardinality, and how much operational time you can spend. Here is the comparison.
X-Ray bills about $5 per million traces recorded and about $0.50 per million traces retrieved or scanned, with a free tier. Sampling is the main lever, since you rarely need every request traced. Here is how the tracing model prices.
Application performance monitoring (Datadog, New Relic, and others) prices by hosts, ingested data, or usage, and it is a notoriously large observability bill. Host right-sizing, data filtering, and sampling cut it. Here is how.
Log platforms (Datadog Logs, Splunk, Elastic, Loki, cloud-native) price by ingestion, indexing, and retention, with very different models. Ingest less, index selectively, and tier retention to cut the bill. This compares the approaches.
Distributed tracing captures request paths across services, priced by spans or traces ingested, so high-traffic systems generate enormous volume. Intelligent sampling is the primary cost lever. Here is how.
Data observability tools monitor data quality, freshness, and pipeline health, priced by tables or volume monitored plus the queries they run to check data. Scoping monitoring to critical data and efficient checks cut it. Here is the model.
CloudWatch, Azure Monitor, and Google Cloud Monitoring all bill on metrics, logs ingested, and API calls, with generous free tiers for platform metrics. Logs and custom metrics drive the bill. This compares them and the shared levers.
Amazon Managed Service for Prometheus bills per metric sample ingested, per query processed, and for storage. High-cardinality metrics and short scrape intervals drive ingestion cost. Here is how the model works and how to control cardinality.
Amazon Managed Grafana bills per active user per month, by role (editor or viewer), plus optional add-ons. Cost scales with your user base, not dashboards or data. Here is how the per-user model works and how to keep it lean.
Cloud Logging charges about $0.50 per GB ingested after a 50 GB monthly free allowance, plus retention beyond the default. Exclusion filters and sampling are the main levers. Here is how the ingestion-based model works and where cost hides.
CloudWatch alarms cost about $0.10 each per month (standard), $0.30 for high-resolution or anomaly-detection, and $0.50 for composite. A few are trivial; thousands generated per-resource across a fleet add up. Here is how to keep it sane.
Application Insights bills by data ingested (around $2.30 per GB after a free allowance) plus retention beyond 90 days. Sampling and trimming noisy telemetry are the main levers. Here is how the ingestion-based model works.
Custom metrics are $0.30/metric/month, but each metric-name + dimension-value combination is a separate billable metric, so high cardinality explodes the bill. Here's how to control it.
Ingestion at $0.50/GB dominates CloudWatch Logs bills. Source-side filtering, structured logging, IA tier, and S3 migration for high-volume groups typically cut the bill 50-80%. Here's the full breakdown and migration playbook.