Observability Cost
Why Your Datadog Bill Will Double Next Year — And How to Stop It
The three pricing mechanisms that make Datadog spend grow faster than engineering headcount — and the policy changes that stop them without a migration.
Executive analysis for
observability economics,
and AI in production.
A collection of executive-level engineering analyses. Each piece answers a business question about cost, architecture, migration risk, or why observability spend keeps growing.
All
Observability Cost
Migration
Architecture
FinOps
Topic clusters
Observability Economics
Why observability costs keep increasing — the organizational patterns that make telemetry spend grow faster than engineering value.
Migration Playbooks
Datadog → OpenSearch, Elastic → OpenSearch, and hybrid coexistence — with parallel-run methodology and rollback criteria.
Architecture & Scale
OpenSearch cluster design, ISM policy, index lifecycle management, and cardinality control at enterprise volume.
All articles
Migration
Datadog vs OpenSearch: Real TCO Comparison for a 50-Engineer Team
Infrastructure, operational overhead, and engineering time across a 12-month production migration — with real numbers from production deployments.
Observability FinOps
What Is Metric Cardinality Bloat — and Why It Explains Your Datadog Invoice
Cardinality bloat is the single largest driver of unexpected Datadog cost growth. Here is how it compounds, and how to measure it in your environment.
Architecture
OpenSearch Is Not a Degraded Elasticsearch — Here Is Why I Use It in Production
After running OpenSearch at LVMH-scale across 12TB/day of telemetry, a direct comparison on performance, feature parity, and operational cost.
Observability FinOps
Why Platform Teams Inherit an Observability Bill They Didn't Create
The organizational pattern that splits cost ownership from cost generation — and how to fix it without a platform rewrite.
Migration
How to Run a Parallel-Run Migration from Datadog to OpenSearch
The methodology that eliminates big-bang cutover risk: running both stacks in production simultaneously until the team has operational confidence in the new one.