How Datadog saves over $1 million each month by optimizing AI usage

Datadog describes how it reduced AI-related engineering spending by monitoring usage across providers, models, token types, teams, and workflows through its Cloud Cost Management platform and an AI gateway. After evaluating models against more than 140 Datadog-specific engineering tasks, the company changed its default model from Claude Opus 4.8 to Sonnet 4.6, accepting an 8% reduction in measured proficiency for a 36.7% cost reduction and attributing more than $687,000 in monthly savings to the shift. It also lowered Claude Code’s default effort setting from high to medium, reporting over $288,000 in monthly savings. Automated cost alerts and workflow-based Slack notifications encouraged users with high spending to adopt savings practices, reducing spending by more than $150,000 over one week among newly alerted users. Datadog also tested the context-optimization tool Headroom, which filters, deduplicates, and compresses tool results before they reach language models; a pilot involving over 1,000 engineers showed 27% lower cost per user and substantially lower token consumption. The company emphasizes continuous evaluation and daily benchmarking to balance model performance, workflow quality, and AI costs.

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