Optimize AI costs with Agent Observability

Publisher preview

AI agent costs accumulate along more dimensions than a model-call count. Spend is a product of both which models you call and how the agent is architected: how many turns the loop runs, how many tools it invokes, how much retrieved context it carries, and how much of that context is re-sent on every turn. Provider bills tell you how much you spent, but they’re keyed to API keys and models instead of the applications and workflow steps that generated the calls. That makes a cost increase hard to diagnose, as a higher bill might reflect more traffic, a model change, poor prompt-cache utilization, an agent loop, or a change anywhere in a multistep workflow. Datadog Agent Observability connects estimated AI cost to the applications and traces that produced it, so spend is tied to your agent’s structure instead of an API key. The Cost page lets you attribute spend across applications and compare their efficiency. From there, you can drill into a single application to investigate its most expensive calls. Cost Savings Opportunities go further by identifying sources of avoidable spend (such as an uncached prompt prefix or a conversation history re-sent in full on every turn), estimating the potential financial impact, and providing remediation guidance that engineers can review and validate. In this post, we’ll show how Agent Observability helps you:

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