AI Agent Cost Monitoring & Spend Attribution
Track exact per-step token consumption, attribute costs across tools and models, detect spend anomalies, and enforce execution budgets to limit runaway spend.
Why Agent Costs Compound Non-Linearly
When an autonomous agent fails a tool validation or enters a retry loop, every subsequent turn re-sends the entire preceding message history, causing context windows and token spend to compound rapidly.
Simulated runaway agent execution loop without budget cap
Full-Stack Cost Attribution & Control
Observyze tracks every token category and applies policy controls to keep your AI infrastructure predictable:
Per-Trace Cost Attribution
Exact dollar calculations mapped to specific session IDs, user identifiers, and customer workspaces.
Model-Level Breakdown
Compare token efficiency across OpenAI GPT-4o, Claude 3.5 Sonnet, Gemini 2.5, and local Ollama models.
Runtime Execution Budgets
Enforce step count and dollar limits that halt active agent executions before runaway spend accumulates.
Cost Anomaly Alerts
Receive immediate notifications via webhook when single-trace spend or project volume spikes abnormally.
Cache Token Tracking
Distinguish cached prompt tokens from uncached input tokens to measure prompt caching savings.
Distributed Circuit Breakers
Halt downstream provider dispatch automatically when upstream error rates or spend caps are breached.
Frequently Asked Questions
Technical details, integration patterns, and operational controls.
In simple chat requests, token usage is linear and predictable. In autonomous agent loops, a single prompt can trigger multiple tool retries, recursive reflection steps, and expanding conversation history—meaning subsequent turns consume increasingly large prompt token payloads.
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AI Agent Guardrails
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Want to test without installing anything?
Explore 6 pre-configured production scenarios in our interactive Demo Mode.