Cost Governance

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.

Early Access · 90 Days Free · No credit card requiredBuilt by the Observyze engineering team for production AI systems.
Cascading Spend Dynamics

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.

Synthetic Illustrative Scenario

Simulated runaway agent execution loop without budget cap

Total Spend: $1.14 / single run
TURN 01Initial User Query
1,200 tokens$0.18
TURN 02Tool Retry #1
2,400 tokens$0.21
TURN 03Tool Retry #2
3,800 tokens$0.23
TURN 04Tool Retry #3
5,100 tokens$0.25
TURN 05Fallback Model Call
6,400 tokens$0.27
With Observyze Execution Budgets: You configure a maximum limit (e.g. $0.50 or 3 steps). When Step 3 finishes, the budget trips, stopping subsequent calls and saving over 50% of runaway API spend.
Supported Capabilities

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.

Developer FAQ

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.